Management Ownership and Investment in the Business Cycle

Brian S. Chen∗

January 2016 (First version: August 2015)

Working Paper

Abstract

Does risk aversion amplify business cycle downturns? This study considers the risk exposure ofCEOs and its effect on firm investment in times of high macroeconomic uncertainty. Exploitingexogenous variation in CEO equity ownership, I document that firms with larger CEO stakesdecrease investment significantly more in periods of high aggregate uncertainty. I considerdifferent explanations and find evidence that risk aversion explains these results. Firms with highCEO stakes decrease risk-taking in times of high uncertainty and experience lower stock returnssubsequent to periods of high uncertainty, suggesting that high managerial equity ownershipmay also pose costs to firms.

∗Email: bchen@fas.harvard.edu. I am very grateful to Charles Hadlock and Ted Fee for sharing data. I thankMalcolm Baker, John Campbell, Gabriel Chodorow-Reich, David Choi, Maximilian Eber, Robin Greenwood, SamuelHanson, Matteo Maggiori, Kevin Pan, David Scharfstein, Benjamin Schoefer, Nihar Shah, Andrei Shleifer, JeremyStein, Adi Sunderam, as well as participants in Harvard’s Finance Lunch, for many helpful comments and suggestions.

1 Introduction

What role, if any, does risk aversion play in amplifying business cycle downturns? Recessions tendto be periods of high macroeconomic uncertainty, and increased uncertainty may lead to declinesin investment and output (e.g., Bernanke 1983, Bloom 2009). If key agents in the economy areeffectively risk-averse – perhaps due to limited risk-sharing – this may have the potential to magnifythe effects of heightened macroeconomic uncertainty.

One set of important agents with significant discretionary control over levels of investment arethe senior managers of firms. Many executives hold large, highly concentrated ownership stakesin their firms. Large managerial ownership stakes are a commonly suggested solution to conflictsof interest between owners and managers of the firm, as they incent managers to curtail excessiveconsumption of private benefits and to exert more effort to maximize firm value, leading to greateralignment of interest between managers and owners (e.g., Jensen and Meckling 1976). But theselarge managerial ownership stakes may have pitfalls as well. They lead managers to be highly ex-posed to firm risks, which may become particularly salient in times of high uncertainty. Risk-aversemanagers may then make investment decisions inconsistent with those desired by diversified outsideshareholders. Separately, large stock ownership stakes may lead managers to become excessivelyaligned with equity holders and encourage them to choose risk-shifting investments, at the expenseof firm creditors.

This paper examines the relationship between managerial ownership stake and investmentthroughout the business cycle. Recessions and periods of high macroeconomic uncertainty areparticularly relevant periods to study the impact of managerial ownership on investment. If largemanagerial ownership stakes lead to excessive risk-aversion or risk-shifting, periods of high uncer-tainty may highlight the role of these factors on firm investment. Moreover, aggregate investmentis highly procyclical and its decline contributes significantly to total output declines in downturns,hence playing a key role in business cycle fluctuations. Studying the microeconomic forces affectingthe investment decisions of firms in downturns may thus shed light on the causes of business cycleinvestment volatility.

In this paper, I compare firms with different CEO equity ownership stakes and study their in-vestment behavior in downturns and periods of high macroeconomic uncertainty, where uncertaintyis measured either as in Jurado, Ludvigson, and Ng (2015), aggregating information from hundredsof macroeconomic indicators, or by the implied volatility on S&P 500 index options (VIX). I findthat firms with high CEO stakes decrease investment significantly more during periods of highuncertainty. Figure 1 shows this basic pattern: firms in the highest quintile of CEO ownershipstake cut investment significantly more during recessions than firms in the lowest quintile. Whilefirms with high and low levels of CEO ownership stake differ along other characteristics as well,

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this differential investment behavior in periods of high uncertainty is robust to controlling for amultitude of covariates, various fixed effects (including industry, firm, time, and industry-time fixedeffects), and different definitions of ownership stake. The magnitude of the effect is economicallysignificant: for a one standard deviation increase in uncertainty, firms in the highest quintile ofCEO ownership stake decrease investment by 0.08 percentage points more than those in the lowestquintile, in the baseline ordinary least squares (OLS) estimates. For comparison, in the same re-gression and sample, the marginal effect of a one standard deviation increase in cash flow – one ofthe most studied predictors of investment in corporate finance – increases investment by 0.17-0.19percentage points.1 These results are not driven by differences in the underlying cyclicality of highand low CEO-stake firms, as there is no pattern of differential investment in response to changesin the output gap, a direct proxy for the macroeconomic cycle.

To causally identify the effect of CEO ownership stake on investment during downturns, I use aninstrumental variable estimation strategy and exploit exogenous variation in CEO ownership stakedue to exogenous CEO turnover. Using two-sample instrumental variables (TSIV) estimation, Iobtain estimates of the effect of CEO ownership stake on investment declines that are larger inmagnitude than those in the baseline OLS results.2 The TSIV estimates imply that for a onestandard deviation increase in uncertainty, firms with CEOs in the highest quintile of ownership,relative to firms in the bottom four quintiles, decrease investment by 0.20-0.45 percentage pointsmore. For comparison, the sample mean investment level is 1.62%. In addition, I test for possiblethreats to identification, such as direct tenure effects on investment, and do not find evidencesupporting them. The IV estimates are larger in magnitude than the OLS estimates as theyplausibly correct for biases such as that due to differences in CEO personal risk aversion. Morerisk-tolerant CEOs will ceteris paribus choose higher ownership stakes, biasing the OLS estimatetoward zero.

One potential explanation for the larger decline in investment among high CEO stake firmsrelative to low stake firms during periods of high macroeconomic uncertainty is managerial riskaversion. Managers who have larger equity ownership stakes are more risk-averse over firm outcomesdue to concentrated, undiversified exposure to firm-specific risk. In a basic, stylized “real options”model with a risk-averse manager, more risk-averse managers cut investment more in response to

1See Fazzari, Hubbard, and Petersen 1988, Hoshi, Kashyap, and Scharfstein 1991, Kaplan and Zingales 1997,Lamont 1997, Rauh 2006, Cummins, Hassett, and Oliner 2006 for a few prominent examples of the large literatureon firm investment sensitivity to cash flow.

2Two-sample instrumental variables (TSIV) is used to maximize statistical power from the limited set of exogenousCEO turnover events. For a detailed description of the TSIV estimation method, see Angrist and Krueger 1992 aswell as Inoue and Solon 2010. Examples of papers that use TSIV estimation include Angrist 1990 and Dee and Evans2003. I also present reduced-form estimates that do not use TSIV estimation but exploit the same underlying sourceof exogenous variation in CEO turnover events.

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increases in uncertainty. This is driven by the “bad news principle” articulated by Bernanke (1983):increased uncertainty leads to less investment because states of the world with poor investmentpayoffs now occur with increased probability. More risk-averse managers are more sensitive topoor outcomes in “bad” states of the world, and thus decrease investment more.3 More generally,managers may attempt to decrease firm riskiness via other channels as well, such as re-allocatingfunds to less risky investments, decreasing operating leverage, or reducing the transformation of safeinternal funds (such as cash) to risky operating assets. Empirically, I find that firms with higherCEO ownership stakes decrease selling, general, and administrative (SG&A) expenses, which are aninvestment-like expense and contribute to higher operating leverage. Moreover, these firms increaseequity payouts and asset sales relatively more.

One important alternative explanation for these results is that managers with higher owner-ship stakes are better-aligned with shareholders, and that the larger investment declines duringperiods of high macroeconomic uncertainty are actually optimal – or, at minimum, better – pol-icy for shareholders. I conduct a number of additional empirical tests to distinguish between thealignment and risk aversion explanations. First, I compare firms with high and low institutionalownership, as firms with high institutional ownership are likely to have better shareholder moni-toring of management and hence a higher degree of manager-shareholder alignment. There is zero(or weakly positive) impact of institutional ownership on investment during downturns, suggestingthat relative declines in investment in times of high uncertainty are not preferred by shareholders.Second, I find that excess stock returns in the year subsequent to a period of high macroeconomicuncertainty are lower for firms with larger CEO stakes, which is inconsistent with the explanationthat it is optimal to decrease investment more in times of high uncertainty. Third, firms with highCEO stakes cut idiosyncratic firm risk-taking relatively more during periods of high uncertainty.Finally, high stake CEOs who possess options whose values are highly sensitive to volatility (i.e.,high “vega” options) cut investment less in periods of high uncertainty. Results of these additionalempirical tests are consistent with the managerial risk aversion explanation.

This paper makes several contributions. The first is to show that CEO ownership is an empiri-cally important factor affecting firm investment policy. Despite an extensive literature on manage-ment ownership, there remains limited evidence of the causal impact of management ownership onfirms.4 This study presents causal evidence that large managerial ownership stakes lead to largerdeclines in investment in times of high macroeconomic uncertainty, adding to the limited literatureon the causal impacts of managerial ownership.

3Greater exposure to firm risk via a larger ownership stake is analogous to being more risk-averse over firminvestment outcomes.

4Li and Sun (2015) use the 2003 dividend tax decrease as an exogenous shock to effective managerial ownershipto study the impact of managerial ownership on firm value.

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The second contribution of the paper is to show that these investment declines are due tomanagerial risk aversion and concentrated managerial exposure to firm risk. While there is anextensive literature on the positive effects of large managerial ownership stakes to align managerand shareholder interests, less evidence exists on the potential costs of large ownership stakes andthe consequences of exacerbating managerial risk aversion.5 High-powered incentives for CEOscome at the expense of risk-sharing, which is consistent with the canonical contract theory trade-off between incentive and insurance provision. Contract theory commonly links strong incentivesto higher average compensation for the risk-averse manager, but this study shows that risk-sharingcosts also manifest themselves as distortions to firm policy.6 Regardless of whether the observedCEO stakes are the outcome of optimal or sub-optimal contracting between managers and owners,the results illustrate a real cost of large management ownership beyond the need to pay managersmore.

Third, the paper describes a novel channel through which managerial moral hazard affectsmacroeconomic outcomes and cyclicality.7 Large CEO ownership stakes mitigate moral hazardconcerns but force risk-averse managers to be exposed to large amounts of firm-specific risk. Intimes of high uncertainty, managers with large equity stakes cut firm investment to reduce theirpersonal risk exposure.8 This exacerbates the high volatility of investment in the business cycle. Thestudy provides evidence that “animal spirits” – in the form of risk aversion – play a significant rolein investment behavior and business cycle amplification (Keynes 1936). While U.S. public equitymarkets are well-developed and a large fraction of U.S. households own equity, concentrated equityownership remains common among the managers of many publicly-traded firms, and likely amongmany non-public firms as well. Due to imperfect risk-sharing and concentrated ownership stakes,CEOs – a small number of agents in the aggregate economy, yet possessing significant discretion

5Faccio, Marchica, and Mura (2011) and Lyandres, Marchica, Michaely, and Mura (2015) study the impact ofowner portfolio diversification on firm risk-taking and investment.

6Becker (2006) finds that Swedish CEOs with higher wealth levels (hence lower risk aversion) hold larger equityownership stakes in their firm, which is evidence on the trade-off between large ownership stakes and risk-sharing.Other studies of the trade-off between incentives and risk-sharing in executive compensation include Aggarwal andSamwick 1999, Prendergast 1999, and Prendergast 2002.

7Rampini (2004) presents a theory in which entrepreneurial activity varies throughout the business cycle dueto fluctuations in entrepreneurial net worth. Entrepreneurs are risk-averse but must bear non-diversifiable riskto prevent moral hazard. Productivity shocks affect entrepreneurial net worth, amplifying the initial shock bydecreasing entrepreneurship. Other work on managerial moral hazard and macroeconomic outcomes includes therole of entrepreneurial moral hazard in external financing frictions and financial intermediation amplifying net worthor credit supply shocks (e.g., Holmstrom and Tirole 1997), and the impact of managerial moral hazard on bank orfinancial sector risk-taking, due to deposit insurance or bailout policy (e.g., Diamond and Dybvig 1983, Grossman1992, Demirgüç-Kunt and Detragiache 2002, Dam and Koetter 2012).

8Although managerial hedging can offset this risk exposure, in practice managers are unlikely to engage in muchhedging of their large aggregate risk exposure, as it is either explicitly prohibited (e.g., shorting own-firm stock) orvery costly (e.g., purchasing many options to offset exposure to aggregate uncertainty).

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over large firm investment choices – may end up amplifying business cycles due to personal riskaversion.

This paper is related to a few prior strands of literature. First, there is an extensive literatureon the misalignment of managers with owners of the firm, and the curative effect of managerialownership (e.g., Jensen and Meckling 1976, Leland and Pyle 1977, Jensen 1986, McConnell andServaes 1990, Himmelberg, Hubbard, and Palia 1999, Bertrand and Mullainathan 2003, Shue andTownsend 2014). While the benefits of managerial ownership are well understood, there are fewerstudies on the costs of high managerial ownership.9 Panousi and Papanikolaou (2012) argue thatmanagerial risk aversion explains their finding that firms with high managerial ownership decreaseinvestment more in face of higher idiosyncratic firm volatility. While their results are consistentwith this study, there are important differences: i) this study focuses on macroeconomic uncertaintyand investment in the business cycle;10 ii) this study exploits exogenous variation in managerialownership; and iii) I use a different measure of ownership stake that is more directly linked to themanager’s risk exposure and risk aversion.

Second, this paper relates to studies on the relationship between managerial characteristicsand firm investment. Bertrand and Schoar (2003) argue that managerial fixed effects – or “styles”– have explanatory power for firm investment, but Fee, Hadlock, and Pierce (2013) show thatthe empirical estimation of managerial style effects must differentiate between endogenous andexogenous managerial turnover events. In this paper, I exploit the exogenous CEO turnover datafrom Fee, Hadlock, and Pierce (2013).11 Other studies show that specific CEO characteristics,ranging from overconfidence to prior work or life experience, affect firm investment policy (e.g.,Malmendier and Tate 2005, Malmendier, Tate, and Yan 2011, Schoar and Zuo 2011, Pan, Wang,and Weisbach 2013, Benmelech and Frydman 2014). Third, previous studies on real options modelsof investment and the importance of uncertainty shocks in investment are related (e.g., Bernanke1983, Dixit and Pindyck 1994, Bloom, Bond, and Van Reenen 2007, Bloom 2009). Finally, there arerelated studies that examine other factors that lead to differential behavior across firms in responseto the business cycle (e.g., Philippon 2006, Eisfeldt and Rampini 2008).

The remainder of the paper proceeds as follows. Section 2 describes the data, the baselineempirical specification, and the TSIV identification strategy. Section 3 presents the basic empiricalresults. Section 4 discusses the interpretation and potential explanations for the results, presents

9Examples of papers on the costs of high management ownership include Morck, Shleifer, and Vishny (1988) onmanagerial entrenchment at high levels of ownership, and Friend and Lang (1988) on high management-ownershipfirms choosing sub-optimally low levels of firm debt.

10In a part of the analysis, this study uses idiosyncratic firm volatility as an outcome, because firm volatility canbe endogenously affected by managers’ decisions. In Panousi and Papanikolaou (2012), idiosyncratic firm volatilityis an explanatory variable.

11I am grateful to Charles Hadlock and Ted Fee for generously providing this data.

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evidence from further tests to distinguish between these explanations, and contains robustnesschecks. Section 5 discusses the macroeconomic implications of the results. Section 6 concludes.

2 Data and empirical strategy

2.1 Data

Quarterly firm data from Compustat is matched to annual firm data on executive compensationand shareholdings from Execucomp, covering the years 1992-2013. Data from Execucomp does notcover all firms in the Compustat database, but only a subset that is approximately the universe offirms in – or formerly in – the S&P 1500.12 I exclude financial firms (SIC codes 6000-6799) andregulated utilities (SIC codes 4900-4949), as is customary in studies of firm investment.

2.1.1 Measuring CEO stake

A commonly used measure of managerial ownership is the fraction of the firm’s equity owned bythe CEO or by top executives of the firm (e.g., Panousi and Papanikolaou 2012). However, thismeasure is not ideal to test the role of CEO risk aversion, because it does not directly measure theCEO’s exposure to firm or aggregate risk relative to her entire wealth portfolio. A CEO can own alarge fraction of the firm’s equity but have this stake represent a small fraction of their total wealth.A better measure is the fraction of the CEO’s total wealth in firm equity, i.e. CEO firm equity wealth

CEO total wealth ,where CEO total wealth includes non-firm equity wealth along with the present value of humancapital.13

In this study, I use as a proxy for CEO stake:

CEOstakeit = CEO firm equity wealthitCEO cash compensationit

.

The numerator, CEO firm equity wealth, is calculated as the product of CEO shares owned, in-cluding restricted stock, and the share price. The denominator is calculated as the annual salaryand bonus of the CEO. CEOstakeit serves as a proxy for fraction of CEO total wealth in the firm,where the denominator is a flow value (rather than present value) approximation for the value of

12According to S&P, firms in the S&P 1500 collectively make up around 90% of US equity market capitalization.The S&P 1500 includes the S&P 500 which covers large-cap firms, but also includes the S&P MidCap 400 and S&PSmallCap 600.

13CEOs are typically prohibited from taking measures to hedge against own-firm risk in their personal portfolio(e.g., such as shorting or buying put options on firm stock), but could hedge their exposure to aggregate risks (e.g.,their market beta exposure). Any unobserved CEO hedging of aggregate risks in their personal portfolio will biasagainst finding any impacts of CEO risk exposure in the empirical tests.

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CEO human capital, as data on CEO financial wealth is generally unavailable in the U.S. Alter-natively, one can interpret CEOstakeit as a normalized measure of the CEO’s firm equity stake.If CEOs of larger firms have larger dollar stakes and higher human capital as well as non-firmequity wealth, this normalization makes ownership stakes more comparable across firm sizes, types,and time periods (as it is inflation-invariant). I divide CEOstakeit into quintiles in my empiricalanalysis to prevent outliers from driving the results.14 In robustness checks, I adjust the numeratorof CEOstakeit, CEO firm equity wealth, for CEO option ownership by adding the estimated dollar“delta” exposure that CEOs face via their options. Delta is a standard measure of the change invalue of the option with respect to a dollar change in the underlying stock price.

2.1.2 Summary statistics

Table 1 displays firm characteristics by quintile of CEO ownership stake. Firms with high CEOstake tend to be more profitable. They also have higher average Tobin’s Q, lower levels of leverage,and faster growth. Firms with high CEO stake do not appear to differ in terms of stock returnvolatility. These statistics suggest that investment opportunities and financial constraints maydiffer across firms with differing CEO stakes. As many of these factors affect investment directly,I control for them in the empirical analysis.15

2.2 Baseline empirical specification

While Figure 1 shows larger investment declines in recessions for high CEO stake firms, the dataare not adjusted for any potential confounds. In an ideal experiment, one would assign otherwiseidentical firms different CEO ownership stakes and observe their investment patterns across thebusiness cycle and in times of differing macroeconomic volatility. Without an experiment, I estimatethe baseline regression

InvitAit−1

= β0+β1CEOstakeit−4+β2CEOstakeit−4 ×Mt︸ ︷︷ ︸Effect of interest

+β3Mt+X′itΓ+J∑j=1

δj1 {Industryi = j}+εit,

(1)for firm i in quarter t. Here, Invit

Ait−1is capital expenditures scaled by lagged assets, CEOstakeit−4

is the lagged CEO stake, Mt is the time-series measure of macroeconomic uncertainty or business14Firms whose CEOs have very low cash compensation are thus included in the top quintile of the CEOstakeit

variable.15All variables are winsorized at the 1% level, except total assets.

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cycle, Xit is a matrix of controls, and δj are industry fixed effects.16 Controls include potentialconfounds and other determinants of investment: Tobin’s Q, cash flow, size, leverage, sales growth,cash-on-hand, stock return volatility, and CEO age. I exclude firm-years in which there is CEOturnover, to avoid matching the previous CEO’s ownership stake with firm policies implemented bythe new CEO. In other specifications, firm fixed effects are used, exploiting within-firm variationin CEO stake, or the macroeconomic indicator Mt main effect is replaced with time fixed effects,leaving the effect of interest, CEOstakeit−4 ×Mt, identified by differential cross-sectional impactsof CEO stake in periods of high versus low Mt.

I use a few different proxies for the macroeconomic state Mt. To measure macroeconomicuncertainty, I use a measure of macroeconomic uncertainty calculated by Jurado, Ludvigson, andNg (2015) (henceforth “JLN uncertainty”), which aggregates information from the time-series ofhundreds of economic variables and removes the predictable or “expected” component of thesetime-series.17 Second, the implied volatility on S&P 500 index options (VIX) is used as a measureof macroeconomic uncertainty. Third, output gap is used to proxy for business cycle. The time-series of these three macroeconomic indicators is shown in Figure 2. While the three variables arecorrelated, the correlation between the uncertainty measures and output gap is relatively low (theR2 from a quarterly regression of JLN uncertainty on the output gap is 0.1; that of a regression ofVIX on output gap is 0.03). I also use an indicator variable for the acute period of financial crisisfrom 2008Q3 through to 2009Q2 along with time fixed effects to document the effect of CEO stakeduring the recent financial crisis.

2.3 Instrumental variable strategy

Even with fixed effects and extensive controls for confounds, firm-time varying unobserved omittedvariables may bias the empirical results. Firm fixed effects will address unobserved firm omittedvariables by exploiting only within-firm variation in CEO stake and macroeconomic uncertainty,but cannot rule out the possibility that unobserved correlates of CEO stake that vary within firmand over time could drive the results.

To address this, I exploit plausibly exogenous variation in CEO stake due to exogenous turnoverin CEOs in an instrumental variable (IV) strategy. CEO turnover leads to changes in CEO stakebecause executives accumulate ownership stakes over time from stock grants, option grants, andequity payouts associated with long-term incentive plans. New CEOs will thus tend to have lowerownership stakes than pre-existing incumbent CEOs. However, CEO turnover in and of itself isproblematic as an instrument as it can coincide with the desire for a firm to change their business

16Execucomp contains annual data on CEO share ownership, which is merged to Compustat based on fiscal year.Hence CEOstakeit−4 uses data on CEO share ownership from the previous fiscal year.

17These data are obtained from Sydney Ludvigson’s website.

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strategy and their investment policy. Therefore I exploit only exogenous CEO turnover events,as documented by Fee, Hadlock, and Pierce (2013). Using news search, Fee et al. (2013) classifyexogenous CEO turnover events as those caused by death, health, or “natural” retirement. Natu-rally retiring CEOs are restricted to those whose age falls in the retirement time window, whoseresignation is not otherwise found to be forced in the news search, and whose firms are not under-performing based on observables.18 The data on exogenous CEO turnover events runs from 1990to 2007 and covers all firms in Compustat with book assets exceeding $10 million (1990 dollars).

In IV regressions, I define the instrument, recentCEOit, as the indicator for recent new CEOsubsequent to exogenous CEO departure, for the five years subsequent to the year of exogenousturnover.19 As there are two endogenous variables – CEOstakeit and CEOstakeit ×Mt – twoinstruments are needed: recentCEOit and recentCEOit ×Mt. The first-stage equations are

CEOstakeit−4 = α10 + α11recentCEOit−4 + α12recentCEOit−4 ×Mt +

α13Mt + X′itΛ1 + FEi + η1it,

CEOstakeit−4 ×Mt = α20 + α21recentCEOit−4 + α22recentCEOit−4 ×Mt + (2)

α23Mt + X′itΛ2 + FEi + η2it,

and the second stage regression is

InvitAit−1

= δ0 + δ1 ̂CEOstakeit−4 + δ2 ̂CEOstakeit−4 ×Mt + δ3Mt + X′itΓ + FEi + νit, (3)

where ̂CEOstakeit−4 and ̂CEOstakeit−4 ×Mt denote the two fitted values estimated in the firststage, and FEi denotes either the industry or firm-level fixed effect used in the regression.

One issue with IV estimation in the sample is that statistical power is limited due to a limitednumber of exogenous CEO turnover events.20 While the Execucomp data on the endogenousvariable CEOstakeit is only available for a subset of the Compustat database, the CEO turnoverdata is matched to a larger sample of Compustat firms. To fully exploit the variation in theinstrument, I use a two-sample instrumental variable (TSIV) estimator (e.g., Angrist 1990, Angristand Krueger 1992, Dee and Evans 2003). The TSIV method combines moments from two datasets

18See Fee et al. (2013) for additional details. Most CEO turnover events in their sample are not exogenous, as only824 of a total of 7,179 CEO turnover events are classified as exogenous. Moreover, Fee et al. (2013) show that firmpolicies do not appear to change significantly around exogenous CEO turnover events, which supports the claim thatthese events are quasi-random and are not otherwise associated with desired underlying changes in firm policy.

19To be consistent with the OLS specifications, the year of CEO turnover is excluded. If firm investment isparticularly anomalous in the first year of a CEO’s tenure, excluding the year of CEO turnover also avoids thispotential concern.

20Variation from the instruments is needed to identify not only the effect of a higher CEO stake, but also theinteraction effect of CEO stake in times of high macroeconomic uncertainty.

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– where the first contains data on the outcome variable and the instrumental variable, and thesecond data for the endogenous variable and the instrumental variable – to derive a consistentestimate for the parameter of interest. Intuitively, the TSIV method estimates a predicted value ofthe unobserved endogenous variable in the first dataset by fitting the estimate of the “first stage”from the second dataset, containing data on both the endogenous and instrumental variables, ontothe observations from the first dataset, which contains data only on the instrumental variable.21

I also report reduced form estimates from the larger sample of Compustat firms matched to CEOturnover data.

3 Empirical results

3.1 Investment during the 2008-2009 Financial Crisis

Before turning to results from baseline specification (1), Table 2 compares the investment policy ofhigh and low CEO stake firms in the four quarters of the financial crisis, beginning from the thirdquarter of 2008 and ending in the second quarter of 2009 (from Lehman Brothers’ bankruptcy to theend of the NBER recession). Column 1 shows results from a regression where the key explanatoryvariables are CEOstakeit, and the interaction of CEOstakeit with an indicator for financial crisis.The interaction term is statistically significant and negative, indicating that firms with higher CEOownership stake cut their investment more during the financial crisis. The coefficient estimateimplies that the investment level of a firm in the highest quintile of CEO ownership stake relativeto a firm in the lowest quintile of CEO stake decreased investment 0.21 percentage points moreper quarter during the crisis. The magnitude is also economically significant, as the average firmquarterly investment level in the sample was 1.49% in 2007 and 1.00% in 2009.

Column 2 of Table 2 shows results where the explanatory variable is a dummy variable equal toone if CEO ownership stake is in the highest quintile. Ownership stakes are the most concentratedin the top quintile, where the median ownership stake is over 10 times the magnitude of the medianin the bottom four quintiles. It is not surprising that the majority of the estimated effect is found tobe concentrated in the highest quintile. Firms in the highest quintile of CEO stake cut investmentby 0.189 percentage points more than those in the bottom four quintiles during the crisis.

21A key additional assumption of the method (relative to those necessary for IV identification) is that the first stageestimates from the second dataset are consistent for the (unobserved) first-stage relationship in the first dataset. SeeInoue and Solon (2010) for a detailed exposition.

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3.2 Baseline results

Table 3 shows results from estimating specification (1). These regressions use three normalizedmeasures for macroeconomic conditions and uncertainty: uncertainty as calculated by Jurado,Ludvigson, and Ng (2015) (JLN uncertainty), the implied volatility (VIX) index, and the outputgap. For ease of interpretation, the CEO stake quintile variable is coded from 0 (lowest) to 4(highest) in all regressions, so the coefficient on the macroeconomic condition variable can be readas the impact of the macroeconomic condition on firms in the lowest CEO stake quintile. Columns 1to 3 of Table 3 test the effect of CEO ownership stake during periods of high JLN uncertainty. Theestimates show that the CEO stake×JLN uncertainty interaction term has a statistically significantnegative effect. The specification in the second column adds a linear time trend to the baselinespecification to account for the decline in investment levels during the sample, and the third columnuses time fixed effects to absorb aggregate time-series variation while still allowing the interactionterm to be identified from differences in the impact of CEO stake during times where uncertainty iseither high or low. The magnitudes imply that a firm in the highest quintile of CEO stake, relativeto one in the lowest quintile, cuts investment by 0.137 to 0.182 percentage points more (dependingon the specification) in response to a two standard deviation increase in JLN uncertainty.22 Forcomparison, the sample mean investment level is 1.62%. Columns 4 through 6 show results fromthe same regression specifications but using VIX as the measure of macroeconomic uncertainty.The magnitude of the effect of CEO stake on investment during periods of high uncertainty isremarkably similar to that estimated using the JLN uncertainty measure.

Columns 7 to 9 of Table 3 use the output gap to proxy for macroeconomic conditions. In allthree specifications, the coefficient on the interaction of CEO ownership stake with output gap isstatistically insignificant, with a precisely estimated zero magnitude. Firms with differential CEOownership stakes do not appear to have differential investment sensitivity to the business cycle.

These results suggest that firms with higher CEO stakes cut investment significantly more in theface of increases in macroeconomic uncertainty. But firms with higher CEO stakes do not changeinvestment differentially throughout the business cycle. This suggests that unobserved differencesin cyclicality between high and low CEO stake firms are unlikely to explain the results. One possibleexplanation for this pattern is managerial risk aversion, which I explore in detail later.

To address the possibility that unobserved, systematic differences in high and low CEO stakefirms are driving these patterns, I estimate specification (1) with firm fixed effects instead of industryfixed effects. Results from these regressions are shown in Table 4. Column 1 shows estimates of theeffect of CEO stake during times of high JLN uncertainty that are similar in magnitude to those

22Figure 2 shows that at the peak of financial crisis, JLN uncertainty was more than 4 standard deviations abovethe sample mean.

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obtained in Column 5 of Table 3, an otherwise identical specification using industry fixed effectsinstead. Columns 2 and 3 add even more control variables: column 2 adds the full set of controlsinteracted with the measure of JLN uncertainty; column 3 uses time fixed effects. The estimates ofthe interaction effect remain statistically and economically significant. Columns 4-6 show similarresults using VIX. Again, the estimate in Column 4 of the effect of CEO stake in times of highVIX is close in magnitude to that in Column 8 of Table 3, suggesting that results from the baselineindustry fixed effects estimation are not driven by systematic firm differences across high and lowCEO stake firms.

3.3 TSIV results

3.3.1 Bias of OLS estimates

Even with fixed effects and extensive controls for confounds, firm-time-varying unobserved omittedvariables could still bias the OLS results. Ex ante, the direction of this bias is unclear. While someomitted variables could bias the OLS estimate away from zero and towards finding an effect, basictheory suggests a few reasons that the bias on the OLS estimates might be in the other direction– that is, the estimates are biased to zero and underestimate the magnitude of the effect of CEOstake in times of high uncertainty. First, CEOs may have some discretion in choosing the sizeof their equity ownership stake in practice. CEOs with lower personal risk aversion will be morelikely to select larger ownership stakes, which would bias estimates of impact of CEO stake intimes of high uncertainty to zero. Second, CEOs with high firm ownership stakes are more likelyto be unobservably hedged in their personal portfolio against aggregate risks, such as shocks tomacroeconomic uncertainty, as they experience larger utility gains from hedging. Third, firms withhigher stake CEOs may employ other unobserved (or unmeasured) governance or compensationmechanisms to offset the impact of excessive CEO risk aversion from large ownership stakes.23

These factors will all tend to bias the OLS estimate toward zero. Finally, measurement error inthe explanatory variable, CEOstakeit, which is an imperfect proxy for the fraction of total CEOwealth in firm equity, will also bias the OLS estimates towards zero.

To address these potential biases, I use an instrumental variable estimation strategy exploitingexogenous CEO turnover events described in Section 2. I first estimate the reduced form relationshipby regressing investment on an indicator for recent new CEO, who succeeds another CEO departingfor exogenous reasons. As long as the instruments have sufficient first-stage explanatory power,identification of the causal effect of interest in an IV specification is driven by the reduced formrelationship.

23In a subsequent section, I show the impact of one such mechanism – options compensation whose value is highlysensitive to increases in underlying firm equity volatility.

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3.3.2 Results

Table 5 presents the IV reduced form estimation results. Columns 1 through 4 show results withJLN uncertainty as the measure of macroeconomic uncertainty. Recent CEOs invest relatively moreduring times of high macroeconomic uncertainty. This is consistent with the sign of OLS results.As recent CEOs have lower ownership stakes, these results imply that CEOs with smaller stakescut investment less during times of high uncertainty. Columns 1 and 2 use industry fixed effects andcolumn 2 includes a linear time trend. Columns 3 and 4 use firm fixed effects. Columns 5 through8 show results with VIX as the measure of uncertainty. While these effects are less statisticallysignificant than those estimated in the specifications with JLN uncertainty, the magnitudes aresimilar and consistent with those in the JLN uncertainty specifications.

In order to derive IV estimates, I use two sample IV (TSIV) methods. Here, the first stageis estimated on the subsample of Execucomp firms while the second stage is estimated on thefull sample.24 First stage estimates are thus imputed for the fraction of the second-stage samplewhere CEO stake data is unavailable. There exists a very strong first-stage relationship betweenthe two endogenous regressors (CEO stake, and CEO stake interacted with macroeconomic uncer-tainty) and the two instruments (recent CEO, and recent CEO interacted with uncertainty) in theExecucomp subsample, where CEO stake is observed. Results are shown in Appendix Table B1.For all specifications, the Kleibergen-Paap F-statistic has values significantly above the Stock andYogo (2005) proposed critical value of 7.03, for just-identified IV with two endogenous regressors.Therefore, weak instrument bias is not a concern.25

Instrumental variable estimates are shown in Table 6. The endogenous regressors are an in-dicator variable equal to one if CEO stake is in the highest quintile and zero otherwise, and theinteraction of this CEO stake dummy variable with macroeconomic uncertainty. IV estimates ofthe coefficient of interest are larger than those from the OLS estimates. Columns 1 through 4 showresults using JLN uncertainty as the measure of macroeconomic uncertainty; columns 5 to 8 use

24For inference, second stage standard errors are computed using the asymptotic variance-covariance matrix, derivedin Inoue and Solon (2010). Intuitively, adjustments must be made to account for the estimated nature of the secondstage regressors (as in two-stage least-squares estimation of instrumental variable regressions), assuming that firststage estimates on the subsample can be consistently applied to the full sample to generate inferred values of theendogenous regressor in the second stage. Inoue and Solon (2010) show that the second-stage estimated variance-covariance matrix generated by OLS estimation of the outcome variable on the generated regressors can then beadjusted by a factor of 1 + (NSS/NFS) β̂

′Σ̂F S β̂σ̂SS

to obtain the correct variance-covariance matrix, where NSS and NFSare the number of observations for the second stage and first stage regressions, respectively; Σ̂FS is the estimatedcovariance of errors from the first stage regressions; and σ̂SS is the sample mean squared residual from the secondstage.

25The Kleibergen-Paap F-statistic takes into account non-i.i.d. residuals and is analogous to the Cragg-DonaldF-statistic in the case of i.i.d. residuals (Cragg and Donald 1993, Kleibergen and Paap 2006). Exceeding the criticalvalue of 7.03 correponds to a 5% rejection that the size of the weak instrument bias is larger than 10 percent of theOLS bias (Stock and Yogo 2005).

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VIX. These estimates imply that for a 2 SD increase in the JLN uncertainty measure, firms withCEOs in the highest quintile of ownership stake, relative to those in the bottom four quintiles,cut investment 0.70-0.90 percentage points more. Estimates using the VIX uncertainty measuresuggest a similar magnitude (0.40-0.58 percentage point larger decrease in investment for a 2 SDincrease in uncertainty). These effects are quantitatively large, as the sample mean of quarterlyinvestment is 1.62%.

3.3.3 Threats to identification

One potential challenge to the exclusion restriction assumption required for identification is thatCEO tenure directly affects investment via other channels, such as in Pan, Wang, and Weisbach(2013).26 Their paper argues that CEOs tend to overinvest as they accumulate more years of tenuredue to agency conflicts such as CEOs gaining more control over the board. This violates the IVexclusion restriction because tenure then directly affects investment. The CEO tenure investmentcycle of Pan et al. (2013) is much stronger in acquisitions than in capital expenditures, and Iconsider only capital expenditures here. Even so, their explanation would only bias the estimationof the direct impact of CEO stake on investment. It does not explain the effect of CEO stake oninvestment during times of high macroeconomic uncertainty, and would predict that CEOs whohave less tenure (and thus less ownership) invest relatively less during downturns, as they overinvestless in general. Thus, this explanation biases against finding a significant negative IV estimate ofthe effect of interest. Robustness checks in section 4 show that using both CEO stake and CEOtenure as explanatory variables in OLS regressions does not change the magnitude of the CEO stakecoefficients relative to baseline OLS results and does not yield statistically significant estimates ofthe direct impact of tenure on capital expenditure investment.

A separate concern is that CEOs who naturally retire with large stakes may still have significantsay in the firms’ decisions, especially during the years of the subsequent new CEO’s tenure. Yetany such effect would bias against finding a positive effect of recent new CEO on investment, as theformer, retired CEOs will have large ownership stakes and tend to be more risk-averse over firmoutcomes.

Another explanation of the IV results that violates the exclusion restriction is that more re-cent CEOs find it harder to cut investment during downturns due to their lack of experience orinformation about the quality of investments. Rather than cut investment sharply, they tend to“smooth” out investment expenditures more because they do not wish to unknowingly cut produc-tive investment projects. Put another way, new CEOs may prefer to “go with the flow.” We can

26The empirical strategy in Pan et al. (2013) does not distinguish between the direct effect of CEO tenure and thatof CEO ownership stake.

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directly test this by comparing new CEOs who are insiders (i.e. previous executives at the firm) tothose who are not. If recent CEOs cut investment less during downturns due to lack of experienceor information on previous firm investments, this effect will be concentrated among outsider re-cent CEOs because insider executives promoted to CEO likely possess extensive knowledge aboutprevious firm investments.

To test this, I define a first indicator variable equal to one when the new CEO is an outside hire,and a second indicator variable equal to one when the new CEO is an inside hire. I then re-runthe reduced form specifications using either the outsider or the insider recent CEO indicator, alongwith the respective macroeconomic uncertainty interaction terms, as the exogenous explanatoryvariables. Results are in Table 7. Comparing the coefficients on Recent Outsider CEO×JLNuncertainty in columns 1 and 2 to those on Recent Insider CEO×JLN uncertainty in columns 5and 6 does not show that only outsider recent CEOs increase investment more during downturns.The results are the same in the specifications using VIX as the measure of uncertainty (columns 3and 4 for outsider recent CEOs, columns 7 and 8 for insiders). These results are inconsistent withthe explanation that recent CEOs decrease investment less due to inexperience, lack of information,or a tendency to “go with the flow.”27

4 Interpretation and additional results

4.1 Potential explanations for results

There are a few plausible explanations for the finding that firms with higher CEO ownership stakesdecrease investment more during times of high macroeconomic uncertainty.28 One explanation ismanagerial risk aversion. Managers with larger stakes in their firms are more exposed to firm risk,and hence decrease risky firm investment more in response to aggregate uncertainty shocks. InAppendix A, I show this behavior is consistent with a simple, stylized real options model in whichthe manager is risk-averse. More generally, managers can decrease their personal firm equity riskexposure by decreasing firm investment, as long as investment at the margin is at least partially

27In untabulated results, I check that the first stage relationships between recent CEOs, and the interaction of recentCEO×uncertainty to CEO stake quintile and the interaction of CEO stake×uncertainty are similar in magnitude usingeither the indicator for outsider or that for insider recent CEOs. As these first stage relationships are comparable,the IV estimates of the impact of CEO ownership stake×uncertainty will yield similar estimates both with the recentoutsider CEO and the recent insider CEO instruments.

28The IV results section discusses a few possible explanations, such as direct CEO tenure effects, or a “go-with-the-flow” effect, but find that these explanations are unlikely to be driving the results. Further evidence on CEO tenureeffects are in the robustness section.

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funded by internal funds or debt issuance.29 Put another way, managers who are more risk-aversewill reduce their conversion of riskless cash into risky operating assets, by decreasing investment(or alternatively by selling off risky assets). They will also reduce the riskiness of the operatingassets to the extent this is possible – perhaps by choosing less risky investments, or by decreasingthe firm’s operating leverage.

Other potential explanations also exist. One possibility is that managers are better aligned withshareholders in high CEO stake firms, and optimal policy is in fact to cut investment more duringtimes of high macroeconomic uncertainty (henceforth denoted as the “alignment explanation”).The results can thus be explained by high stake CEOs adopting more shareholder-optimal policyin downturns, during which low stake CEOs overinvest.

One variant of the alignment explanation is that high stake CEOs exert more effort due tobetter alignment. In periods of low uncertainty their firms invest more. By diminishing marginalreturns, these incremental investments have lower returns, but are still NPV positive. However, indownturns, these incremental investment projects are the first to turn NPV-negative or fall belowa profitability threshold. High stake CEOs cut these projects and hence reduce investment moreaggressively. Another variant of the alignment explanation is that there are high private costs todownsizing and layoffs, and thus only high stake CEOs are sufficiently incented to incur the highprivate costs of decreasing investment significantly (which may result in layoffs) in downturns.

One issue common to all variants of the alignment explanation is that they do not explain thedifferential investment response to macroeconomic uncertainty rather than to the business cycle.The results show no differential investment patterns between high versus low stake CEOs in responseto business cycle downturns, but show differential investment by high stake CEOs in response tochanges in uncertainty. This lends support to the risk aversion explanation.

Other potential explanations are unrelated to agency considerations, but are inconsistent withthe IV results. If CEOs use their stake as a signaling tool, à la Leland and Pyle (1977), therecould be time-variation in the need for signaling. CEOs may signal during times of low macroe-conomic uncertainty that their company is profitable with high stakes, but during times of highmacroeconomic uncertainty it is no longer necessary to do so. Underlying company profitabilityis driving the higher positive correlation between investment and CEO stakes during times of lowuncertainty than during times of high uncertainty. Yet this explanation is inconsistent with theIV results, which rely on exogenous changes in CEO stake due to CEO turnover events orthogonal

29If the marginal dollar of investment is fully funded by equity issuance and the firm is 100% equity financed, themarginal investment may not increase managerial equity risk exposure, as the per-equity share risk could remainconstant with greater investment.

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to any potential signaling motives.30 A second non-agency-related explanation is that CEOs withhigher stakes are more overconfident (e.g., Malmendier and Tate 2005). If overconfidence variesover time such that it drops during periods of high uncertainty, then this could explain the highercorrelation between CEO stake and investment in periods of low uncertainty. But this is also ruledout by the IV results, where the variation in CEO stakes induced by exogenous CEO turnover isplausibly orthogonal to CEO overconfidence and time-variation in overconfidence.

The positive association of CEO stake with investment in normal macroeconomic conditionsis not the focus of this study, but I briefly discuss whether the explanations considered abovealso rationalize the “main effect” of higher CEO stake in which high CEO stake firms invest morethan comparable firms with low CEO stakes. If the only channel through which high CEO stakesaffect firm policy is to increase managers’ exposure to firm risk and their risk aversion, high CEOstake firms would invest less in all periods. Thus the main effect of higher CEO stakes on firmpolicy during times of normal macroeconomic conditions cannot be explained by risk aversion.Given the evidence in the literature (and widespread belief by investors) that large managementownership stakes induce better alignment between the CEO and shareholders, it is unlikely thatlarge management ownership stakes only lead to greater risk aversion.31 But higher investment byfirms with large CEO stakes in normal times is consistent with the notion that managerial effortand investment are complements, and that higher CEO stakes both induce more CEO effort andlead to higher levels of investment.32 For instance, evidence on excess stock returns presented laterin the paper suggests that firms with higher management stakes have greater excess returns inperiods of average uncertainty and supports the notion that the main effect is a consequence ofthe beneficial impact of management stake on firm value.33 As long as the channel driving themain effect of higher investment levels for higher stake CEOs is relatively constant across periods

30As well, the OLS regressions use lagged CEO stake, which mitigates to some degree CEO stakes respondingendogenously to higher profitability.

31For example, evidence exists for the impact of ownership on alignment even in other contexts such as mutual funds(e.g., Khorana et al. 2007, Cremers et al. 2009). In response to alignment concerns, some companies have mandatoryminimum ownership thresholds, occasionally denoted as multiples of base pay. For example, Microsoft requires itsCEO to have minimum stock ownership of 10 times base pay (with lower levels for other senior executives). Seehttps://www.microsoft.com/investor/CorporateGovernance/ShareholderAccountability/execstockrequirements.aspx[last accessed January 2016].

32Interestingly, evidence on the effect of CEO stake on investment is also consistent with the evidence of Asker,Farre-Mensa, and Ljungqvist (2015). They compare the investment levels of private versus publicly-listed companies,and find that private firms invest more than matched public firms do. High CEO stake firms are likely to be similarto private firms as both groups have concentrated ownership. Asker et al. (2015) argue the gap between privateand public-company investment is driven by the short-termism and earnings management motives of public companyCEOs.

33The evidence from the institutional ownership tests in Table 11 also suggest that higher institutional ownership,typically linked to better manager-shareholder alignment and monitoring, is associated with higher average levels ofinvestment.

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of both high and low uncertainty, the effect of interest – the impact of CEO stake×uncertainty oninvestment – remains interpretable as being caused by a distinct channel from that explaining themain effect.

4.2 Further results

To differentiate between the two remaining plausible explanations of alignment or risk aversion forthe basic results in Section 3, I turn to additional tests.

4.2.1 Other outcomes

If CEOs with large stakes are more risk-averse with respect to firm risk, they may decrease otherinvestment-like expenses, such as selling, general, and administrative (SG&A) expenses, whichinclude marketing and management expenses, and may contain investment-like expenditure. TheseCEOs may also choose to sell off risky assets and accelerate equity payouts to shrink the size of thefirm, converting risky operating assets into safe assets such as cash or into diversifiable personalwealth that can be re-invested in safe assets.

Table 8 presents evidence that is consistent with these effects. Columns 1 and 2 show thatCEO with larger stakes cut SG&A expenses more during times of high macroeconomic uncertainty.Estimates in Columns 1 and 2 suggest that a firm in the highest quintile of CEO ownership stakerelative to a firm in the lowest quintile decreases quarterly SG&A expenses (as a percentage ofassets) by 0.43-0.57 percentage points more for a 2 SD increase in uncertainty. Columns 3 and4 of Table 8 provide suggestive evidence that firms with higher ownership stake CEOs chooselarger equity payouts (dividends or equity repurchases) in response to higher uncertainty. Finally,firms with larger CEO stakes sell off more assets in response to heightened uncertainty: estimatesfrom Columns 5 and 6 suggest that a firm in the highest quintile of CEO stake, relative to alowest quintile firm, increases asset sales by 0.29-0.32 percentage points more for a 2 SD increasein uncertainty. The magnitude of these effects are economically significant, and these effects arerobust to specifications including firm effects (see Appendix Table B2).

4.2.2 Firm risk-taking

One direct implication of the risk aversion explanation is that high stake CEOs should attempt toreduce firm risk-taking relatively more during periods of high macroeconomic uncertainty. Managersmay decrease firm risk through channels other than reducing the quantity of investment. Forinstance, they may allocate funds towards less risky investments, or reduce the firm’s operatingleverage. Some of these actions – e.g., choosing less risky investments – are not directly observable

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in accounting data. Hence, to test whether managers reduce firm risk more generally, I proxy forfirm risk-taking by using the idiosyncratic volatility of firm stock returns (e.g., Guay 1999, Gormley,Matsa, and Milbourn 2013, Shue and Townsend 2014).34

To construct a time-varying measure of idiosyncratic stock return volatility, I decompose stockreturns r for firm i at time τ as

riτ = αi + βirmτ + εiτ (4)

in a one-factor model with aggregate stock market returns as the single risk factor. I also use atwo-factor model where the second factor is the return on the industry portfolio rind, based on theFama-French industry classification. Idiosyncratic returns are calculated as σit =

√∑τ∈tε2iτ , where

t is a quarter, and then annualized. I use either daily returns or weekly returns and estimatespecification (4) separately for each firm i in each quarter t.

Results from regressions with idiosyncratic volatility as the outcome are presented in Table9. In the first four columns, the outcome variable is idiosyncratic stock return estimated basedon daily returns and a one-factor model. Columns 5 through 8 use idiosyncratic stock returnsestimated using weekly returns and a two-factor model as the outcome variable. Firms with higherCEO stakes in periods of high macroeconomic uncertainty decrease their idiosyncratic stock returnvolatility relative to firms with low CEO stakes. The estimates are highly statistically significant,with an average t-statistic of over 5 on the coefficient of CEO stake×uncertainty. The estimatesimply that relative to a firm in the lowest quintile of CEO stake, a firm in the highest quintiledecreases annualized idiosyncratic risk by 0.028-0.065 percentage points for a 2 SD increase inuncertainty. This suggests that high stake CEOs reduce the risk-taking of their firms during timesof high macroeconomic uncertainty, and are consistent with the CEO risk aversion explanation.They are also inconsistent with the CEO alignment explanation. Outside shareholders are well-diversified and have no particular desire for firms to cut idiosyncratic risk more in times of highmacroeconomic uncertainty.

34While commonly used in the literature as a proxy for firm risk-taking, the volatility of equity returns is not aflawless measure. There may be underlying trends in equity return volatility (e.g., Campbell, Lettau, Malkiel, andXu 2001), and equity return volatility may not necessarily reflect changes in underlying riskiness but could be drivenby market perception of risks or by discount rate volatility (rather than fundamental cash flow volatility). In theabsence of a better measure, I use residual equity return volatility as a proxy for idiosyncratic firm risk. Gilje (2014)studies risk-shifting in the oil and gas industry, and uses information on the type of capital expenditures as a directmeasure of risk-taking, based on firms’ financial statements disclosures. However, information on type of investmentis limited only to the oil and gas industry.

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4.2.3 Evidence from stock returns

If better alignment of managers with shareholders causes the larger investment declines of highCEO stake firms in periods of high uncertainty, excess stock returns of these firms in these timeperiods should exceed those of low CEO stake firms. Stock returns can thus shed light on whetherthese investment declines are optimal for outside shareholders. The impact of lowering investmenton equity returns may not be immediately apparent and hence difficult to detect in short-run orcontemporaneous stock returns unless investors rapidly update their expectations upon observinghigher macroeconomic uncertainty. Hence, I use one-year (four-quarter) excess equity returns asthe outcome variable.35

Results are presented in Table 10. In normal times, firms with higher CEO stakes earn largerexcess equity returns than low CEO stake firms, but this pattern of relative outperformance declinesin the year subsequent to a period of high macroeconomic uncertainty. On average, firms withCEO stakes in the highest quintile relative to those in the lowest quintile have lower excess annualequity returns of 7.6 to 10.1% (depending on the specification) in the year subsequent to a 2SD increase in macroeconomic uncertainty, relative to the difference between highest- and lowest-quintile firms during a period with average aggregate uncertainty. During a period with averageaggregate uncertainty, firms in the highest quintile of CEO stake have 2.6 to 3.4% higher excessannual equity returns than lowest quintile firms.

This evidence is inconsistent with the alignment explanation of larger investment declines. Itsupports the risk aversion explanation, as the results suggest that the investment declines duringperiods of high uncertainty are not in the interests of outside shareholders. Interestingly, thehigher excess returns of high stake CEOs during times of average uncertainty may also indicatethat the higher level of investment of high stake CEO firms during normal times is not suboptimal,suggesting that an explanation for the higher levels of investment during normal times is unlikelyto be the same as that for the large relative investment declines in periods of high uncertainty.

4.2.4 Testing alignment using institutional ownership

Differences in institutional ownership can serve as another test of the alignment explanation. Largeinstitutional owners are effective monitors because they have strong incentives to overcome the free-riding problem among small shareholders (e.g., Shleifer and Vishny 1997, Hartzell and Starks 2003,Ferreira and Matos 2008). Firms with a larger fraction of institutional owners are likely to havegreater manager-shareholder alignment due to more effective monitoring. If the larger investment

35Excess equity returns are calculated based on a four-factor model, including the market, value, and size, andmomentum factors. Betas are estimated based on the past 60 months of monthly returns, and are winsorized at the0.5 and 99.5th percentiles to mitigate the impact of outlier estimates.

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declines of high stake CEO firms during periods of high uncertainty is due to better alignment,then investment declines should be more pronounced among high institutional ownership firms.

To conduct this test, I use data from the Thomson Financial 13F database to calculate institu-tional ownership at the firm-quarter level. Firm-quarters where there is no indication of institutionalownership in the Thomson Financial data are assigned zero institutional ownership. As with CEOstake, I divide institutional ownership into quintiles, and then estimate an analogous equation to(1) but replacing CEO stake quintiles with institutional ownership quintiles.

Results are shown in Table 11. Across specifications with differing levels of fixed effects, and us-ing both JLN uncertainty and VIX as proxies for macroeconomic uncertainty, results show that theeffect of higher institutional ownership on investment during times of high uncertainty is approxi-mately zero or weakly positive. This is inconsistent with the alignment explanation of investmentdeclines for high stake CEO firms, as firms with better manager-shareholder alignment in the formof greater institutional ownership do not cut investment more in times of high uncertainty. More-over, results in Appendix Table B3 show that the effect of CEO stake is concentrated in firms whereinstitutional ownership is lower and the CEO likely has greater discretion to reduce investment dueto her personal risk preferences.36

4.2.5 Option ownership vega

Beyond direct stock ownership, options are an important component of CEO compensation andaffect CEO firm risk exposure. However, Ross (2004) shows that options can induce CEOs to beeither more risk-averse or risk-loving. Intuitively, CEOs who own options with values that are highlysensitive to changes in volatility – high “vega” options – are incented to take on more risk. On theother hand, CEOs with high “delta” options (with high sensitivity to changes in the underlyingstock price) have large concentrated exposure to firm risk and hence become more risk-averse.

Here, I test the effect of differences in the vega of CEO option-holdings on the relationshipbetween CEO stake and investment in times of high uncertainty. Estimates of the Black-Scholesparameters are needed for the CEO options portfolio to calculate estimates of the vega and delta ofCEO options compensation.37 Execucomp does not provide data on Black-Scholes parameters forpart of the CEO options portfolio, so I calculate CEO option vega and impute key parameters using

36Appendix Table B3 splits the Execucomp sample into above- and below-median institutional ownership, andestimates regression equation (1) separately in both subsamples. The negative coefficient on CEO stake×Uncertaintyis larger in magnitude in the below-median institutional ownership sample.

37The Black-Scholes formula for valuing European call options is computed according to Merton (1973). Thekey parameters are the price of the underlying stock at the date of valuation, the exercise price of the option, theannualized volatility (estimated using the standard deviation of monthly stock returns over the past 60 months), theriskfree rate (estimated as the maturity-matched Treasury bill yield), and the dividend rate (estimated as past yearannual dividend rate).

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the method of Core and Guay (2002), as previous studies have done (e.g., Panousi and Papanikolaou2012, Shue and Townsend 2014).38

Dollar vega is the partial derivative of the value of options owned with respect to a 1 percentagepoint change in stock-return volatility. As a more meaningful measure of incentives, I divide thedollar vega by the total dollar value of shares owned by the CEO in order to normalize the dollarvega measure. I then split the sample by firm-quarters where this vega ratio is above and belowmedian, and estimate equation (1) in both subsamples separately. Table 12 presents the results:each pair of columns shows estimation results first for the below-median vega ratio sample, then forthe above-median sample. The relative investment decline of high CEO stake firms during periodsof high uncertainty is concentrated in firms where the CEO has low options vega.

These results are consistent with managerial risk aversion driving larger investment declines forhigh CEO stake firms in periods of high uncertainty. They also suggest that firms may attempt tomitigate excessive managerial risk aversion by using options that are highly sensitive to volatility.However, if CEO options vega are endogenously determined, these results could still be consistentwith the alignment explanation. If a subset of the firms with high CEO ownership stakes findit optimal to decrease investment more in periods of high uncertainty, these firms may choose toaward fewer options to their CEOs and will end up as low CEO vega firms.

4.3 Robustness

4.3.1 Controlling for CEO tenure

Pan, Wang, and Weisbach (2013) argue that CEO tenure directly affects investment because thereare tenure investment “cycles” driven by agency considerations. CEO tenure is correlated withCEO stake due to the gradual accumulation of ownership stake over time from stock and optionsawards, and thus may potentially confound the results.

Table 13 shows the results when I add CEO tenure and CEO tenure×macroeconomic uncertaintyas a control variable to the baseline OLS specification.39 The magnitudes of the coefficients onthe interaction of CEO stake×macroeconomic uncertainty are unchanged relative to the baseline

38The method of Core and Guay (2002) treats CEO option ownership in the annual Execucomp data as threeseparate awards: i) options awarded in that year, ii) previously awarded, unexercised, but exerciseable options, andiii) previously awarded, unexercised, but unexerciseable options. Maturity and exercise prices are imputed for thelatter two award categories. Core and Guay (2002) show that their method yields a very close approximation tothe actual value of the option portfolio, based on comparisons to hand-collected data for a sample of CEO optionportfolios.

39To reduce the potential effect of measurement error attenuation bias on the effect of CEO tenure, I excludeobservations where data on CEO tenure in Execucomp is unclear, such as when a CEO is re-hired after a previousstint as CEO. This reduces the number of observations in the regressions with CEO tenure as an explanatory variablerelative to the number of observations in the baseline regressions of Table 3.

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estimates; the estimates remain statistically significant and are slightly larger in absolute value.The impact of CEO tenure on investment is positive (consistent with Pan et al. 2013), but notstatistically significant. Moreover, the interaction effect of CEO tenure×macroeconomic uncertaintyis weakly positive and statistically insignificant.

While these results cannot confirm or reject the validity of the exclusion restriction assumptionof the IV estimation strategy, which is intrinsically untestable, they support it by suggesting thatCEO tenure does not directly affect investment when CEO stake is also included in OLS regressions.The regressions in Table 13 do not support the contention that CEO tenure direct affects investmentonce we control for CEO stake. Furthermore, even if the exclusion restriction assumption did nothold due to the direct impact of CEO tenure, the bias would attenuate the magnitude of the effectof CEO ownership stake on investment during times of high macroeconomic uncertainty, becausethe direct effect of CEO tenure on investment appears to be (insignificantly) positive during timesof high macroeconomic uncertainty. The direct effect of CEO tenure×uncertainty on investmentwould have to be negative in order to explain the negative effect of CEO stake on investment intimes of high uncertainty.

4.3.2 Adjusting CEO stake for options ownership

The baseline measure of CEO stake does not include options ownership. As Frydman and Jenter(2010) show, options were an important component of total CEO compensation in the years from1990 to 2010, constituting more than 20% of total pay for S&P 500 CEOs. If CEO options ownershipis negatively correlated with CEO equity ownership, this could confound the results.

To properly reflect the impact of options ownership on CEO risk exposure, I include optionsdelta, the sensitivity of the value of options owned to changes in the underlying stock price (Ross2004). Results in the previous section show that higher vega of options ownership incents highstake CEOs to cut back less on investment and mitigates risk aversion. High options delta has theopposite impact, and is analogous to owning more stock. I adjust the measure of CEO stake toinclude the dollar delta of total options ownership. Adjusted CEO stake is calculated as

CEOstakeait =CEO firm equity wealthit +

∑j∈J

∆ijt × number of options in award jit

CEO cash compensationit, (5)

where option awards are separately counted using the method of Core and Guay (2002), and ∆ijt

is the dollar delta of the option award calculated using estimated Black-Scholes parameters foroptions award j.

I re-estimate specification (1), where the measure of CEO stake adjusts for options ownership,and find results that are quantitatively and qualitatively similar to the baseline OLS results in

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Table 3, in which the measure of CEO stake excludes options ownership. Results are shown inAppendix Table B4. Again, the coefficient on the interaction term of option ownership-adjustedCEO stake with output gap is insignificant and precisely estimated at zero, whereas the interactionof option ownership-adjusted CEO stake with both measures of macro uncertainty is significantlynegative, with similar magnitudes to those in the baseline results of Table 3.

4.3.3 Other robustness checks

The baseline results are similar under different definitions of ownership stake. Columns 1 to 3from Appendix Table B5 contain results, where CEO stake is redefined to include only salary inthe denominator, as opposed to total current cash compensation (including bonus). Columns 4through 6 show results using the baseline definition of ownership stake, but calculated for all topfive executives (including the CEO). Results are qualitatively unchanged.

Another test is to directly compare the impact of CEO stake×output gap relative to that of CEOstake×uncertainty, in a “horserace” regression where both variables are included. Appendix TableB6 presents results from these regressions. The coefficients on CEO stake×JLN uncertainty, andCEO stake×VIX are essentially unchanged from the baseline results, even with the addition of bothCEO stake×output gap and output gap variables. Moreover, the coefficient on CEO stake×outputgap is precisely estimated at zero, implying no difference in underlying cyclicality of investmentbetween high and low CEO stake firms.

Another robustness check is to use different fixed effects to further restrict the identifyingvariation. Appendix Table B7 shows the results of the baseline OLS specifications using industry-quarter fixed effects, which control non-parametrically for differing industry trends across time andindustry cyclicality. In these regressions, the effect of interest (CEO stake×macro uncertainty) isidentified using variation in CEO stake within the same industry and time period. The coefficientof interest is an estimate of how CEO stake affects within-industry-quarter investment differentiallyacross times of high and low macroeconomic uncertainty. The results are similar: firms with highCEO stakes do not invest differentially in times of high or low output gap but do decrease investmentmore when uncertainty is higher. Similarly, when industry-quarter fixed effects are used in thereduced form of the instrumental variable estimation strategy, quantitatively similar estimates ofthe effect of recent CEO×macro uncertainty on investment are obtained (results untabulated).

5 Macroeconomic implications

These results have potentially significant macroeconomic ramifications. First, the strong procycli-cality of investment is an important component of business cycle volatility in aggregate output.

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Keynes (1936) argued that “animal spirits” were an important factor behind business cycles, andwhile subsequent interpretations of animal spirits have highlighted factors such as consumer con-fidence, risk aversion and fear of uncertainty can also be considered behavioral primitives andcomponents of “animal spirits.” The results in this study provide evidence that risk aversion playsan important role in the business cycle, particularly during downturns associated with high levels ofaggregate uncertainty. This risk aversion is a byproduct of large managerial ownership stakes thatare designed to mitigate managerial moral hazard. A common solution to a fundamental tensionwithin modern corporations – the alignment of managers and shareholders – indirectly leads tobusiness cycle amplification.

Moreover, the evidence implies that the risk aversion of a very small but important set of individ-uals – CEOs – plays a role in amplifying the cyclicality of investment. While many macroeconomicstudies assume a representative household in their analyses, the importance of CEO personal equitystakes in firm decisions suggests that the incentives and balance sheets of a very small group ofeconomic agents can potentially affect macroeconomic outcomes.

High CEO stake public firms are likely to be the most similar public firms to most privatefirms, in terms of managerial ownership. Private firms are important to consider in assessingmacroeconomic impacts as they contribute significantly to aggregate investment.40 Asker et al.(2015) show that private firms have very different investment behavior than public firms. Figure5 from their study (p. 358) shows that private firms decreased investment significantly more inresponse to the financial crisis during the year 2009 than the group of matched public firms. Notonly is this suggestive evidence consistent with the results of this study on the role of managerialrisk aversion, it also suggests that the impact of risk aversion on aggregate investment dynamicscould be significant as many private firms behave like public firms with high stake CEOs.

One potential extension is to consider whether variation across countries in manager stakes isassociated with differences in the cyclicality and volatility of investment over the business cycle.Smaller managerial ownership stakes tend to be more common in the United States than in manyother countries (e.g., La Porta, Lopez-De-Silanes, and Shleifer 1999), and may explain cross-countrydifferences in business cycle volatility. In less financially-developed countries, large managerialstakes may be particularly important to mitigate agency issues, counteract against poor shareholderprotections, or substitute for costly external finance, but may come at the cost of magnifyingmanagerial risk aversion in times of high uncertainty, leading to more business cycle volatility.

40According to Asker, Farre-Mensa, and Ljungqvist (2015), private firms accounted for 52.8% of aggregate nonres-idential fixed investment in 2010.

25

6 Conclusion

Management equity ownership is an important tool to align managers with shareholders and miti-gate moral hazard. But while it is undoubtedly an important mechanism to deal with conflicts ofinterest between managers and shareholders, it poses costs as well. One cost is that concentratedmanagerial ownership leads to excessive risk aversion. I present evidence that managerial riskaversion causes larger declines in investment during periods of high macroeconomic uncertainty.Exploiting exogenous CEO departures, I show that the association between CEO ownership stakeand investment declines in times of high macroeconomic uncertainty is causal, and that these ef-fects are economically meaningful: CEOs in the highest quintile of ownership stake cut quarterlyinvestment levels by 0.40-0.90 percentage points more when macroeconomic uncertainty increasesby two standard deviations.

While there are a number of potential explanations for these results, such as better alignmentbetween managers and shareholders due to large managerial ownership stakes and managerial riskaversion, I find evidence that managerial risk aversion explains the results. High CEO stake firmscut idiosyncratic firm risk in times of high uncertainty and have lower stock excess returns subse-quent to periods of high uncertainty. These additional results are consistent with the role of CEOrisk aversion but are not consistent with alternative explanations.

These results provide empirical support for the classic argument of Keynes (1936) that “animalspirits” play a role in business cycle fluctuations. Here, the risk aversion of CEOs causes largerdeclines in firm investment in periods of high macroeconomic uncertainty. The incentives of asmall group of individuals – the CEOs who own large equity ownership stakes and have significantdecision-making power in the firms they manage – have macroeconomic impacts by driving largerfirm investment declines in business cycle downturns. One might consider this a different manifes-tation of the “granular” origins of macroeconomic fluctuations, à la Gabaix (2011). In this case,the “granular” effects are not only due to the fact that firms are themselves large relative to thewhole economy, but also due to the fact that a very small number of individuals – CEOs – possesssignificant control over the investment decisions of these firms.

As private firms have high managerial ownership concentration, large investment declines indownturns may be a feature of their investment behavior as well. Countries with less-developedfinancial markets, less risk-sharing with outside investors, and higher levels of managerial owner-ship, may also experience more volatile investment throughout the business cycle due to the highmanagerial risk exposure. Both potentially merit future study. Using a more structural approachto illustrate and quantify the effects of managerial ownership and risk aversion may also lendadditional insights to its role in amplifying business cycle volatility.

26

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8 Tables and Figures

Figure 1: Investment of High Versus Low CEO Stake Firms

Notes: The grey highlighted areas are NBER recession quarters. Quarterly investment is measured as capital expen-diture scaled by lagged assets, adjusted for quarter-of-year seasonality. See Section 2.1 for calculation of CEO stakemeasure.

Figure 2: Time-Series of Macroeconomic Indicators

Notes: The grey highlighted areas are NBER recession quarters. All macroeconomic variables are normalized to havemean of 0 and a standard deviation of 1 during the sample period.

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Table 1: Summary Statistics: Sample Split by Quintile of CEO stake

CEO stake Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Mean NTotal assets, M 3,299.4 5,335.4 6,484.6 7,491.1 5,463.0 5,614.7 118,733

Investment / assets, pct 1.388 1.465 1.532 1.693 2.049 1.625 117,314

Tobin’s Q 1.862 1.803 1.982 2.169 2.900 2.143 118,702

Cash flow / assets, pct 1.067 2.091 2.460 2.765 3.089 2.299 110,512

Leverage (market value of equity) 0.244 0.224 0.202 0.184 0.130 0.197 114,154

Cash / assets, pct 17.33 13.01 13.09 14.25 18.39 15.21 118,655

Return on assets, pct -0.324 3.929 5.445 6.758 7.451 4.654 117,128

Annual sales growth, pct 10.12 10.10 12.46 16.19 24.27 14.63 116,892

Stock return SD (monthly), pct 14.71 12.75 12.33 12.46 13.30 13.10 116,900

Notes: The sample runs from 1992 to 2013. Data is at the quarterly level, except where indicated.

Table 2: Impact of CEO Stake on Investment During the Financial Crisis 2008-2009(1) (2)

Inv/Assets Inv/AssetsCEO stake quintile 0.0642∗∗∗

(0.0132)

CEO stake X fin. crisis -0.0529∗∗∗(0.0191)

CEO stake highest quint 0.199∗∗∗(0.0480)

CEO stake(hi) X fin. crisis -0.189∗∗∗(0.0536)

Controls Yes Yes

Industry FE Yes Yes

Time FE Yes YesObservations 84158 84158R2 0.321 0.321

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent.The sample is Execucomp firms from 1992-2013. CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated as described in the text in Section 2.1. Controls include lagged values of Tobin’s Q, balance sheet cash level,size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthlystock returns for past 60 months. Industry and year-quarter fixed effects. Standard errors double clustered by firmand quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table 3: Impact of CEO Stake on Investment in Different Macroeconomic Conditions(1) (2) (3) (4) (5) (6) (7) (8) (9)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsCEO stake quintile 0.0626∗∗∗ 0.0712∗∗∗ 0.0621∗∗∗ 0.0575∗∗∗ 0.0732∗∗∗ 0.0641∗∗∗ 0.0634∗∗∗ 0.0708∗∗∗ 0.0616∗∗∗

(0.0130) (0.0129) (0.0131) (0.0128) (0.0127) (0.0129) (0.0124) (0.0126) (0.0128)

CEO stake X JLN uncertainty -0.0171∗∗ -0.0228∗∗∗ -0.0226∗∗∗(0.00777) (0.00767) (0.00789)

JLN uncertainty -0.0843∗∗ 0.0567∗∗(0.0397) (0.0241)

CEO stake X VIX -0.0228∗∗∗ -0.0226∗∗∗ -0.0223∗∗∗(0.00709) (0.00673) (0.00665)

VIX 0.0221 0.0541∗∗(0.0259) (0.0212)

CEO stake X output gap 0.00606 -0.00159 -0.00100(0.00951) (0.00965) (0.00943)

Output gap 0.139∗∗∗ -0.00950(0.0268) (0.0272)

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Time trend No Yes No No Yes No No Yes No

Time FE No No Yes No No Yes No No YesObservations 81254 81254 81254 84158 84158 84158 84158 84158 84158R2 0.294 0.311 0.321 0.291 0.311 0.321 0.297 0.311 0.321

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. CEO stake quintile is the lagged fiscalyear quintile of CEO stake, calculated as described in the text in Section 2.1. The sample is Execucomp firms from 1992-2013. Output gap, JLN uncertainty,and VIX time-series variables are all normalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is the quarterlyaverage of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility of S&P 500 index options.Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standarddeviation of monthly stock returns for past 60 months. Time trend is a linear time trend in years. Time fixed effects are year-quarter fixed effects. Standarderrors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table 4: Firm Fixed Effects Estimates: Impact of CEO Stake on Investment(1) (2) (3) (4) (5) (6)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsCEO stake quintile 0.0645∗∗∗ 0.0613∗∗∗ 0.0425∗∗∗ 0.0642∗∗∗ 0.0638∗∗∗ 0.0440∗∗∗

(0.0109) (0.0108) (0.0106) (0.0108) (0.0107) (0.0106)

CEO stake X JLN uncertainty -0.0196∗∗∗ -0.0146∗∗ -0.0130∗(0.00736) (0.00703) (0.00684)

JLN uncertainty 0.0427∗∗ 0.0168(0.0211) (0.0694)

CEO stake X VIX -0.0205∗∗∗ -0.0178∗∗∗ -0.0138∗∗(0.00672) (0.00615) (0.00541)

VIX 0.0493∗∗ 0.0443(0.0199) (0.0697)

Controls Yes Yes Yes Yes Yes Yes

Controls X uncertainty No Yes Yes No Yes Yes

Firm FE Yes Yes Yes Yes Yes Yes

Time trend Yes Yes No Yes Yes No

Time FE No No Yes No No YesObservations 81782 81782 81782 84711 84711 84711

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, inpercent.The sample is Execucomp firms from 1992-2013. CEO stake quintile is the lagged fiscal year quintile ofCEO stake, calculated as described in the text in Section 2.1. JLN uncertainty and VIX time-series variables are allnormalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is thequarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterlyaverage of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheetcash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standard deviationof monthly stock returns for past 60 months. Controls X Uncertainty indicates that additional controls consistingof all controls, interacted with the uncertainty time-series variables, are also included. Time trend is a linear timetrend in years. Time fixed effects are year-quarter fixed effects. Standard errors double clustered by firm and quarter.* p < 0.10, ** p < 0.05, *** p < 0.01

35

Table 5: Reduced Form Estimates: Recent New CEO after Exogenous CEO departure(1) (2) (3) (4) (5) (6) (7) (8)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsRecent CEO (exogenous CEO depart) -0.144∗∗∗ -0.149∗∗∗ -0.0577 -0.0487 -0.147∗∗∗ -0.159∗∗∗ -0.0683∗ -0.0665∗

(0.0421) (0.0411) (0.0365) (0.0359) (0.0429) (0.0418) (0.0369) (0.0363)

Recent CEO X JLN uncertainty 0.0790∗∗∗ 0.0618∗∗ 0.0591∗∗ 0.0582∗∗(0.0266) (0.0255) (0.0248) (0.0237)

JLN uncertainty -0.196∗∗∗ -0.0566∗∗ -0.118∗∗∗ 0.0257(0.0387) (0.0278) (0.0339) (0.0256)

Recent CEO X VIX 0.0409∗ 0.0294 0.0423∗ 0.0370∗(0.0243) (0.0246) (0.0217) (0.0213)

VIX -0.0890∗∗ -0.00768 -0.0553∗ 0.00912(0.0400) (0.0337) (0.0305) (0.0255)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Controls X uncertainty Yes Yes Yes Yes Yes Yes Yes Yes

Time trend No Yes No Yes No Yes No YesFixed effects Industry Industry Firm Firm Industry Industry Firm FirmN 259580 259580 261669 261669 267988 267988 270116 270116

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Compustat firms from1992-2013 with assets larger than $10M (in 1990 dollars). Recent CEO is a lagged fiscal year indicator for whether the CEO is a new CEO subsequent to anexogenous CEO departure within the past five years. JLN uncertainty and VIX time-series variables are all normalized to have mean zero and standard deviationone during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIXis the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage;contemporaneous values of cash flow, annual sales growth, standard deviation of monthly stock returns for past 60 months. “Controls X uncertainty” indicatesthat additional controls consisting of all controls, interacted with the uncertainty time-series variables (either JLN uncertainty or VIX), are also included. Timetrend is a linear time trend in years. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

36

Table 6: TSIV estimates: Recent New CEO after Exogenous CEO departure(1) (2) (3) (4) (5) (6) (7) (8)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets

CEO stake (hi) 1.031*** 1.047*** 0.590 0.452 1.078*** 1.132*** 0.811* 0.727*(0.311) (0.294) (0.436) (0.397) (0.325) (0.303) (0.467) (0.412)

CEO stake (hi) X JLN uncertainty -0.451*** -0.353** -0.371** -0.359**(0.157) (0.149) (0.148) (0.141)

JLN uncertainty -0.124*** -0.019 -0.048 0.086**(0.044) (0.037) (0.042) (0.038)

CEO stake (hi) X VIX -0.272* -0.199 -0.289** -0.251*(0.156) (0.155) (0.139) (0.134)

VIX -0.047 0.011 -0.003 0.049(0.040) (0.035) (0.039) (0.034)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Controls X uncertainty Yes Yes Yes Yes Yes Yes Yes Yes

Time trend No Yes No Yes No Yes No YesFixed effects Industry Industry Firm Firm Industry Industry Firm FirmFirst stage N 84,084 84,653 84,084 84,653 87,295 87,888 87,295 87,888Second stage N 259,580 259,580 261,669 261,669 267,988 267,988 270,116 270,116

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. CEO stake (indicator for highest quintile)is instrumented for by an indicator for recent CEO after exogenous CEO departure, CEO stake×uncertainty is instrumented by recent CEO×uncertainty. JLNuncertainty and VIX time-series variables are all normalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is thequarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility of S&P 500 indexoptions. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage; contemporaneous values of cash flow, annual sales growth, standarddeviation of monthly stock returns for past 60 months. “Controls X uncertainty” indicates that additional controls consisting of all controls, interacted with theuncertainty time-series variables (either JLN uncertainty or VIX), are also included. Time trend is a linear time trend in years. Standard errors double clusteredby firm and quarter, and are calculated as in Inoue and Solon (2010). * p < 0.10, ** p < 0.05, *** p < 0.01

37

Table 7: Reduced Form Estimates: Recent New CEO, Insider vs. Outsider hire(1) (2) (3) (4) (5) (6) (7) (8)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsRecent CEO (outsider) -0.115 -0.0609 -0.122 -0.0737

(0.0858) (0.0744) (0.0849) (0.0781)

Recent CEO (out) X JLN uncertainty 0.0321 0.0919∗∗(0.0445) (0.0427)

Recent CEO (out) X VIX 0.0169 0.0641(0.0511) (0.0448)

Recent CEO (insider) -0.156∗∗∗ -0.0441 -0.167∗∗∗ -0.0619(0.0462) (0.0398) (0.0469) (0.0393)

Recent CEO (ins) X JLN uncertainty 0.0714∗∗ 0.0435(0.0304) (0.0272)

Recent CEO (ins) X VIX 0.0334 0.0259(0.0262) (0.0228)

JLN uncertainty -0.0524∗ 0.0289 -0.0564∗∗ 0.0277(0.0279) (0.0256) (0.0278) (0.0256)

VIX -0.00654 0.0106 -0.00787 0.0103(0.0331) (0.0255) (0.0334) (0.0255)

Time trend Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Controls X uncertainty Yes Yes Yes Yes Yes Yes Yes YesFixed effects Industry Firm Industry Firm Industry Firm Industry FirmN 259580 261669 267988 270116 259580 261669 267988 270116

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Compustat firms from1992-2013 with assets larger than $10M (in 1990 dollars). Recent CEO (outsider) is a lagged fiscal year indicator for whether the CEO is a new CEO who is anoutsider hire subsequent to an exogenous CEO departure within the past five years. Recent CEO (insider) is a lagged fiscal year indicator for whether the CEO

38

is a new CEO who is an insider hire subsequent to an exogenous CEO departure within the past five years. JLN uncertainty and VIX time-series variables are allnormalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertaintymeasure as calculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values ofTobin’s Q, balance sheet cash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthly stockreturns for past 60 months. “Controls X uncertainty” indicates that additional controls consisting of all controls, interacted with the uncertainty time-seriesvariables (either JLN uncertainty or VIX), are also included. Time trend is a linear time trend in years. Standard errors double clustered by firm and quarter.* p < 0.10, ** p < 0.05, *** p < 0.01

39

Table 8: Other Outcomes: SG&A Expenses, Total Payouts, and Asset Sales(1) (2) (3) (4) (5) (6)

SGA/Assets SGA/Assets Payouts/Assets Payouts/Assets Asset sales Asset salesCEO stake quintile -0.213∗∗∗ -0.217∗∗∗ -0.0236∗∗ -0.0231∗∗ 0.0115 0.00725

(0.0538) (0.0538) (0.00973) (0.00995) (0.0280) (0.0281)

CEO stake X JLN uncertainty -0.0714∗∗ 0.0240∗∗∗ 0.0365∗∗(0.0288) (0.00620) (0.0156)

JLN uncertainty 0.260∗∗∗ -0.108∗∗∗ -0.0667(0.0819) (0.0234) (0.0419)

CEO stake X VIX -0.0533∗∗ 0.00461 0.0397∗∗∗(0.0208) (0.00607) (0.0129)

VIX 0.166∗∗ -0.0283 -0.110∗∗∗(0.0688) (0.0246) (0.0370)

Controls Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

Time trend Yes Yes Yes Yes Yes YesObservations 75979 78764 76078 78837 77274 80045R2 0.379 0.379 0.174 0.174 0.096 0.097

Notes: The outcome variables in these regressions are: selling, general, and administrative (SG&A) expenses divided by lagged assets, in percent, in Columns 1and 2, total equity payouts (a sum of dividends paid and repurchases) divided by lagged assets, in percent, in Columns 3 and 4, and total asset sales divided bylagged assets, in percent, in Columns 5 and 6. The sample is Execucomp firms from 1992-2013. CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated as described in the text in Section 2.1. JLN uncertainty and VIX time-series variables are all normalized to have mean zero and standard deviationone during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIXis the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage;contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthly stock returns for past 60 months. Time trend is a linear timetrend in years. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

40

Table 9: Other Outcomes (II): Idiosyncratic Firm Risk(1) (2) (3) (4) (5) (6) (7) (8)

Idio. vol (daily) Idio. vol (daily) Idio. vol (daily) Idio. vol (daily) Idio. vol, 2F Idio. vol, 2F Idio. vol, 2F Idio. vol, 2FCEO stake quintile -0.00121 0.00619∗∗∗ -0.00291∗ 0.00392∗∗ -0.000413 0.00619∗∗∗ -0.00196 0.00439∗∗∗

(0.00158) (0.00178) (0.00155) (0.00157) (0.00148) (0.00163) (0.00147) (0.00147)

CEO stake X JLN uncertainty -0.00815∗∗∗ -0.00577∗∗∗ -0.00742∗∗∗ -0.00537∗∗∗(0.00121) (0.00102) (0.00103) (0.000926)

JLN uncertainty 0.0900∗∗∗ 0.0820∗∗∗ 0.0752∗∗∗ 0.0675∗∗∗(0.00690) (0.00708) (0.00589) (0.00597)

CEO stake X VIX -0.00539∗∗∗ -0.00403∗∗∗ -0.00458∗∗∗ -0.00354∗∗∗(0.00112) (0.000919) (0.00115) (0.00103)

VIX 0.0844∗∗∗ 0.0777∗∗∗ 0.0695∗∗∗ 0.0630∗∗∗(0.00472) (0.00394) (0.00459) (0.00400)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Time trend Yes Yes Yes Yes Yes Yes Yes YesFixed effects Industry Firm Industry Firm Industry Firm Industry FirmN 77678 78196 80573 81118 77678 78196 80573 81118

Notes: The outcome variables in these regressions are: annualized idiosyncratic equity return volatility (in percent) as measured in daily return regressions withmarket returns as a single risk factor in Columns 1-4 (as in equation (4) in the text), and annualized idiosyncratic equity return volatility (in percent) as measuredin weekly return regressions with market and industry returns as two risk factors in Columns 5-8. The sample is Execucomp firms from 1992-2013. CEO stakequintile is the lagged fiscal year quintile of CEO stake, calculated as described in the text in Section 2.1. JLN uncertainty and VIX time-series variables are allnormalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertaintymeasure as calculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values ofTobin’s Q, balance sheet cash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age. Time trend is a linear time trend in years.Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

41

Table 10: Excess equity returns by CEO ownership stake(1) (2) (3) (4)

Excess return Excess return Excess return Excess returnCEO stake quintile 0.00847∗∗ 0.00664∗∗ 0.00712∗∗ 0.00667∗∗

(0.00352) (0.00317) (0.00338) (0.00304)

CEO stake X JLN uncertainty -0.0126∗∗ -0.00991∗∗(0.00516) (0.00475)

JLN uncertainty 0.0501∗∗ 0.0606∗∗∗(0.0250) (0.0178)

CEO stake X VIX -0.00957∗∗ -0.00932∗∗(0.00444) (0.00406)

VIX 0.0534∗∗∗ 0.110∗∗∗(0.0191) (0.0115)

Fixed effects Industry Industry-Quarter Industry Industry-QuarterN 67434 67434 67434 67434

Notes: The outcome variable in these regressions is excess stock returns, adjusted by a four factor model including the three Fama-French factors and momentum,from quarter t+ 1 through quarter t+ 4 (for the next year starting from quarter t+ 1). The sample is Execucomp firms from 1992-2013. CEO stake quintile isthe lagged fiscal year quintile of CEO stake, calculated as described in the text in Section 2.1. JLN uncertainty and VIX time-series variables are all normalizedto have mean zero and standard deviation one during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure ascalculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility of S&P 500 index options. Standard errors double clustered by firm andquarter. * p < 0.10, ** p < 0.05, *** p < 0.01

42

Table 11: Impact of Institutional Ownership on Investment in Different Macroeconomic Conditions(1) (2) (3) (4)

Inv/Assets Inv/Assets Inv/Assets Inv/AssetsInstitutional ownership, quintile 0.0331∗∗ 0.0223 0.0350∗∗ 0.0173

(0.0163) (0.0143) (0.0160) (0.0138)

Inst own X JLN uncertainty 0.0152∗ 0.000848(0.00799) (0.00561)

JLN uncertainty -0.0475 0.00191(0.0431) (0.0295)

Inst own X VIX 0.00146 -0.00351(0.00668) (0.00529)

VIX 0.00443 0.0221(0.0335) (0.0259)

Controls Yes Yes Yes Yes

Time trend Yes Yes Yes YesFixed effects Industry Firm Industry FirmN 72948 73409 75795 76278

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Execucomp firms from 1992-2013. Institutional ownership data is from the Thomson Reuters 13F database. Institutional ownership quintile is the lagged fiscal year quintile of institutionalownership fraction of the firm’s equity. JLN uncertainty and VIX time-series variables are all normalized to have mean zero and standard deviation one duringthe sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterlyaverage of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage; contemporaneousvalues of cash flow, annual sales growth, CEO age, standard deviation of monthly stock returns for past 60 months. Time trend is a linear time trend in years.Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

43

Table 12: Investment Regressions, Splitting Sample by Vega Ratio(1) (2) (3) (4) (5) (6) (7) (8)

Low Vega High Vega Low Vega High Vega Low Vega High Vega Low Vega High VegaCEO stake quintile 0.0828∗∗∗ 0.0140 0.0791∗∗∗ 0.0341∗∗ 0.0823∗∗∗ 0.0146 0.0780∗∗∗ 0.0340∗∗∗

(0.0175) (0.0165) (0.0164) (0.0136) (0.0172) (0.0156) (0.0159) (0.0128)

CEO stake X JLN uncertainty -0.0369∗∗∗ -0.00331 -0.0223∗∗ 0.00292(0.0108) (0.00937) (0.00981) (0.00799)

JLN uncertainty 0.0919∗∗∗ 0.0273 0.0488∗ 0.00640(0.0302) (0.0284) (0.0274) (0.0230)

CEO stake X VIX -0.0308∗∗∗ -0.0117 -0.0239∗∗∗ -0.00511(0.00898) (0.00928) (0.00843) (0.00826)

VIX 0.0854∗∗∗ 0.0350 0.0641∗∗∗ 0.0240(0.0251) (0.0253) (0.0242) (0.0210)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Time trend Yes Yes Yes Yes Yes Yes Yes YesFixed effects Industry Industry Firm Firm Industry Industry Firm FirmN 40673 39530 40959 39667 42220 40887 42532 41042

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Execucomp firms from1992-2013. Vega ratio calculated as the ratio of vega to value of total shares owned, and low vega are firm-quarters with vega ratio below sample median (viceversa for high vega). CEO stake quintile is the lagged fiscal year quintile of CEO stake, calculated as described in the text in Section 2.1. JLN uncertainty andVIX time-series variables are all normalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is the quarterlyaverage of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility of S&P 500 index options.Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standarddeviation of monthly stock returns for past 60 months. Time trend is a linear time trend in years. Standard errors double clustered by firm and quarter.* p < 0.10, ** p < 0.05, *** p < 0.01

44

Table 13: Investment Regressions: Controlling for CEO Tenure(1) (2) (3) (4) (5) (6) (7) (8) (9)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsCEO stake quintile 0.0591∗∗∗ 0.0678∗∗∗ 0.0573∗∗∗ 0.0548∗∗∗ 0.0701∗∗∗ 0.0597∗∗∗ 0.0613∗∗∗ 0.0665∗∗∗ 0.0564∗∗∗

(0.0150) (0.0147) (0.0147) (0.0147) (0.0144) (0.0145) (0.0142) (0.0143) (0.0144)

CEO stake X JLN uncertainty -0.0167∗ -0.0261∗∗∗ -0.0276∗∗∗(0.00895) (0.00894) (0.00942)

JLN uncertainty -0.0796∗∗ 0.0557∗∗(0.0389) (0.0247)

CEO stake X VIX -0.0267∗∗∗ -0.0276∗∗∗ -0.0274∗∗∗(0.00843) (0.00810) (0.00804)

VIX 0.0182 0.0485∗∗(0.0261) (0.0220)

CEO stake X output gap 0.00132 -0.00655 -0.00575(0.0113) (0.0117) (0.0114)

Output gap 0.134∗∗∗ -0.0165(0.0271) (0.0276)

CEO tenure 0.00156 0.00289 0.00342 0.00100 0.00263 0.00323 0.00122 0.00298 0.00352(0.00284) (0.00282) (0.00279) (0.00278) (0.00274) (0.00272) (0.00278) (0.00275) (0.00273)

CEO tenure X JLN uncertainty -0.000263 0.000935 0.00146(0.00141) (0.00139) (0.00141)

CEO tenure X VIX 0.00150 0.00174 0.00184∗(0.00112) (0.00109) (0.00110)

CEO tenure X output gap 0.000900 0.000874 0.000793(0.00172) (0.00174) (0.00173)

Time trend No Yes No No Yes No No Yes No

Time FE No No Yes No No Yes No No Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes YesObservations 77428 77428 77428 80298 80298 80298 80298 80298 80298R2 0.292 0.309 0.319 0.288 0.309 0.320 0.294 0.309 0.319

45

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Execucomp firms from1992-2013. CEO tenure is measured as the number of years since the current CEO became CEO. CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated as described in the text in Section 2.1. Output gap, JLN uncertainty, and VIX time-series variables are all normalized to have mean zero and standarddeviation one during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015).VIX is the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage;contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthly stock returns for past 60 months. Time trend is a linear timetrend in years. Time fixed effects are year-quarter fixed effects. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

46

Appendix A: Real options model with risk aversion

I present a simple real options model of firm investment with a risk-averse owner. The set-up is asin Hugonnier and Morellec (2012).

A firm has one potential project to invest in, which produces stochastic cash flows dX =µXdt + σXdz and requires an investment I. The manager of the firm can choose to make thisirreversible investment at any time t. When the firm does not invest in the risky project, it investsin the riskless asset, with return r. The firm begins with cash-on-hand I. The manager whoowns the cash flows to the firm has a subjective discount rate of ρ, and is also risk-averse with aconstant relative risk aversion utility over flow payments Xs, where γ is the parameter of relativerisk aversion, so U(Xs) = X1−γ

s1−γ .

I do not model managers as having differing fractional ownership of the firm’s cash flows. Rather,the model allows for differences in managerial risk aversion γ, which is analogous to a frameworkin which managers have differing fractional ownership levels but similar risk aversion.

The total value of the firm T (X) is composed of the “base value” of the firm – investing I in theriskless asset in perpetuity, U(rI)/ρ – along with the option value of investing in the risky project,denoted F (X). The option value can be expressed as

F (X) = maxτ∈S

EXˆ ∞τ

e−ρs (U(Xs)− U(rI)) ds, (6)

where τ is the optimal investment time, which can be rewritten in Bellman equation form as

ρF (X, t)dt = max {EX [dF (X)]} . (7)

Applying Ito’s Lemma, we re-express

dF (X) = ∂F

∂tdt+ ∂F

∂XdX + 1

2∂2F

∂X2σ2X2dt, (8)

and then substitute dF (X) into equation (7) to derive the following ordinary differential equation,

12F′′(X)σ2X2 + µXF ′(X)− ρF (X) = 0, (9)

which has a standard, well-known general solution.One can then use common dynamic programming arguments to solve for the optimal investment

threshold X∗, the level of cash at which the manager decides to invest in the risky project. First,define V (X) as the intrinsic value of the investment, i.e. the value of the project if the option toinvest is exercised immediately. We can express V (X) at time t as:

V (Xt) = Eˆ ∞t

e−ρs (U(Xs)− U(rI)) ds

=ˆ ∞t

e−ρsE [U(Xs)] ds−U(rI)ρ

, (10)

47

where U(Xs) = X1−γs

1−γ . So E [U(Xs)] = 11−γE

[X1−γs

]. We re-apply Ito’s Lemma to re-express

E[X1−γs

]. After some algebraic manipulation, we can use the new expression for E

[X1−γs

]to

obtain41

V (Xt) =ˆ ∞

0e−ρse(1−γ)(µ− 1

2γσ2)sX

1−γt

1− γ ds−U(rI)ρ

= X1−γt

(1− γ)[ρ− (1− γ)

(µ− 1

2γσ2)] − U(rI)

ρ. (11)

Second, we use “smooth-pasting” and “value-matching” boundary conditions, along with thefact that the option value of the project is 0 if project cash flows are 0, to obtain a particular solutionto the ordinary differential equation (9). The value-matching condition is F (X∗) = V (X∗), becauseat the investment threshold X∗ the option value is equal to the intrinsic value. The smooth-pastingcondition is F ′ (X∗) = V ′ (X∗), and the final boundary condition is F (0) = 0.

Combining these boundary conditions with the expression V (X) and the general solution forF (X) obtained from the ordinary differential equation (9) allows us to solve for the investmentthreshold, X∗ and the value function F (X), yielding:

X∗ =[(

β1β1 + γ − 1

)(κ

ρ

)]1/(1−γ)rI

F (X) =[X∗1−γ

(1− γ)κ −U(rI)ρ

] [X

X∗

]β1

, (12)

where κ = ρ − (1− γ)(µ− 1

2γσ2)

and β1 > 1 is the positive root to the quadratic equationσ2

2 β (β − 1) + µβ − ρ = 0.There are two key predictions to this model. First, there is the standard real options prediction

that investment decreases as uncertainty increases, i.e. ∂X∗

∂σ > 0. Analytically, we have

∂X∗

∂σ2 = 11− γX

∗γ(rI)1−γ[κ

ρ

(− 1− γ

(β1 + γ − 1)2∂β1∂σ2

)+( 1

2ρ

) (1− γ) γβ1β1 + γ − 1

]. (13)

In the case when γ > 1, the term outside the square brackets is negative, and both terms insidethe square brackets are negative as ∂β1

∂σ2 < 0 and hence the square bracketed term is negative aswell. Conversely, when γ < 1 the term outside the square bracket is positive and both termsinside the square brackets are positive. Hence ∂X∗

∂σ2 > 0, so the well-known real options resultthat investment decreases when uncertainty or volatility increases holds when decision-makers arerisk-averse. As the investment threshold increases for the single project, in a firm with multipleprojects and thresholds, the total amount of investment will decrease.

41For the first term of V (X) to be finite, a necessary condition is that ρ− (1 − γ)(µ− 1

2γσ2) > 0. This condition

is innocuous, and is analogous to the requirement in the non-stochastic infinite-horizon dividend growth model thatthe difference between the discount and the growth rate of dividends be positive. Here, the risk-adjusted growth rateof dividends is used instead.

48

The second prediction is that firms with more risk-averse managers decrease investment more,i.e.∂2X∗

∂σ∂γ > 0. More risk-averse managers find the possibility of the downside to be more costly thanless risk-averse managers, and hence when uncertainty – and the probability that bad states of theworld will be realized – increases, more risk-averse managers decrease investment more.

49

Appendix B: Additional tables

Table B1: First Stage regressions of CEO stake and CEO stake×uncertainty, on exogenous CEO departure(1) (2) (3) (4)

CEO stake CEO st. X Unc. CEO stake CEO st. X Unc. CEO stake CEO st. X Unc. CEO stake CEO st. X Unc.Panel A: JLN Uncertainty

Recent CEO (exogenous CEO depart) -0.137∗∗∗ 0.00730 -0.140∗∗∗ 0.00698 -0.0821∗∗∗ 0.0248∗ -0.0889∗∗∗ 0.0237∗(0.0125) (0.00689) (0.0125) (0.00697) (0.0134) (0.0135) (0.0132) (0.0134)

Recent CEO X JLN uncertainty 0.00156 -0.171∗∗∗ 0.00117 -0.171∗∗∗ -0.00666 -0.170∗∗∗ -0.00611 -0.170∗∗∗(0.00754) (0.0163) (0.00733) (0.0163) (0.00702) (0.0179) (0.00705) (0.0179)

JLN uncertainty 0.00587 0.172∗∗∗ 0.0222∗ 0.173∗∗∗ 0.0000931 0.188∗∗∗ 0.0196∗∗ 0.191∗∗∗(0.0101) (0.0234) (0.0117) (0.0237) (0.00757) (0.0234) (0.00820) (0.0234)

Kleibergen-Paap F-stat 59.14 61.41 17.80 21.57

N 84084 84084 84084 84084 84627 84627 84627 84627(5) (6) (7) (8)

Panel B: VIX interaction

Recent CEO (exogenous CEO depart) -0.136∗∗∗ 0.00115 -0.140∗∗∗ 0.00171 -0.0793∗∗∗ 0.0134 -0.0870∗∗∗ 0.0128(0.0125) (0.00680) (0.0126) (0.00675) (0.0133) (0.0137) (0.0131) (0.0133)

Recent CEO X VIX -0.00292 -0.162∗∗∗ -0.00262 -0.162∗∗∗ -0.00607 -0.163∗∗∗ -0.00548 -0.163∗∗∗(0.00560) (0.0124) (0.00563) (0.0124) (0.00440) (0.0131) (0.00450) (0.0131)

VIX 0.00481 0.172∗∗∗ 0.0133 0.171∗∗∗ -0.00208 0.177∗∗∗ 0.00621 0.177∗∗∗(0.00859) (0.0178) (0.00933) (0.0179) (0.00582) (0.0188) (0.00580) (0.0188)

Kleibergen-Paap F-stat 55.87 57.89 17.38 21.70

N 87295 87295 87295 87295 87864 87864 87864 87864Controls Yes Yes Yes Yes Yes Yes Yes Yes

Controls X uncertainty Yes Yes Yes Yes Yes Yes Yes Yes

Time trend No No Yes Yes No No Yes YesFixed effects Industry Industry Industry Industry Firm Firm Firm Firm

50

Notes: The outcome variable in these regressions is lagged fiscal year CEO ownership stake in percent, calculated as described in the text in Section 2.1, orCEO ownership stake×uncertainty, where uncertainty is JLN uncertainty in Panel A and VIX in Panel B. The sample is Execucomp firms from 1992-2013. JLNuncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterly average of implied volatility ofS&P 500 index options. JLN uncertainty and VIX time-series variables are all normalized to have mean zero and standard deviation one during the sample timeperiod. Recent CEO is a lagged fiscal year indicator for whether the CEO is a new CEO subsequent to an exogenous CEO departure within the past five years.Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standarddeviation of monthly stock returns for past 60 months. “Controls X uncertainty” indicates that additional controls consisting of all controls, interacted with theuncertainty time-series variables (either JLN uncertainty or VIX), are also included. Time trend is a linear time trend in years. Standard errors double clustered byfirm and quarter. Kleibergen-Paap F-statistic is a measure of strength of the first-stage regressions with multiple endogenous regressors, accounting for non-i.i.d.residuals. It is analogous to the Donald-Cragg F-statistic when residuals are i.i.d., and has identical critical values. * p < 0.10, ** p < 0.05, *** p < 0.01

51

Table B2: Other Outcomes, Firm Fixed Effects: SG&A Expenses, Total Payouts, and Asset Sales(1) (2) (3) (4) (5) (6)

SGA/Assets SGA/Assets Payouts/Assets Payouts/Assets Asset sales Asset salesCEO stake quintile -0.0968∗∗∗ -0.107∗∗∗ -0.00471 -0.00360 -0.0286 -0.0291

(0.0303) (0.0302) (0.0115) (0.0113) (0.0272) (0.0262)

CEO stake X JLN uncertainty -0.0605∗∗∗ 0.0234∗∗∗ 0.0314∗∗(0.0181) (0.00600) (0.0132)

JLN uncertainty 0.148∗∗∗ -0.0956∗∗∗ -0.0859∗∗(0.0492) (0.0219) (0.0388)

CEO stake X VIX -0.0304∗∗∗ 0.00110 0.0274∗∗(0.0118) (0.00679) (0.0118)

VIX 0.0693∗ -0.0145 -0.0916∗∗∗(0.0366) (0.0257) (0.0349)

Controls Yes Yes Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes Yes Yes

Time trend Yes Yes Yes Yes Yes YesObservations 76473 79280 76558 79342 77741 80536

Notes: The outcome variables in these regressions are: selling, general, and administrative (SG&A) expenses divided by lagged assets, in percent, in Columns 1and 2, total equity payouts (a sum of dividends paid and repurchases) divided by lagged assets, in percent, in Columns 3 and 4, and total asset sales divided bylagged assets, in percent, in Columns 5 and 6. CEO stake quintile is the lagged fiscal year quintile of CEO stake, calculated as described in the text in Section2.1. The sample is Execucomp firms from 1992-2013. JLN uncertainty and VIX time-series variables are all normalized to have mean zero and standard deviationone during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIXis the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage;contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthly stock returns for past 60 months. Time trend is a linear timetrend in years. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

52

Table B3: Investment Regressions, Splitting Sample by High versus Low Institutional Ownership(1) (2) (3) (4) (5) (6) (7) (8)

Low inst own High inst own Low inst High inst Low inst High inst Low inst High instCEO stake quintile 0.0650∗∗∗ 0.0743∗∗∗ 0.0635∗∗∗ 0.0548∗∗∗ 0.0723∗∗∗ 0.0730∗∗∗ 0.0689∗∗∗ 0.0516∗∗∗

(0.0163) (0.0171) (0.0148) (0.0130) (0.0164) (0.0163) (0.0146) (0.0122)

CEO stake X JLN uncertainty -0.0318∗∗∗ -0.0117 -0.0286∗∗∗ -0.00766(0.0118) (0.00866) (0.00984) (0.00799)

JLN uncertainty 0.0796∗∗ 0.0370 0.0709∗∗ 0.0162(0.0336) (0.0260) (0.0306) (0.0238)

CEO stake X VIX -0.0288∗∗∗ -0.0118∗ -0.0256∗∗∗ -0.00952(0.0105) (0.00653) (0.00877) (0.00579)

VIX 0.0748∗∗ 0.0314 0.0571∗∗ 0.0272(0.0306) (0.0229) (0.0283) (0.0172)

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Time trend Yes Yes Yes Yes Yes Yes Yes YesFixed effects Industry Industry Firm Firm Industry Industry Firm FirmN 38166 40737 38412 40796 38783 43015 39040 43086

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Execucomp firms from1992-2013. Institutional ownership data is from the Thomson Reuters 13F database, and low institutional ownership are firm-quarters with lagged quarterinstitutional ownership below sample median (vice versa for high institutional ownership). CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated as described in the text in Section 2.1. JLN uncertainty and VIX time-series variables are all normalized to have mean zero and standard deviationone during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIXis the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage;contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthly stock returns for past 60 months. Time trend is a linear timetrend in years. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table B4: Investment Regressions: CEO Stake Adjusted by Options Delta(1) (2) (3) (4) (5) (6) (7) (8) (9)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsCEO stake (OA), quint 0.0620∗∗∗ 0.0715∗∗∗ 0.0624∗∗∗ 0.0566∗∗∗ 0.0730∗∗∗ 0.0642∗∗∗ 0.0622∗∗∗ 0.0707∗∗∗ 0.0617∗∗∗

(0.0130) (0.0130) (0.0131) (0.0129) (0.0127) (0.0129) (0.0125) (0.0126) (0.0128)

CEO stake (OA) X JLN uncertainty -0.0166∗∗ -0.0234∗∗∗ -0.0241∗∗∗(0.00790) (0.00776) (0.00801)

JLN uncertainty -0.0682∗ 0.0815∗∗∗(0.0405) (0.0281)

CEO stake (OA) X VIX -0.0216∗∗∗ -0.0223∗∗∗ -0.0226∗∗∗(0.00714) (0.00696) (0.00685)

VIX 0.0418 0.0760∗∗∗(0.0286) (0.0251)

CEO stake (OA) X output gap 0.00649 -0.00106 -0.000441(0.00919) (0.00937) (0.00921)

Output gap 0.131∗∗∗ -0.0101(0.0332) (0.0331)

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Time trend No Yes No No Yes No No Yes No

Time FE No No Yes No No Yes No No YesObservations 80869 80869 80869 83770 83770 83770 83770 83770 83770R2 0.295 0.311 0.321 0.291 0.311 0.322 0.297 0.311 0.321

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent. The sample is Execucomp firms from1992-2013. CEO options-adjusted stake quintile is the lagged fiscal year quintile of CEO stake, adjusted for the delta of CEO options ownership, calculated asdescribed in the text in Section 4.3. Output gap, JLN uncertainty, and VIX time-series variables are all normalized to have mean zero and standard deviationone during the sample time period. JLN uncertainty is the quarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIXis the quarterly average of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheet cash level, size, leverage;contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation of monthly stock returns for past 60 months. Time trend is a linear timetrend in years. Time fixed effects are year-quarter fixed effects. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table B5: Different Definitions of CEO or Top Executive Ownership Stakes(1) (2) (3) (4) (5) (6)

Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/AssetsCEO stake quint (salary denom.) 0.0692∗∗∗ 0.0709∗∗∗ 0.0690∗∗∗

(0.0137) (0.0134) (0.0133)

CEO stake X JLN uncertainty -0.0170∗∗(0.00817)

CEO stake X VIX -0.0205∗∗∗(0.00725)

CEO stake X output gap -0.00667(0.00939)

Top 5 exec stake quint 0.0641∗∗∗ 0.0646∗∗∗ 0.0622∗∗∗(0.0135) (0.0132) (0.0131)

Top exec stake X JLN uncertainty -0.0181∗∗(0.00756)

Top exec stake X VIX -0.0170∗∗∗(0.00606)

Top exec stake X output gap 0.00501(0.00915)

Controls Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

Time FE Yes Yes Yes Yes Yes YesObservations 81254 84158 84158 61201 63702 63702R2 0.321 0.322 0.322 0.327 0.327 0.327

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent.The sample is Execucomp firms from 1992-2013. CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated with CEO salary in the denominator. Top 5 executive stake quintile is the lagged fiscal year quintile ofownership stake, calculated for all Top 5 executives, including the CEO, analogous to the stake measure calculcatedfor CEOs described in the text in section 2.1. Output gap, JLN uncertainty, and VIX time-series variables are allnormalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is thequarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterlyaverage of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheetcash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation ofmonthly stock returns for past 60 months. Time fixed effects are year-quarter fixed effects. Standard errors doubleclustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table B6: Impact of CEO Stake on Investment: Including both Macroeconomic Cycleand Uncertainty Interactions

(1) (2) (3) (4) (5) (6)Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets Inv/Assets

CEO stake quintile 0.0683∗∗∗ 0.0722∗∗∗ 0.0634∗∗∗ 0.0663∗∗∗ 0.0736∗∗∗ 0.0648∗∗∗(0.0126) (0.0126) (0.0129) (0.0125) (0.0124) (0.0127)

CEO stake X JLN uncertainty -0.0184∗∗ -0.0264∗∗∗ -0.0258∗∗∗(0.00807) (0.00814) (0.00834)

CEO stake X VIX -0.0238∗∗∗ -0.0237∗∗∗ -0.0233∗∗∗(0.00749) (0.00697) (0.00696)

CEO stake X output gap -0.00117 -0.0102 -0.00924 0.00147 -0.00615 -0.00547(0.00945) (0.00922) (0.00921) (0.00953) (0.00957) (0.00953)

JLN uncertainty -0.0423 0.0639∗∗∗(0.0404) (0.0241)

VIX 0.0545∗∗ 0.0550∗∗(0.0255) (0.0223)

Output gap 0.123∗∗∗ 0.00611 0.149∗∗∗ 0.00140(0.0285) (0.0265) (0.0258) (0.0269)

Controls Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

Time trend No Yes No No Yes No

Time FE No No Yes No No YesObservations 81254 81254 81254 84158 84158 84158R2 0.298 0.311 0.321 0.297 0.311 0.321

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent.The sample is Execucomp firms from 1992-2013. CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated as described in the text in Section 2.1. Output gap, JLN uncertainty, and VIX time-series variables areall normalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is thequarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterlyaverage of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheetcash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standard deviationof monthly stock returns for past 60 months. Time trend is a linear time trend in years. Time fixed effects areyear-quarter fixed effects. Standard errors double clustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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Table B7: Impact of CEO Stake on Investment, Using Industry-Quarter Fixed Effects(1) (2) (3)

Inv/Assets Inv/Assets Inv/AssetsCEO stake quintile 0.0534∗∗∗ 0.0553∗∗∗ 0.0530∗∗∗

(0.0126) (0.0124) (0.0122)

CEO stake X JLN uncertainty -0.0215∗∗∗(0.00751)

CEO stake X VIX -0.0202∗∗∗(0.00617)

CEO stake X output gap -0.000662(0.00922)

Controls Yes Yes Yes

Ind X Quarter FE Yes Yes Yes

Time FE Yes Yes YesObservations 81254 84158 84158R2 0.361 0.361 0.361

Notes: The outcome variable in these regressions is quarterly capital expenditures divided by lagged assets, in percent.The sample is Execucomp firms from 1992-2013. CEO stake quintile is the lagged fiscal year quintile of CEO stake,calculated as described in the text in Section 2.1. Output gap, JLN uncertainty, and VIX time-series variables areall normalized to have mean zero and standard deviation one during the sample time period. JLN uncertainty is thequarterly average of the 3-month uncertainty measure as calculated by Jurado et al. (2015). VIX is the quarterlyaverage of implied volatility of S&P 500 index options. Controls include lagged values of Tobin’s Q, balance sheetcash level, size, leverage; contemporaneous values of cash flow, annual sales growth, CEO age, standard deviation ofmonthly stock returns for past 60 months. Time fixed effects are year-quarter fixed effects. Standard errors doubleclustered by firm and quarter. * p < 0.10, ** p < 0.05, *** p < 0.01

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