1

Unconventional monetary policy and the stock market’s reaction to

Federal Reserve policy actions

Abstract: We examine the change in the effect of Federal Reserve’s policy actions on stock returns after the Fed started to use unconventional policy actions. We find that the response of stock returns to monetary policy actions are almost seven times higher after the federal funds rate hit the zero lower bound. We conduct additional analysis to examine the underlying causes of the increase in the impact of monetary policy actions of stock returns. We show that investors rebalance their portfolios towards equity after selling Treasury securities to the Federal Reserve during Large Scale Asset Purchases.

JEL Codes: E43; E44; E52; G12

Keywords: Monetary policy; Stock market; Zero lower bound; LSAP

2

“LSAPs also appear to have boosted stock prices, presumably both by lowering discount rates and by improving the economic outlook; it is probably not a coincidence that the sustained recovery in U.S. equity prices began in March 2009, shortly after the FOMC's decision to greatly expand securities purchases. This effect is potentially important because stock values affect both consumption and investment decisions.” (Ben S. Bernanke, Jackson Hole, Wyoming, August 31, 2012)

1 Introduction

Before the financial crisis of 2007-09, the Federal Reserve (Fed) was using the federal funds

rate as the main policy instrument. The Fed lowered the federal funds rate to boost aggregate

demand and provide economic stimulus. However, since the end of 2008 the Federal Open

Market Committee (FOMC) sets a near-zero target range for the federal funds rate. After hitting

the zero lower bound of the federal funds rate the Fed started to use unconventional monetary

policy tools, namely forward policy guidance1 and large-scale asset purchases (LSAPs), to

support economic recovery. During LSAPs, the Fed purchased longer-term securities with the

goal of putting downward pressure on longer-term interest rates and promote economic activity

by making financial conditions more accommodative. In this paper, we examine the change in

the sensitivity of stock returns to monetary policy actions after the Fed started to use these

unconventional policies.

Many studies in the economics and finance literature examine the relationship between the

stock market and monetary policy. Understanding this relationship is essential for both

monetary policy makers and financial market participants. Bernanke and Kuttner (2005) state

that “The most direct and immediate effects of monetary policy actions, such as changes in the

Federal funds rate, are on the financial markets; … Understanding the links between monetary

policy and asset prices is thus crucially important for understanding the policy transmission

mechanism.” (Page 1221). Monetary policy makers need this information to identify the real

effects of monetary policy actions, and financial market participants require a precise estimate

of the reaction of stock prices to policy actions to design effective investment decisions. Gali and

Gambetti (2014) argue that the recent financial crisis challenged the consensus of the

interaction between monetary policy and financial markets. Accordingly, the investigation of the

structural change in the impact of the actions of the Fed on financial markets has the potential to

provide critical information for both policy makers and investors.

1 In the September 2012 statement, the FOMC states that “the Committee also decided today to keep the target

range for the federal funds rate at 0 to 1/4 percent and currently anticipates that exceptionally low levels for the federal funds rate are likely to be warranted at least through mid-2015.”

3

The Fed used the unconventional policy actions of forward-guidance and LSAPs after the

federal funds rate reached its zero lower bound. The Fed started to conduct forward-guidance

by making commitments about the future path of the federal funds rate in late 2008. As

summarized in Table 2 of Engen et al (2015), there has been nine FOMC statements which

mention that the exceptionally low levels of the federal funds rate will be warranted in the

future. Starting with the August 2011 statement, the FOMC provided more explicit information

about future policy outlook. Williams (2012) states that forward-guidance was successful in

shifting market expectations about the future path of the federal funds rate. The Fed completed

three LSAPs by purchasing longer-term Treasury and mortgage-backed securities. When the

recent LSAP program ended in October 2014, the Fed’s balance rose from $500 billion to over $4

trillion.

One of the major challenges of this empirical analysis is measurement of the overall stance

of monetary policy. Studies such as Bernanke and Kuttner (2005), Bjorland and Leitemo (2009)

and Gali and Gambetti (2014) use the federal funds rate as a measure of monetary policy when

examining the effect of monetary policy shocks on stock returns.2 On the other hand, since the

federal funds rate was set to the zero lower bound, implementation of unconventional monetary

policy makes it difficult to assess the stance of monetary policy. One possible proxy for this

stance can be the size of the Fed’s balance sheet. Using this proxy, several financial services

companies like the Raymond James Financial3 drew attention to the correlation between S&P

500 index and the total assets of the Fed to examine the relationship between large-scale asset

purchases and stock prices. Figure 1 below displays the expansion of the Fed’s Balance Sheet

though LSAPs and the S&P 500 index.

2 Bernanke and Kuttner (2005) study differs from the other studies mentioned above in a way that use an event-

study approach, based on daily changes observed on monetary policy decision dates, to uncover the response

of stock prices to unanticipated changes in the federal funds rate 3 Technical Strategy Team - Technical Chart Book, May 26 2015.

4

Figure 1: Total Assets of the Federal Reserve and the S&P 500 Index

Figure 1 shows that S&P 500 index climbed to 2000 in 2015 from less than 700 in 2009.

Moreover, the figure presents that there is almost one-to-one relationship between the stock

market and expansion of FED’s balance sheet through LSAPs. Figure 1 calls for further analysis

of this relationship between stock returns and unconventional policy actions of FED.

Using the Fed’s balance sheet as a proxy for monetary policy is not problem-free. First of all,

as stated by Wright (2012): “with forward-looking financial markets, one would expect a policy

of asset purchases to impact asset prices not at the time that the purchases are actually made but

rather at the time that investors learn that they will take place.” (Page 448) Since LSAPs are

announced ahead of time, investors form expectations about them and act accordingly. In

addition, the Fed uses other unconventional monetary policy actions like forward guidance to

guide the expectations of the public. Secondly, we need a measure for Fed’s unconventional

monetary policy actions that is consistent with the federal funds rate to be able to assess

whether there is a structural change in the reaction of stock returns to policy shocks. We try to

overcome both problems by using ‘the shadow interest rate’ constructed by Wu and Xia (2015).

This rate is equal to the federal funds rate before 2009, and provide a similar composite measure

for the monetary policy for the following era. Wu and Xia (2015) construct a term structure

Correlation: 0.98 after LSAP

50

01

00

01

50

02

00

0

S&

P 5

00

10

00

20

00

30

00

40

00

50

00

To

tal A

sse

ts o

f th

e F

ed

(b

illi

on

$)

2005m1 2010m1 2015m1daten

Fed Total Assets S&P 500

The Fed's Balance Sheet and the S&P 500

5

model which produces a “shadow rate” interacting with macroeconomic variables similarly as

the federal funds rate.4 The shadow rate is derived using a linear function of latent variables

which follow VAR (1) processes. The latent factors and the shadow rate are estimated using one-

month forward rates.

Several methodological issues also present challenges in estimating the reaction of stock

returns to monetary policy. First issue is the endogeneity problem caused by the endogeneity of

monetary policy actions and stock returns. Second, other variables about the macroeconomic

condition of the economy and related news contemporaneously effect monetary policy and stock

returns. Hence, identification of the “responsiveness of asset prices” is problematic under

previously used methods as stated by Rigobon and Sack (2004). In this paper, we follow the

suggestion of Rigobon and Sack (2004) and implement a methodology which “identifies the

response of asset prices based on the heteroskedasticity of monetary policy shocks.” (Page

1554). Our methodology follows Lewbel (2012), which serves to identify structural parameters

in regression models with endogenous or mismeasured regressors.

Our results indicate that the impact of monetary policy on stock returns increases

immensely- almost seven times higher-since the start of the unconventional policy actions at the

end of 2008. To uncover the reasons behind this dramatic change, we examine the effect of

LSAPs on investor portfolios. We find that during LSAPs investors sell Treasury securities to the

Fed and rebalance their portfolios towards riskier assets by buying stocks. This result suggests

that the Fed contributed significantly to the surging stock markets through this portfolio

rebalancing effect. Hence, in line with the claim of Bernanke (2012), our results suggest that it is

really not a coincidence that the substantial rise in U.S. equity prices began after the FOMC's

decision to greatly expand securities purchases.

These results add to the literature which study the economic effects of LSAPs. Gagnon

(2001) show that LSAP announcements decreased U.S. long-term yields using an event study

approach. Hamilton and Wu (2012) develop an affine term structure model to assess the effect

of LSAP on the maturity structure of U.S. debt. Their results suggest that LSAPs were effective at

lowering long-term yields at the zero lower bound. On the contrary, Stroebel and Taylor (2012)

4 Lombardi and Zhu (2014) and Krippner (2012) calculate alternative shadow policy rates than Wu and Xia

(2015)’s for the US economy. We prefer to use the Wu and Xia (2015) shadow rate calculations as presented

by the Federal Reserve Bank of Atlanta for two reasons. First, the Wu and Xia (2015) shadow rate is much

smoother compared to its alternatives. Lombardi and Zhu (2014) rate has very high variation. It switches

between negative and positive values over time. Second, the Wu and Xia (2015) shadow rate does not have

extreme values. For example, Krippner (2012) rate drops to below -9% in August 2011 showing an extremely

loose monetary policy stance.

6

find the Federal Reserve’s mortgage-backed securities purchase program did not have a

significant impact on mortgage spreads. Wu and Xia (2015) implement a VAR analysis and

conclude that unconventional policy actions of the Fed succeeded in decreasing the

unemployment rate. Besides the domestic effects of LSAPs some papers examine their

international effects. Neely (2013) and Neely and Bauer (2014) show that LSAP program has

strong effects on international bond yields. In addition, Neely (2015) presents that

unconventional monetary policy announcements of the Fed reduced the spot value of the dollar.

The primary contribution of this paper is the investigation of the effect of Fed’s

unconventional policy actions on the stock markets. We also evaluate the role of the LSAPs on

the surge of the stock markets since 2009. We conclude that the Fed significantly contributed to

this raise. The effect of policy actions of the Fed is seven times higher compared to pre-LSAP

period. Secondarily, we analyze the dynamics behind this consequential change in the Fed’s

impact on the stock markets. We find that the LSAPs prompt investors to rebalance their

portfolios towards stocks.

The remainder of the paper is organized as follows. In Section 2, we describe the data and

methodology. Section 3 presents the empirical results. Section 4 evaluates the robustness of the

empirical results. Section 5 investigates the underlying dynamics and section 6 concludes.

2. Data and Methodology

As stated by Rigobon and Sack (2004) and Rosa (2009), several complications arise when

estimating the reaction of stock returns to monetary policy actions. First, stock returns and

monetary policy actions might simultaneously affect each other causing the endogeneity

problem. Second, both stock returns and monetary policy actions are influenced by other

variables, such as news about the macroeconomic outlook. The omitted-variables bias occurs

when those variables are not included in the empirical model. The following equations from

Rigobon and Sack (2004) present both of these potential biases:

∆𝑖𝑡 = 𝛽Δ𝑠𝑡 + 𝛾𝑍𝑡 + 𝜀𝑡 (1)

∆𝑠𝑡 = 𝛼Δ𝑖𝑡 + 𝜌𝑍𝑡 + 𝜂𝑡 (2)

where 𝑖𝑡 is the monetary policy interest rate and 𝑠𝑡 is the stock price index. 𝑍𝑡 contains the

variables which affect both the change in the interest rate and change in the stock price index.

We examine whether the parameter 𝛼, the measure of the reaction of stock returns to change in

7

the policy actions of the Federal Reserve, differs since the Federal Reserve started the LSAP

programs.

In this paper, we implement the recently developed instrumental variables methodology of

Lewbel (2012) (LW). LW proposes a method which identifies structural parameters when valid

instrumental variables do not exist. The methodology provides an unbiased and consistent

estimate of the parameters when the regression model contains endogenous or mismeasured

regressors or when the model suffers from the omitted-variables bias. Monte Carlo study results

and numerous empirical applications presented in LW show that the estimator works very well

compared to two-stage least squares and generalized method of moments.

We briefly describe the methodology of LW using equations (1) and (2) below.5

Endogeneity between 𝑖𝑡 and 𝑠𝑡 causes a biased least square estimation of 𝛼, the coefficient which

represents the impact of monetary policy on stock returns. The common technique used in the

literature to obtain an unbiased estimate of 𝛼 is to use an instrumental variable (IV) technique.

LW uses the heteroscedasticity of the errors to construct the required instruments. Then, these

newly constructed instruments are employed for the IV estimation of 𝛼 using GMM. The

construction of the IVs can be described as the following. Suppose a set of exogenous variable(s)

are denoted with X. In the first stage estimation, the endogenous variable, Δ𝑖𝑡, is regressed on

the X vector and the residuals, 𝑒, of this regression are obtained. If these residuals are

heteroscedastic with respect to X, which is tested through the standard Breusch - Pagan type

test, then they are used to construct the variable (𝑋𝑖 − 𝑋𝑖) ∗ 𝑒𝑖, where 𝑋𝑖 is the mean of 𝑋𝑖 . The

variable (𝑋𝑖 − 𝑋𝑖) ∗ 𝑒𝑖, is employed as the standard instrumental variable in the second-stage

regression in estimating Equation (2), which can be estimated by GMM. Intuitively, even though

the error terms in equations (1) and (2), ɛ and 𝜂, are correlated, the constructed IV variable

(𝑋𝑖 − 𝑋𝑖) ∗ 𝑒𝑖 will be uncorrelated with the error term 𝜂. Yet, the variable (𝑋𝑖 − 𝑋𝑖) ∗ 𝑒𝑖 will be

correlated with Δ𝑖𝑡.

LW’s method has several advantages over Rigobon and Sack (2004). First of all, there is no

need to look for variance shifts in the system. Second, additional tests like the “Sargan-Hansen”

tests of orthogonality conditions can be calculated which will provide information about the

validity of the estimation. Table 1 presents the Sargan-Hansen statistics which confirms the

validity of the IVs used in the GMM estimation.

5 We provide a brief description since Lewbel (2012) explains the methodology in detail. We avoid repetition in

the paper and refer to the excellent presentation of Lewbel (2012) for further details about the methodology.

8

To estimate the effect of monetary policy on stock returns, we need an empirical asset

pricing model. We follow Fama and French (2015) and add inflation, output gap and shadow

interest rate to the model of Fama and French (1993). As implemented in Rigobon and Sack

(2003), we use inflation and output gap as additional variables since the reaction function of the

Fed contains these variables6. Specifically, we use the following asset-pricing regression model:

𝑅𝑡 − 𝑅𝐹𝑡 = 𝛽0 + 𝛽1𝑠𝑚𝑏𝑡 + 𝛽2ℎ𝑚𝑙𝑡 + 𝛽3𝜋𝑡−1 + 𝛽4𝑔𝑎𝑝𝑡−1 + 𝛽5𝛥𝑖𝑡−1 + 𝛽6𝛥𝑖𝑡 + 𝜀𝑡

(3)

where 𝑅𝑡 − 𝑅𝐹𝑡 is the excess return of all NYSE, AMEX, and NASDAQ stocks over the one-month

Treasury bill rate, and 𝑖𝑡 is the Wu and Xia (2015)’s ‘shadow interest rate’ mentioned in the

Introduction section of this paper. We employ the first-difference of this variable as the measure

of policy actions of the Federal Reserve. This rate is identical with the federal funds (FED) rate

before the FED rate hits the zero lower bound, and also reflects the unconventional monetary

policy actions following this era. To isolate the effect of past monetary policy actions on stock

prices, the lag of 𝛥𝑖 is also used as a covariate.

Other explanatory variables in equation (1) are as follows. Inflation (𝜋) and output gap

(𝑔𝑎𝑝) are used to account for, respectively, the effects of changes in nominal prices and in

economic conditions on the stock prices.7

SMB and HML are two variables used to control for changes in market returns due to firm

specific risks. SMB stands for ‘Small [market capitalization] Minus Big’ and measures the historic

excess returns of small portfolios over big portfolios. HML stands for ‘High [book-to-market

ratio] Minus Low’ and measures the historic excess returns of value stocks over growth stocks.

These widely used financial factors are first employed by Fama and French (1993), who state

that “SMB and HML typically capture substantial time-series variation in stock returns.” We use

SMB and HML as control variables to take into account that historically, small firms and

distressed firms have lower stock prices to compensate investors for their risks.

We collect the data from the following databases: FRED database of the Federal Reserve

Bank of St. Louis, the Federal Reserve Bank of Atlanta web site and the web site of Kenneth

6 A large literature including Bernanke and Gertler (2001), Coibion and Gorodnichenko (2012) and Curdia et al.

(2015) use reaction functions with inflation and various measures of the output gap to describe the policy

reaction of the Fed. 7 These variables are also common factors of monetary policy interest rate rules. Hence, employing these

variables as regressors helps us to avoid the omitted variables problem presented in equations (1) and (2).

9

French8. We gather inflation and industrial production from the FRED database. We calculate the

output gap by extracting the cyclical component of industrial production using the Hodrick-

Prescott filter. The shadow interest rate is calculated by Wu and Xia (2015) and available at the

Federal Reserve Bank of Atlanta Economic Research web site.9 Finally, the excess return of stock

markets, SMB and HML are calculated by Kenneth French and available at his web site.

3. Empirical Results

We analyze whether the coefficient of the measure of monetary policy, 𝛽6, differs after the

federal funds rate hit the zero lower bound and the Federal Reserve started to conduct

unconventional policy actions. Figure 2 provides a first look at the shift in the relationship

between the financial markets and monetary policy.

Figure 2: Shadow Rate and the S&P 500 Index

Figure 2 presents the change in the correlation between the S&P 500 index and the shadow

interest rate. The shadow interest rate is identical with the federal funds rate before the zero

lower bound. Figure 2 displays the dramatic structural shift in the correlation between the S&P

8 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html 9 https://www.frbatlanta.org/cqer/research/shadow_rate.aspx.

Correlation: 0.64 Correlation: -0.93

50

01

00

01

50

02

00

0

S&

P 5

00

-4-2

02

46

Sh

ado

w I

nte

rest

Rat

e

2002m1 2004m1 2006m1 2008m1 2010m1 2012m1 2014m1 2016m1daten

Shadow Interest S&P 500

The Shadow Rate and the S&P 500

10

500 index and monetary policy actions. After the Fed started to use unconventional monetary

policy actions, the correlation became negative and very close to one. To uncover the true nature

of the reaction of the financial market to monetary policy, we use the empirical asset-pricing

model described in section 2. We estimate the parameters of the model before and after the zero

lower bound using the LW methodology. The estimation results are presented in table 1.

Table 1

Time

1979m8-2008m12 2009m1-2015m2

ΔShadow Interest Rate (t) -1.18 -7.24 (2.42)* (2.33)*

SMB 0.15 0.84 (1.32) (4.30)**

HML -0.65 0.58 (7.70)** (3.56)**

Output Gap (t-1) -0.20 0.11 (1.90) (0.69)

Inflation (t-1) -0.37 -1.22 (0.51) (0.74)

ΔShadow Interest Rate (t-1) 0.40 -1.25 (1.34) (0.41)

Constant 0.82 1.41 (2.42)* (2.90)**

R2 0.25 0.40 N 353 74

Hansen J Statistic P-value

0.93 0.92

1.78 0.78

Chow Test Statistic P-value

9.13 0

* p-value <0.05, ** p-value < 0.01.

Table 1 displays the dramatic change in the coefficient of the monetary policy indicator, the

shadow interest rate. Before 2009, the coefficient is significant with a value of -1.18. When the

Federal Reserve starts to use unconventional policy actions in 2009, the coefficient rises to -7.24.

This result shows that the effect of monetary policy actions on the stock market returns

increased almost sevenfold after the LSAP. Hansen J statistic test verifies the validity of the

instrumental variable specifications. We cannot reject the null hypothesis that the instruments

used in the estimation are valid with 92% and 78% p-values before and after 2009. The Chow

11

test strongly validates that the coefficients of the shadow interest rate are significantly different

for these time periods.

4. Robustness of the Results

The previous section analyzes the effect of monetary policy on the stock returns before and

after 2009. Even though the Chow structural stability test also confirms that the coefficient of

the shadow interest rate significantly differs before and after 2008:m12, a common criticism of

empirical structural change analysis is the possible endogeneity of the structural break date.

Hansen (1992) argues that “... the date of structural change may be selected by appeal to events

known a priori. ... it is essential that the researcher can argue that the events are selected

exogenously.” To make sure that the results of Table 1 are not subject to this criticism, we carry

out the Quandt Likelihood Ratio (QLR) test to identify the unknown break date statistically from

the data. Instead of analyzing potential breaks in some predetermined dates, the QLR test,

determines the structural break date by searching among all dates.

The QLR test, also known as the supremum test, is originally proposed by Quandt (1958,

1960). The distribution of this test, together with the tabulated critical values computed by

simulation, are given in Andrews (1993). The QLR test computes the Chow test statistics for

structural stability for each date in the data (except for some trimmed portion from both ends).

The date that maximizes the estimated Chow statistics is the most likely break point in the data.

In order to investigate the existence of breaks in the data, we use the following model:

𝑅𝑡 − 𝑅𝐹𝑡 = 𝛽 ∗ 𝑋′ + 𝛽1𝛥𝑖𝑡 + 𝛽1𝐷𝑖𝑡

∗ + 𝜀𝑡 , (4)

where X stands for other variables than the shadow interest rate used in equation (3), and

where 𝑖𝑡∗ is defined as

𝑖𝑡

∗ = {𝛥𝑖𝑡 𝑖𝑓 𝑡 ≥ 𝑗0 𝑖𝑓 𝑡 < 𝑗

for each date j for 0.1T ≤ j ≤0.9T, where T is the number of observations. 𝑖𝑡∗ is equal to 0 prior to

date j and equal to the interest rate following this date. Hence, using 𝑖𝑡∗ in equation (4) is simply

using the interest rate twice in the same regression whereas one of these rates is only piecewise

defined. If 𝑖𝑡 turns out to be significant but 𝑖𝑡∗ does not, we can conclude that the interest rate can

significantly explain the stock returns. Moreover, this effect does not change after period j. On

the other hand, if inclusion of 𝑖𝑡∗ improves the fit of the regression, we can conclude that, the

effect of the shadow interest rate on stock returns is subject to a structural change at time j.

12

Figure 3 below displays the results from estimating Eq. (4). Specifically, the figure shows

the QLR statistics for 𝛽1𝐷 at each date j defined above. The figure indicates that the QLR statistic is

below the 5 percent critical value except for 2009:m1. This result suggests that the effect of the

shadow interest rate, which we use as a proxy for monetary policy, on stock returns has two

different regimes; one before 2008m12; and one after 2009:m1.10 And notice that this is exactly

the date where Federal funds rate hits the zero lower bond.

Figure 3: QLR statistics of the structural break test

5. Understanding the Dynamics behind the Structural Change

Sections 3 and 4 present the significant and robust increase in the reaction of the stock

markets to the policy actions of the Federal Reserve after the start of unconventional policy

actions. This result brings up the question about the reasons behind this substantial structural

change. To investigate the underlying causes, we examine the portfolio rebalancing effect of the

Federal Reserve asset purchases by following the methodology of Carpenter et al. (2015).

10 We also tested for the presence of multiple breaks in the data by using Perron and Qu (2007)’s approach.

Critical value 5% (9.11)

Max QLR = 12.15, at 2009:m1

05

10

15

QL

R s

tati

stic

1983m1 1993m1 2003m1 2013m1

Break Statistics for Forecasts

13

Carpenter et al. (2015) investigates the types of investors that are selling financial assets to

the Federal Reserve under the LSAP program and changes in their portfolio holdings after these

sales. In this section, we extend the empirical analysis of Carpenter et al. (2015) by lengthening

their data period and focusing only on households since Carpenter et al. (2015) show that the

Federal Reserve is buying Treasury securities primarily from households (which includes hedge

funds and some of the other investor types)11. Additionally, in response to the decline in the

Fed’s assets, households will substitute toward alternative investment options. Carpenter et al.

(2015) show that key participants of the market rebalance their portfolios toward riskier assets

including equity. The portfolio balance model of Neely (2015) also indicates that “… a change in

the supply of an asset should affect its own expected return and those of assets whose returns

covary with it.” Hence, it is essential to find out whether households invest in the stock markets

(equity) using the liquidity supplied by the Federal Reserve thorough LSAPs. In other words, we

examine the impact LSAP on the Treasury holdings of households and the risky substitute equity

holdings.

We implement the empirical methodology of Carpenter et al. (2015) to identify the effect of

LSAPs on the portfolio holdings of the households. Namely, we estimate regression equation (5)

in which the dependent variable is change in the asset holdings of household investor type and

the key independent variable is the change in the Federal Reserve’s holdings of Treasury

securities. We also control for changes in the outstanding issuance of that security. The Federal

Reserve Board quarterly publishes these Flow of Funds variables and they are available at the

FRED data base. We conduct the empirical analysis using quarterly data starting in 2003:Q1 and

ending in 2015:Q1.12

∆𝐻𝐻 𝐴𝑠𝑠𝑒𝑡𝑡 = 𝛼 + 𝛽1∆𝐹𝑒𝑑 𝑇𝑟𝑒𝑎𝑠𝑢𝑟𝑦𝑡 + 𝛽2∆𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 𝐴𝑠𝑠𝑒𝑡𝑡 + 𝛽3∆𝐻𝐻 𝐴𝑠𝑠𝑒𝑡𝑡−1 + 𝜀𝑡 (5)

Table 2 displays the estimation results of regression equation (5). We are interested in the

coefficient of the change in the Federal Reserve’s holdings of Treasury securities, 𝛽1. 𝛽1

measures the effect of LSAP on the portfolio holdings of the households.

11 The household sector is described as the following in the Flow of Funds: ‘‘the values for the household

sector are calculated as residuals. That is, amounts held or owed by the other sectors are subtracted from known totals, and the remainders are assumed to be the amounts held or owed by the household sector....because of the residual nature of the household sector, assets of entities for which there is no data source, such as domestic hedge funds, private equity funds, and personal trusts, are included in this sector.’’ Available at http://www.federalreserve.gov/apps/fof/DisplayTable.aspx?t=l.100

12 The data period in Carpenter et al. (2015) is 1991:Q1-2012Q3.

14

Table 2

Dependent Variable

Δ(HH Treasury) Δ(HH Equity)

Δ(Fed Treasury) -0.04 0.20 (3.15)** (2.23)* Δ(Outstanding Treasury ) 0.15 -1.19 (1.77) (1.76) Lag dependent variable 0.11 -0.13 (0.84) (0.79) Constant -10.19 376.94 (0.40) (1.81) R2 0.25 0.15 N 45 45 Notes: Robust t-statistics are in parentheses. * p-value <0.05, ** p-value < 0.01.

First column of table 2 shows the results where the dependent variable is the Treasury

security holdings of households. The coefficient, 𝛽1, is significant with a negative sign. This result

shows that the increase in the Treasury security holdings of the Fed through LSAP reduces

Treasury security holdings of households. Carpenter et al. (2015) state that during the first LSAP

the Fed purchased about 60 percent of $300 billion Treasury securities from households.

Second column of table 3 examines portfolio rebalancing of households. We analyze

whether households shift their portfolios toward stock markets. The coefficient, 𝛽1, is significant

with a positive sign. Hence, the increase in the Treasury security holdings of the Federal Reserve

through LSAP increases equity holdings of households. Figure 4 displays the relationship

between the Fed’s asset holdings and the financial asset holdings of the households.

15

Figure 4: Treasury Security Holdings of the Federal Reserve and Equity Holdings of the

Households

Both table 2 and figure 4 indicate that the Fed purchases Treasury securities from

households and then households invest the liquidity in equities. This result provides the

intuition for the dramatic increase in the effect of the monetary policy action on stock returns

presented in Section 3. In addition to lowering long-term interest rates, the LSAPs increase the

demand for stocks. The Fed purchases longer-term assets from investors and prompts them to

purchase riskier assets like equities. This increase in demand for stocks causes an increase in

stock returns. Figure 5 below supports this transmission mechanism of LSAPs.

Figure 5 exhibits the relationship between the stock market and the household demand by

presenting the regression line where the dependent variable is the S&P 500 index and the

independent variable is household equity holdings. The significant positive relationship between

the variables suggests that the LSAPs causes a significant increase in the S&P 500 index by

inducing households to shift their portfolios from Treasury securities to stocks.

16

Figure 5: Regression Line of the Equity Holdings of the Households and the S&P 500

Index

6. Conclusion

This paper has empirically presented the significant effect of unconventional monetary

policy actions of the Fed on stock returns. The impact of Fed’s policy actions elevated sevenfold

after the federal funds rate hit the zero lower bound in December 2008 and the Fed started to

use unconventional policy actions. The S&P 500 index climbed from 797.87 in the beginning of

2009 to 2058.90 in the end of 2014 when the last LSAP program ended. Our results suggest that

monetary policy is one of major driving forces of this dramatic increase. We also study the

causes of the increased impact of the Fed’s actions since it started using unconventional

monetary policy actions. We find that the Fed purchases longer-term assets from investors and

prompts them to purchase equities. This causes an increase in demand for stocks and the rise in

the stock prices.

These findings have implications for both policy makers and investors. From a monetary

policy point of view, the results of this paper show that using LSAPs has a much stronger effect

on the stock markets compared to the traditional monetary policy tool of using the federal funds

50

01

00

01

50

02

00

0

4000 6000 8000 10000 12000 14000Household Equıty (Billion $)

S&P 500 Fitted values

Regression Line of S&P 500 and Household Equity Holdings

17

rate. Hence, the policy makers should take into account a stronger transmission mechanism

when using LSAPs. These results also provide valuable information for the investors which they

can use while making portfolio decisions.

18

References

Andrews, D. W. K., 1993, “Tests for Parameter Instability and Structural Change with Unknown Change Point”, Econometrica, 61: 821-856.

Bernanke, B. S., and A. S. Blinder, 1992, “The Federal Funds Rate and the Channels of Monetary Transmission”, the American Economic Review, 82, 901-921.

Bernanke, B. S. and M. Gertler, “Should Central Banks Respond to Movements in Asset Prices?” American Economic Review Papers and Proceedings, XCI (2001), 253–257.

Bernanke, B. S., and K. N. Kuttner, 2005, "What Explains the Stock Market Reaction to Federal Reserve Policy?," The Journal of Finance, 60(3), 1221-1256.

Bauer, M. D. and C. J. Neely, 2014, “International channels of the Fed's unconventional monetary policy”, Journal of International Money and Finance, 44:24-46.

Bjørnland, H., and K. Leitemo, 2009, “Identifying the interdependence between US monetary policy and stock market”, Journal of Monetary Economics, 56, 275–282.

Carpenter, S., S. Demiralp, J. Ihrig and E. Klee, 2015, “Analyzing Federal Reserve asset purchases: From whom does the Fed buy?”, Journal of Banking and Finance, 52: 230-244.

Coibion, O. and Y. Gorodnichenko, 2011, “Monetary Policy Trend, Inflation, and the Great Moderation: an Alternative Interpretation”, American Economic Review, 101:341–370.

Cúrdia,V., A. Ferrero, G. C. Ng, A. Tambalotti, 2015, “Has U.S. monetary policy tracked the efficient interest rate?”, Journal of Monetary Economics, 70:72-83.

Engen, E. M., T. Laubach and D. Reifschneider, 2015, “The Macroeconomic Effects of the Federal Reserve’s Unconventional Monetary Policies”, Finance and Economic Discussion Series 2015-005. Board of Governors of the Federal Reserve System (U.S.).

Fama, E. F., and K. R. French, 1993, “Common Risk Factors in the Returns on Stocks and Bonds”, Journal of Financial Economics, 33:1, 3-56.

Fama, E. F., and K. R. French. 2015a. A five-factor asset pricing model. Journal of Financial Economics 116: 1-22.

Gagnon, J.E., Raskin, M., Remache, J., Sack, B.P., 2011, “The financial market effects of the Federal Reserve’s large-scale asset purchases”, International Journal of Central Banking 7 (1), 3–43.

Gali, J. and L. Gambetti, 2015, "The Effects of Monetary Policy on Stock Market Bubbles: Some Evidence", American Economic Journal: Macroeconomics, 7(1), 233-57.

Hamilton, J.D., Wu, J.C., 2012, “The effectiveness of alternative monetary policy tools in a zero lower bound environment”, Journal of Money, Credit and Banking 44 (1), 3–46.

Hansen, Bruce E, 1992, “Testing for parameter stability in linear models”, Journal of Policy Modelling, 14:517-533.

Krippner, L. (2012) “Modifying Gaussian Term Structure Models When Interest Rates are Near the Zero Lower Bound” Reserve Bank of New Zealand Discussion Paper 2012/02.

Lewbel, A., 2012, “Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models,” Journal of Business and Economic Statistics, 30, 67-80.

Lombardi, M. J., and F. Zhu (2014) “A shadow policy rate to calibrate US monetary policy at the zero lower bound” Working paper, BIS.

19

Neely, J. C., 2015, “Unconventional monetary policy had large international effects”, Journal of Banking and Finance, 52:101-111.

Quandt, R. E., 1958, “The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes”, Journal of the American Statistical Association, 53: 873-880.

Quandt, R. E., 1960, “Tests of the Hypothesis That a Linear Regression System Obeys Two Separate Regimes”, Journal of the American Statistical Association, 55: 324-330.

Rigobon, R. and Sack, B., 2003, “Measuring the Response of Monetary Policy to the Stock Market”, Quarterly Journal of Economics, 118:639-669.

Rigobon, R. and Sack, B., 2004 “The Impact of Monetary Policy on Asset Prices”, Journal of Monetary Economics, 51,8:1553-1575.

Rosa, C. 2009, “The Validity of the Event-study Approach: Evidence from the Impact of the Fed’s Monetary Policy on US and Foreign Asset Prices”, Economica, 78: 429-439.

Stroebel, J.C., Taylor, J.B., 2012, “Estimated impact of the Fed’s mortgage-backed securities purchase program”, International Journal of Central Banking, 8 (2), 1–42

Williams, J. C., 2012, “The Federal Reserve’s Unconventional Policies”, FRBSF Economic Letter, 2012-34.

Wright, J.H., 2012, “What does Monetary Policy do to Long-term Interest Rates at the Zero Lower Bound?”, The Economic Journal, 122, 447-466.

Wu, J. C., and F. D. Xia, 2015, “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound”, Chicago Booth Research Paper No. 13-77. Available at SSRN:http://ssrn.com/abstract=2321323 or http://dx.doi.org/10.2139/ssrn.2321323