Earnings Management and Market Efficiency:

Re-examining the Post-Merger Performance Puzzle

ABSTRACT Earnings management by acquirers may affect the terms of a deal, whether a bid

succeeds, which management team emerges from the market for corporate control in

command of the target’s assets, and can therefore have irreversible consequences for

industrial structure and the distribution of gains between target and acquirer

shareholders. Using a sample of UK acquirers, the study documents opportunistic

behaviour in the periods preceding the announcement of a takeover bid.

If acquiring firms do engage in earnings management activities, the following

question naturally arises: Is earnings management successful? Furthermore, given that

income-increasing manipulation inevitably requires equal offsetting changes in later

years, if prospective bidders indeed resort to such creative accounting devices, to what

extent could then the evidence on poor post-merger performance be attributed to the

reversal of the effects of past earnings management?

Dr Antonia Botsari Lecturer Department of Banking and Financial Management University of Piraeus Piraeus 185 34 Grecce Email: abotsari@unipi.gr

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Earnings Management and Market Efficiency: Re-examining the Post-Merger Performance Puzzle

ABSTRACT

Earnings management by acquirers may affect the terms of a deal, whether a bid succeeds, which management team emerges from the market for corporate control in command of the target’s assets, and can therefore have irreversible consequences for industrial structure and the distribution of gains between target and acquirer shareholders. Using a sample of UK acquirers, the study documents opportunistic behaviour in the periods preceding the announcement of a takeover bid. If acquiring firms do engage in earnings management activities, the following question naturally arises: Is earnings management successful? Furthermore, given that income-increasing manipulation inevitably requires equal offsetting changes in later years, if prospective bidders indeed resort to such creative accounting devices, to what extent could then the evidence on poor post-merger performance be attributed to the reversal of the effects of past earnings management?

1. INTRODUCTION

In share for share corporate mergers, the consideration received by target shareholders is the acquiring firm’s stock. The total number of shares issued by the acquiring firm to gain control is determined by a negotiated exchange ratio (the number of shares of acquiring firm’s stock to be issued for a share of target’s stock) agreed on by the acquirer and the target. The number of shares of the acquiring firm’s stock exchanged for each of the target’s shares is computed based on the acquiring firm’s stock price on or near the merger agreement date. Because the exchange ratio is inversely related to the acquiring firm’s stock price, the acquiring firm may have an incentive to increase accounting earnings prior to the merger in the hope of raising the market price of its stock, and therefore reducing the cost of buying the target. Whether earnings management succeeds in raising the market price of a bidder’s stock will depend on the level of information efficiency in the market, and whether an analyst can “see through” and “reverse out” the earnings management device employed by the bidder’s directors. But if they cannot – for example, the market is semi-strong efficient in Fama’s (1970) terms whilst the earnings management is opaque to the analyst - and the bidder’s price is affected, then earnings management in such takeovers may have much more powerful economic consequences than in routine financial reporting. This is because, in such routine reporting, earnings management may only have a short-term impact on reported earnings, and one which is subsequently reversed: earnings are “borrowed” from future accounting periods, or expenses are delayed; but for given cash flows, the truth will come out in the end. However, in a share for share merger, earnings management can affect the terms of the deal and whether the bid succeeds. In this context, therefore, it could have irreversible consequences for the wealth of different shareholder groups (bidder and target), for industrial structure, and for which management team emerges from the market for corporate control in command of the target’s assets. Moreover, in the case of a large bid, the impact of the deal on the acquirer’s post-merger accounts is often so pervasive that the reversals of past earnings management may be swamped.

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This paper provides the first analysis of earnings management by bidders in share for share mergers in the world’s second largest takeover market (the London Stock Exchange). The analysis covers the period 1997-2001, when M&A activity reached a record level, share for share bids grew to dominate the value of transactions1, and the UK accounted for 31% of the global value of cross-border acquisitions (UNCTAD (2000)). Furthermore, if acquiring firms do engage in earnings management activities, the following question naturally arises: Is earnings management successful? In other words, do acquiring companies succeed in misleading the market and inflating their share price or is it the case that investors can “see through” the earnings manipulation? Finally, there has been accumulated evidence in the financial economics literature that acquisitions, especially those financed with stock, exhibit poor performance in the long-run. Given that accrual manipulation to inflate earnings inevitably requires equal offsetting changes in later years, if prospective bidders indeed adjust discretionary accruals upward prior to the offer, it would be interesting to explore to what extent the evidence on negative post-takeover performance could be attributed to the reversal of the effects of past earnings management, rather than to failure in underlying performance. Hence, the paper also aims to address the research questions presented above: i.e. can the post-merger performance puzzle be re-examined in the context of market inefficiency and accrual mispricing?

2. THE EARNINGS MANAGEMENT CONTEXT 2.1. Earnings Management ahead of Share-for-Share Bids Erickson and Wang (1999) were the first to test for earnings management by acquiring firms in stock for stock mergers. In a sample of 55 mergers performed by US companies and completed between 1985 and 1990, they find that acquiring firms manipulate total accruals, and hence manage earnings upward in the periods prior to the merger announcement (particularly in the quarter immediately preceding the offer). In contrast, they find no evidence of earnings management in a control group of 64 cash mergers. Their results also indicate that the degree of income increasing earnings management prior to the merger is positively related to the relative size of the deal2. Heron and Lie (2002) come to a different conclusion from Erickson and Wang. They examine 859 acquisitions (427 of which were paid with stock only) announced and completed by US companies between 1985 and 1997. Even though acquiring firms exhibit superior operating performance relative to their industry counterparts prior to acquisitions, they find no evidence of earnings management (even among the stock group) as proxied by discretionary accruals. They argue that the discrepancy with Erickson and Wang may be attributable to different samples or different procedures for estimating unexpected accruals.

1 This was a global pattern: see Andrade et al. (2001), Holmstrom and Kaplan (2001), Shleifer and Vishny (2003), Rhodes-Kropf and Viswanathan (2004). 2 Erickson and Wang (1999) also analyse discretionary accruals for target companies, a topic not analysed in this paper. For further literature on targets see Christie and Zimmerman (1994), Easterwood (1998), Eddey and Taylor (1999), and North and O’Connell (2002).

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The remaining literature focussing on stock for stock deals is consistent with Erickson and Wang. Louis (2004) examines 373 mergers (including 236 pure stock swaps) of publicly traded US companies that were announced and completed between 1992 and 2000. He finds that discretionary working capital accruals are positive and statistically significant for bidders engaging in stock swaps –especially in the quarter immediately prior to the merger announcement- whereas they are insignificant for acquirers that pay with cash. In an international setting, Rahman and Bakar (2002) analyse a sample of 125 Malaysian share acquiring firms over the period 1991-2000, and conclude that acquirers in share for share acquisitions manage earnings upward in the year prior to the acquisition3. 2.2. Accrual-Based Measures of Earnings Management A series of earnings management instruments are potentially available to managers. These range from real operating decisions such as asset sales (e.g. Bartov, 1993; Black et al., 1998) and changes in R&D expenditure (e.g. for the US: Baber et al., 1991; Perry and Grinaker, 1994; Bange and DeBondt, 1998; Bushee, 1998; Cheng, 2004; for the UK: Demirag, 1995; Grinyer et al., 1998; Osma and Young, 2006) to pure financial reporting decisions such as accounting method changes (e.g. Holthausen, 1981, 1990; Watts and Zimmerman, 1986) and accrual choices (e.g. McNichols and Wilson, 1988). Following Healy (1985), accruals have emerged as the preferred measure of accounting choice, and accrual-based models dominate the contemporaneous tests of the earnings management hypothesis. From a managerial perspective, accruals are likely to represent a favoured instrument for manipulating reported earnings (especially when the goal is to manage earnings temporarily) because of their relative low cost, as opposed to the potential reduction of shareholder value due to sub-optimal operating decisions (Peasnell, 1998). Furthermore, due to their opaque nature, accruals are often difficult to observe directly, in contrast to accounting procedure changes and highly visible transactions that are more likely to be “undone” by adjustment after publication by external parties (Young, 1999). Even if accruals manipulation is suspected, the information needed to undo the accrual changes may be limited or unavailable, making it harder to adjust away their effect. Watts and Zimmerman (1990) note that from a theoretical perspective accrual-based models are appealing because accruals aggregate into a single number the net effect of numerous accounting policies, thereby capturing the portfolio nature of income determination. In addition, studying accruals reduces the problems associated with the inability to measure the effect of various accounting choices on earnings. In the current study, while not denying the merits of modelling specific accruals in some contexts (see, e.g. McNichols (2000)), considering especially the fact that sample firms span a wide range of industries, the “portfolio” approach is adopted (i.e. examining a proxy for the sum of all accruals’ discretionary components). Under this method, predictions about earnings management relate to at least the total of all accounting adjustments, and therefore should capture a larger portion of earnings management. 3 Koumanakos et al. (2005) find no evidence of earnings management ahead of M&A in their study of the Athens Stock Exchange. However, they do not disaggregate their analysis according to the means of payment.

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2.3. Modelling Accruals The first model adopts the approach of Jones (1991), who was the first to introduce an expectations model that takes into account changes in the economic circumstances of the firm. In the first stage total accruals (TA) are regressed on the change in sales (ΔREV) and the gross level of property, plant and equipment (PPE), using the longest available time series of data immediately prior to the event year (i.e. the estimation period, when no systematic earnings management is hypothesised):

TAit/ Ait-1 = αi (1/ Ait-1) + β1i (ΔREVit / Ait-1) + β2i (PPEit / Ait-1) + εit (1) where: TAit = Total accruals for firm i in year t, defined as the change in non-cash

current assets minus the change in current liabilities (excluding the current portion of long term debt) minus depreciation & amortisation, i.e. (ΔCA-ΔCash) – (ΔCL-ΔCPLTD) – Dep.& Amort. The current portion of long-term debt (CPLTD) is excluded from current liabilities because changes in this account do not affect the calculation of net profit.

ΔREVit = Change in revenue (total sales) from year t-1 to year t for firm i PPEit = Gross property, plant and equipment for firm i in year t Ait-1 = Beginning of period total assets for firm i εit = Error term for firm i in year t t = 1,…,T estimation years immediately prior to event year i = 1,…,N firm index All variables are scaled by lagged total assets to reduce heteroskedasticity. Revenues are used to control for the economic environment of the firm because they are an objective measure of the firm’s operations before managers’ manipulations. Change in revenues and not the actual revenues figure is included in the expectations model, because changes in working capital accounts are included in the total accruals measure. The sign of the ΔREV coefficient is expected to be positive, since working capital accruals are expected to increase with revenues. Gross PPE is used to control for the portion of total accruals related to non-discretionary depreciation expense. PPE is included in the expectations model rather than changes in this account, because total depreciation and amortisation expense (as opposed to the change in depreciation and amortisation expense) is included in the total accruals measure. The sign of the PPE coefficient is expected to be negative, since PPE is related to an income-decreasing accrual (i.e. depreciation). In the second stage, the parameter estimates ai, b1i and b2i of αi, β1i and β2i from regression (1) are combined with data from the event (prediction) period to generate the estimated discretionary accruals: EDAip = u ip = TAip/ Aip-1 – [ai (1/ Aip-1) + b1i (ΔREVip / Aip-1) + b2i (PPEip / Aip-1)] (2)

where p = year index for years included in the event period An implicit assumption of the Jones model is that revenues are exogenous (i.e. non-discretionary). If, however, earnings are managed through discretionary revenues,

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then the Jones model will remove part of the managed earnings from the discretionary accrual proxy (e.g. in a situation where management uses its discretion to accrue revenues at year-end when the cash has not yet been received and it is highly questionable whether the revenues have been earned, the result will be an increase in revenues and total accruals - through an increase in receivables). Jones herself recognises this limitation of her model4, and the limitation has been explored further in Anthony and Ramesh (1992), Beneish (1997), Duncan (2001), Wells (2001), Eltimur et al. (2003), Nelson et al. (2003), and Marquardt and Wiedman (2004). In response to the Jones model limitation, Dechow et al. (1995) propose a modified version of the model. For estimation purposes, the modified-Jones model is identical to the standard-Jones model, with the exception that the change in accounts receivable (ΔREC) is deducted from ΔREV at the second stage. Hence, estimated discretionary accruals are now defined as:

EDAip= TAip/Aip-1 – [a i (1/Aip-1) +b1i (ΔREVip /Aip-1 – ΔRECip /Aip-1) +b2i (PPEip /Aip-1)] (3) The Modified Jones model implicitly assumes then that all changes in credit sales in the event period result from earnings management, given that (as discussed above) it is easier to manage earnings by exercising discretion over the recognition of revenue on credit sales than it is to manage earnings by exercising discretion over the recognition of revenue on cash sales. The merits and drawbacks of the Jones and modified Jones models have been explored by Dechow et al. (1995), Bernard and Skinner (1996), Guay et al. (1996), Rangan (1998), Young (1999), McNichols (2000), Peasnell et al. (2000a), Thomas and Zhang (2000), and Fields et al. (2001). We follow the conclusion of Balatbat and Lim (2003) that, while the Jones approach has its limitations, the evidence suggests that no other model is superior in estimating discretionary accruals. 2.4. Measuring Accruals The first issue regarding the measurement of accruals is the treatment of depreciation. Many recent studies (DeFond and Jiambalvo, 1994; Teoh et al., 1998 a&b; Peasnell et al., 2000a&b; Louis, 2004 to name a few) focus on working capital accruals only, i.e. exclude depreciation from the total accruals measure. As Beneish (1998) and Young (1999) discuss, depreciation has limited potential as an instrument of earnings management (especially over multiple periods) due to its visibility, rigidity and predictability. More specifically, Beneish (1998) argues that managing earnings via depreciation is potentially transparent (because changes in estimates that alter depreciation expense are disclosed in footnotes) and economically implausible (because the timing of capital expenditures would need to be divorced partially from the arrival of profitable investment opportunities). Young (1999) notes that it is doubtful whether the depreciation accrual can provide management with a source of multiperiod manipulation, since consistent changes of depreciation method and/or changes in the assumptions relating to asset life, residual value, etc., would almost certainly attract the attention of the incumbent auditor. Hunt et al. (1996) present empirical evidence that managers do not use the depreciation accrual as a means of 4 In an import relief investigation setting, Jones (1991) expresses the concern that “reported revenues may be affected to some extent by managers’ attempts to decrease reported earnings. For example, managers may postpone the shipment of merchandise during import relief investigation years in order to postpone recognition of revenue until the following year”.

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smoothing earnings or lowering debt-related costs. Sloan (1996) finds that current accruals account for most of the variation in total accruals. Working capital accrual manipulations, on the other hand, are more opaque than non-current accounts. Furthermore, working capital accruals include such judgemental items as provisions for doubtful debts, warranties and inventory obsolescence which prior research has shown are used to manage earnings. In a takeover setting similar to the one the current study analyses, Louis (2004) notes that in valuing acquisition partners, investment bankers rely more on earnings before interest, taxes, depreciation, and amortisation; therefore, an acquirer is more likely to manage its current accruals (i.e. total accruals before depreciation and amortisation). When working capital accruals are used to test for earnings management in either the standard-Jones or the modified-Jones model, the estimation procedure is identical to the one described in the previous section. The only difference is that since depreciation is not included in the definition of accruals, PPE is not included as an explanatory variable in the model. The second issue relates to the balance sheet versus the cash flow approach for measuring accruals. Hribar and Collins (2002) argue that the error induced by using a balance sheet approach contaminates computations of discretionary accruals, and can lead to erroneously concluding that earnings management exists when no such opportunistic activity is present. The balance sheet approach relies on the presumed articulation between changes in balance sheet working capital accounts and the accrual component of revenues and expenses on the income statement. This presumed articulation breaks down when non-operating (or “non-articulation” according to Hribar and Collins’s (2002) terminology) events occur. Such events include reclassifications, acquisitions, divestitures, accounting changes, and foreign currency translations. Changes in current assets and liabilities due to these non-operating events show up in the balance sheet, but do not flow through the income statement. Consequently, a portion of the changes in balance sheet working capital accounts relates to the non-operating events, and would erroneously be shown as accruals under the balance sheet approach. Mergers and acquisitions, for example, induce a positive bias to estimated accruals using the balance sheet approach, as net current assets tend to increase when a firm acquires another company (e.g. accounts receivable and inventory increase to reflect the balance of the merged entity). Therefore, Hribar and Collins (2002) suggest that it would be prudent for researchers to rely on accrual measures taken directly from the cash flow statement. Gore et al. (2001) criticise Hribar and Collins (2002): they contend that the error in total accruals measured through the balance sheet approach is unlikely to be correlated with the assumed drivers of accruals in the Jones model, resulting in the measurement error being captured entirely by the residual or discretionary accruals estimate. Gore et al. (2001) argue that this conjecture on the correlation between the measurement error and the change in revenue, in particular, is counter-intuitive. They note that change in total consolidated revenue is, a priori, no less susceptible to influence by mergers, acquisitions, and discontinued operations than change in working capital balances. They conclude that measuring total accruals using the cash flow statement is itself not unproblematic, since the difference between operating profit and operating cash flow usually includes a number of idiosyncratic accruals that cannot be classified systematically as either discretionary or non-discretionary. Given the contention surrounding this issue, we have prepared estimates using both total and working capital accruals, and the balance sheet and cash flow approaches.

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2.5. Estimating Accruals Many (if not most) recent earnings management studies use cross-sectional variations of the Jones and modified-Jones model, as the time-series specification has received several criticisms. First, the time-series formulation can be restrictive when implemented empirically because of the need for a sufficiently long time-series of data to allow effective estimation of the first-stage regression parameters (in the original Jones model, the estimation period for each firm ranges between 14 and 32 years, with a minimum requirement of 10 years). Hence, issues of survivorship as well as selection bias naturally arise: given that sample firms must have survived for at least 11 years (when using annual data), the sample tends to include large, mature firms with greater reputational capital to lose if earnings management is uncovered (Jeter and Shivakumar, 1999). Second, the assumption that the coefficient estimates on ΔREV and PPE remain stationary over time may not be appropriate (Peasnell et al., 2000a). Third, the self-reversing property of accruals may introduce specification problems in the form of serially-correlated residuals. Finally, a long time series of observations improves estimation efficiency but increases the likelihood of structural change occurring during the estimation period (Jones, 1991). In fact, evidence reported by Dechow et al. (1995), Kang and Shivaramakrishnan (1995), and Guay et al. (1996) for the US suggests that time-series versions of both models estimate discretionary accruals with considerable imprecision. In an effort to overcome these problems, cross-sectional variations of the models were developed5. The cross-sectional estimation consists of constructing industry-event period matched portfolios for each sample firm, estimating regression (1) for each industry/year combination to generate industry/year specific estimates of α, β1 and β2, and finally combining these estimates with firm-specific data in Equation (2) to yield EDA for each firm. In other words, the cross-sectional estimation of the Jones model takes the following form:

TA ijp /A 1−ijp = α jp (1/A 1−ijp ) + β jp1 (ΔREV ijp /A 1−ijp ) + β jp2 (PPE ijt /A 1−ijp ) + ε ijp (4) where: TA ijp = Total accruals for estimation portfolio j for firm i in event year p

ΔREV ijp = Change in revenue (total sales) for estimation portfolio j for firm i in event year p

PPE ijt = Gross property, plant and equipment for estimation portfolio j for firm i in event year p

Ait-1 = beginning of period total assets for estimation portfolio j for firm i in event year p

εit = error term for estimation portfolio j for firm i in event year p i = 1,…,N firm index j = 1,…,J estimation portfolio index p = 1,…,P year index (for years included in the event period) and estimated discretionary accruals are computed as:

5 DeFond and Jiambalvo (1994) introduced the cross-sectional variation of the Jones model.

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EDAip = TAip/ Aip-1 – [a jp (1/ Aip-1) + b jp1 (ΔREVip / Aip-1) + b jp2 (PPEip / Aip-1)] (5) In the cross-sectional modified-Jones model, regression (4) in the second stage becomes: EDAip =TAip/Aip-1 – [a jp (1/Aip-1) +b jp1 (ΔREVip/Aip-1 – ΔREC ip /Aip-1 ) +b jp2 (PPEip/Aip-1)] (6)

Cross-sectional models make no assumptions with regard to systematic earnings management in the estimation sample, and produce “industry-relevant” abnormal accruals that enable researchers to detect earnings management above and beyond the average unconditional earnings management found in that industry (Jeter and Shivakumar, 1999). In addition, they reduce the possibility that estimates of abnormal accruals are “contaminated” by time effects (e.g. interest rates and other macroeconomic factors) and industry effects (Peasnell et al., 2000a), and generate larger samples. Peasnell et al. (2000b) also show that the cross-sectional version of the modified-Jones model is more capable of capturing relatively subtle instances of accrual management in UK data. On the other hand, cross-sectional models, by implicitly assuming that the model parameters are the same across all firms in the estimation sample, may introduce noise into the parameter estimation to the extent that firms differ structurally within the industry. Furthermore, they ignore possible reversals of abnormal accruals from prior periods, thereby reducing the power of empirical tests to detect earnings management. Finally, they do not generate firm specific coefficients, and are less likely to capture dynamic accrual management strategies or industry-wide earnings management. Bartov et al. (2001) evaluate the ability of the cross-sectional Jones model and the cross-sectional modified-Jones model to detect earnings management vis-à-vis their time series counterparts by examining the association between discretionary accruals and audit qualifications. Their results suggest that the cross-sectional models perform better than their time-series equivalents at least among firms with extreme earnings management (i.e. those with qualified audit reports). Furthermore, when evaluating the effects of mergers and acquisitions on their findings (due to the concerns of Hribar and Collins (2002) discussed above), Bartov et al. (2001) find that only the two cross-sectional models survive all sensitivity tests. However, Bartov et al. (2001) also recognise the limitations of their study. First, the findings merely indicate the superiority of the cross-sectional models vis-à-vis their time-series counterparts in an audit qualification setting, without validating either the former or the latter. Second, since their tests evaluate the ability of discretionary accrual models to identify firms engaging in an extreme form of earnings management, the findings of the study may not generalise to firms with moderate levels of earnings management (e.g. firms engaging in earnings management within generally accepted accounting principles). With serious arguments for and against both estimation procedures, the relative power of cross-sectional and time-series models to detect earnings management remains an open empirical question. In the estimation which follows the results of the cross-sectional analysis are presented (a discussion on the results of the time-series approach can be found in Botsari (2006)).

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3. PRIOR EMPIRICAL RESEARCH ON POST-TAKEOVER PERFORMANCE

Numerous studies in both the US and the UK provide evidence of significantly negative performance for acquiring firms in the long-run. As Gregory (1997) summarises: “the conclusion […] is unambiguous; takeovers were, on average, wealth reducing events for acquiring companies”. This underperformance is associated particularly with the use of equity as a means of financing the acquisition. Given that one might expect markets to “learn” or become more efficient through time, the greatest puzzle from all these papers is the suggestion that takeovers in more recent years have produced significant negative returns for acquiring company shareholders. These results make the study of later acquisitions especially interesting. As far as the studies examining long-run bidder performance in a UK setting are concerned, the sometimes contrasting results may simply reflect time-varying returns to acquisitions or may be due to bias regarding the measurement of abnormal returns. In either case, the results raise the intriguing question as to whether or not the UK experience is different from that of the US, where negative abnormal returns to acquirers is now well established.

4. EVIDENCE ON ACCRUAL MISPRICING

As Dechow and Skinner (2000) note, the natural tendency of academics to assume investor rationality has caused accounting researchers to ignore capital market incentives for earnings management. Sloan (1996) was among the first to question the traditional efficient markets view that stock prices fully reflect all publicly available information. More specifically, in Sloan’s (1996) study, stock prices are found to act as if investors “fixate” on earnings, failing to reflect fully information contained in the accrual and cash flow components of current earnings until that information impacts future earnings. If investors naively fixate on earnings, then they will tend to overprice (underprice) stocks in which the accrual component is relatively high (low). This occurs because the lower persistence of earnings performance attributable to the accrual component of earnings is not fully anticipated. The mispricing will be corrected when future earnings are realised to be lower (higher) than expected, resulting in predictable negative (positive) abnormal stock returns. This asymmetric relation between current accruals and future stock returns was later labelled the “accrual anomaly”6. Houge and Loughran (2000) provide evidence in support of Sloan’s (1996) findings. They conclude that investors commit a cognitive error when valuing the information contained in current earnings in that they fixate on earnings and fail to fully reflect the information conveyed by accruals and cash flows: the consistent overvaluation of firms in their high accrual portfolio seems to imply that the market actually rewards firms for engaging in earnings management. 6 In a contemporaneous study, Subramanyam (1996) finds that indeed the stock market, on average, attaches value to the discretionary component of accruals. The evidence provided is consistent with managerial discretion improving the ability of earnings to reflect economic value, rather than discretionary accruals being opportunistic and value-irrelevant but priced by an inefficient market. However, Subramayam (1996) recognises that the research design employed in the study is not best suited to identify discretionary accruals motivated by opportunism.

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Xie (2001) demonstrates that the market not only prices, but also overprices discretionary accruals. Xie’s (2001) results also suggest that the overpricing of total accruals that Sloan (1996) documents is due largely to abnormal accruals. DeFond and Park (2001) report that the market anticipates only 19-23 percent of the pricing implications of abnormal accruals: market participants adjust only partially for suspected earnings management. Richardson et al. (2005) link earnings persistence and stock prices to accrual reliability. They construct a model showing that less reliable accruals (i.e. accruals that involve a greater degree of subjectivity –such as accounts receivable and inventories) lead to lower earnings persistence. They furthermore show that investors naively fail to anticipate the lower persistence of these less reliable accruals leading to significant security mispricing. Bradshaw et al. (2001) examine whether investors can “see through” temporary distortions in accrual accounting numbers by analysing the role of analysts and auditors. Overall, their evidence suggests that even professional investment intermediaries who specialise in interpreting accounting information do not alert investors to the future earnings problems associated with high accruals. As Dechow and Skinner (2000) note, the evidence suggests that investors underutilise publicly available financial statement information, and market participants are “fooled” by relatively simple earnings management practices. At the same time, the findings could indicate that investors do not view earnings management as so pervasive as to make earnings data unreliable (Healy and Wahlen, 1999). Schipper (1989) notes that there may be several reasons why researchers are able to observe earnings management while users of the managed earnings cannot. For example, “a researcher using large historical data sets might be able to document statistically a pattern of behaviour consistent with earnings management within the sample, without being able to say with confidence whether earnings were managed for any particular firm in the sample”. She further notes that in the case of studies examining capital market motivations for earnings management, the users of financial statements are public stakeholders who face a collective action problem in attempting to undo management’s manipulations. Maybe more importantly though, the definition of earnings management hinges fundamentally on managerial intent (i.e. whether management will exercise discretion over accruals to communicate private information about firm profitability and improve the ability of reported earnings to reflect fundamental value of the firm; or take advantage of the required exercise of judgement to subvert the intent of accounting standards and opportunistically manipulate accruals), which is difficult to assess using ex post accounting information. In the case of mergers and acquisitions, one could then argue that the target’s management at least has strong incentives to ensure that the acquirer’s reported accounting figures are free of manipulation. However, even if earnings management is suspected, the managers of the target may still agree to “sell out” for reasons of retirement or illiquid stock options (Shleifer and Vishny, 2003), due to extraordinary personal treatment (Hartzell et al., 2004) or because they underestimate the extent of the bidder’s overvaluation (Rhodes-Kropf and Viswanathan, 2004).

5. SAMPLE AND DATA

The sample used in the study is a sub-set of the population of mergers and acquisitions announced and completed between 1st January 1997 and 31st December

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2001 by UK companies. This initial sample was hand-collected and compiled using the information and data provided by the 61 relevant issues of Acquisitions Monthly. This source was chosen because it provided the most comprehensive listing of relevant transactions, while offering the required information regarding the dates, the terms, and other details of the deals7. While only UK acquirers were included in the sample (so as to achieve consistent data such as common accounting standards, etc.), no restriction was applied on the listing status or location of the target companies (in order to secure as broad range as possible of acquisition activity). 5.1. Identifying the Key Transaction Dates The research design requires accurate identification of dates in the takeover transaction relative to reporting periods. Any transaction normally follows three steps: negotiating the terms of the deal, reaching an agreement, and completing the exchange of the method of payment chosen to perform the acquisition. News announcements in the financial press relating to these steps are defined as announcement date, agreement date, and completion date respectively. More specifically, announcement date is the date when the intention to undertake a merger is first revealed. Agreement date is the date when a formal agreement concerning the terms of a merger is reached. Finally, completion date is the date on which the deal closes and is declared unconditional. These dates were assigned by a detailed study of the narratives to takeovers provided in Acquisitions Monthly. In the case of acquisitions of UK public companies, the stages of the transaction were reported in successive issues of the publication. But in the case of other targets, or where the data for UK public firms were incomplete, the data had to be supplemented by referring to FACTIVA and the Regulatory News Service (RNS) in particular. Because the timing of the successive stages is significant when it comes to locating accruals manipulation, and because the methods of reporting timing were diverse, detailed decision rules were adopted in this manual review of case histories to achieve consistency across the sample. These rules are discussed in Botsari (2006). 5.2. Sample Description The data collection process described above resulted in obtaining the population of mergers and acquisitions performed by UK companies between 1st January 1997 and 31st December 2001 (around 3,000 transactions). Thus it coincides with the last merger wave and the boom in takeover activity of the late 1990s. From this full sample, the subset of share swap transactions to be used in the study was selected. More specifically, in order for a company or transaction to be included in the sample the following criteria had to be fulfilled:

7 Although an effort was made to construct a sample as close to the population as possible, it is probable that some takeover transactions were not recorded in Acquisitions Monthly. However, since it is one of the most comprehensive listing sources with the information required, it was decided not to seek other data sources, as the methodology and transaction terms might be disclosed differently.

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(1) The deal was successfully completed8.

(2) The bidder acquired a majority interest in the target company or ended holding a majority interest as a result of the deal.

(3) The bidder is a UK publicly traded company (however, no restrictions were

imposed on the listing status or location of the target companies).

(4) The bidder is a non-financial company (SIC Codes 60-69)9.

(5) The transaction is either pure share swap or mixed but with a separately identifiable share element (i.e. when the target’s shareholders are given the option to receive the consideration either in the bidding firm’s shares or in cash)10.

As a result of the above selection criteria, 147 companies were identified that were involved in 176 transactions. In the case where a company has realised multiple transactions during the sample period, the earliest one is retained to avoid using overlapping data. Panel A of Table 1 presents the distribution of the companies per industry, while Panel B of Table 1 presents the distribution of the companies per year in relation to 8 Since all bids included in the sample were successful, the terms “bidder” and “acquirer” are used interchangeably throughout the study. 9 The rationale behind the exclusion of financial companies is that their financial reporting environments (regulatory regimes, internal governance structures) differ from those of industrial firms. In addition, financial firms have fundamentally different accrual processes that are not likely to be captured well by expectations models for normal accrual activity. Finally, the efficacy of the Jones (modified-Jones) model at detecting accrual management in financial firms has not been documented in the literature. 10 The cash alternative in an otherwise all share offer is very common in the UK. Rule 9 of “The City Code on Takeovers and Mergers” requires anybody acquiring 30% or more of the voting rights in a company to make a cash offer to all other shareholders at the highest price paid by it in the previous 12 months. In addition, Rule 11 (on mandatory cash offer) requires an offer to include a cash alternative whenever an offeror purchases offeree company shares during an offer period or within 12 months prior to its commencement (a practice very common across the sample companies), and that the cash alternative should be made available at not less than the highest price paid for the relevant shares during those 12 months or during the offer period respectively. Finally, Note 5 to Rule 11 states that shares acquired in exchange for securities may be deemed to be purchased for cash. It is reasonable to assume that when a bidding firm has the intention to acquire the target using its shares, then it also has the motive to manage earnings regardless of the way target’s shareholders will in the end decide to receive the consideration. This is why these mixed transactions have been included in the sample. There was also flexibility as to whether the transactions occurred as mergers or tender offers (in some cases, elements of both were involved). Mergers are negotiated directly with target’s management and approved by the target’s board of directors before going to a vote of target’s shareholders for approval. Tender offers are offers to buy shares made directly to target’s shareholders who decide individually whether to tender their shares for sale to the bidding firm (Jensen and Ruback, 1983). Both types of takeover were included because the motive for earnings management should exist in both settings. Furthermore, in US studies, often target hostility cannot be disentangled from the form of the deal (mergers versus tender offers) and the method of payment (stock versus cash), i.e. in the US terminology, hostile deals are more likely to be structured as tender offers and financed with cash, whereas non-hostile deals are more likely to be structured as mergers and financed with equity. However, as Higson and Elliott (1998) and Gregory (2005) note, there is no direct analogy in the UK to the merger/tender offer dichotomy. More specifically, Gregory (2005) argues that in contrast to the US position, many hostile bids are made when the form of payment is in equity (put in US terminology, “tender offers” can be, and frequently are, financed with equity).

13

ONS aggregates for M&A. The percentage by value of transactions performed as share for share deals follows an increasing trend, which peaks at 76% in 2000 and significantly decreases thereafter11. Erickson and Wang (1999) note that there was a correspondingly sharp increase in the percentage of corporate mergers completed as stock for stock exchanges in the late 1980s period they analyse, adding that commentators in the financial press linked this trend to the relatively higher valuations accorded to many firms during that period’s bull market. This argument also holds for the late 1990s period which is the focus of the current study: the FTSE All Share index rose from 2200 in 1998 to 3200 in 2000. The fact that companies belonging to the “Business Services” industry group (which incorporates the so-called high technology sector) performed almost 16% of the transactions throughout the sample period can then be explained in this context of high market valuations driving merger activity. The original share sample identified and the final sample used for the econometric analyses do not coincide. After excluding reverse takeovers, companies with lack of full accounting data on DATASTREAM/WORLDSCOPE, companies for whom only a completion but no announcement date for the deal was identified (the reason for this will become obvious in the following section), the final sample consists of 42 companies involved in 48 transactions. Although at first this may seem as a significant reduction in the sample size, it has to be noted that the vast majority of the transactions excluded are very small in terms of value, and that the transactions finally included in the sample capture more than 70% of the total deal activity of all the 147 share for share acquirers initially identified. Other comparable studies experience similar sample size reductions. For example, Eddie and Taylor (1999) have an initial sample of 205 target companies of which they were able to examine 43; Louis (2004) starts with 894 successful mergers, while his final sample contains 373 mergers. Table 2 summarises the way the final sample was derived, while Table 3 provides some descriptive statistics. 5.3. Identifying Years of Possible Earnings Management The underlying research question in this paper is whether acquiring firms manage reported earnings upward in the periods with an earnings release12 preceding the announcement of a merger. The incentive to do this is also expected to persist through periods preceding the agreement date. Out of the 42 companies in the final share sample, there are 7 companies whose announcement and agreement dates can be separately identified, 12 companies with an announcement date but no agreement date, and 23 companies with an agreement date that is the same as the announcement date13. For the 7 companies with separately

11 Because share for share deals tend to be the larger ones, the trend is much more pronounced for series based on deal value than numbers of deals. 12 In this study, the earnings release date is the date that the annual financial statements are published and made available to the general public. This date is obtained from the Regulatory News Service of the London Stock Exchange. In the sample examined, there is usually a 2-4 month lag between the end of the accounting year and the release of the financial statements. For the majority of the companies, the earnings release date is the date that the annual report is published. There are 8 cases, however, where the announcement date of the deal falls between the preliminary earnings announcement for the year ended and the final earnings announcement. In these cases, the first date is taken into account. 13 The mean (median) lapse in days between a merger announcement (pending or agreed) and its completion is 74 (60). For the 7 cases with a separately identifiable announcement and agreement date, the mean (median) lapse in days between these two dates is 17 (16).

14

identifiable announcement and agreement dates, there was no earnings release in between. Therefore, from this point onwards, the announcement of a deal will refer to the announcement of either a pending or an agreed bid. Hence, Year 0 is defined as the first year14 with an earnings release preceding the announcement of a merger (pending or agreed), Year -1 as the second year with an earnings release preceding a merger announcement, and Year 1 as the first year with an earnings release following the merger announcement. For 32 out of the 42 companies, the latter is the earnings release that also reports the completion of the deal. For the 10 companies with an earnings release between the announcement of a merger (pending or agreed) and its completion, a special period, Year 01, is introduced. The above sequence is illustrated in Table 4.

6. RESULTS OF ACCRUAL TESTS The results of the cross-sectional tests are based on discretionary accruals estimated using the models described below. All models are estimated for each event year (Year -1, Year 0, Year 01 (where applicable), and Year 1) using all companies in the same two-digit SIC code as the sample company but who did not experience an event similar to the one experienced by the sample company (in this case, a share for share transaction)15. (1) Cross-sectional standard-Jones model

TA ijp /A 1−ijp = α jp + β jp1 (ΔREV ijp /A 1−ijp ) + β jp2 (PPE ijt /A 1−ijp ) + ε ijp (7) where: TA ijp = Total accruals for estimation portfolio j for firm i in event year p

ΔREV ijp = Change in revenue (total sales) for estimation portfolio j for firm i in event year p

PPE ijt = Gross property, plant and equipment for estimation portfolio j for firm i in event year p

Ait-1 = Beginning of period total assets for estimation portfolio j for firm i in event year p

εit = error term for estimation portfolio j for firm i in event year p i = 1,…,N firm index j = 1,…,J estimation portfolio index p = 1,…,P year index (for years included in the event period) and estimated discretionary accruals are computed as: 14 This refers to fiscal year, and following Peasnell et al. (2000b) it includes all firms with financial year-ends falling on or between 1st June year t and 31st May year t+1. 15 Across all SIC codes it was very rare to find companies that had not performed even a small cash acquisition. Therefore, a firm was considered not to have experienced an event the same as the sample company (and was therefore included in the estimation portfolio) if it had been involved in a cash transaction where the net assets from the acquisition did not exceed 5% of the company’s net assets. However –and similar to the exclusion criteria for the sample companies- if a company’s absolute total and/or working capital accruals scaled by lagged total assets were greater than one, then this company was deleted from the industry/event year estimation portfolio.

15

EDAip = u ip = TAip/ Aip-1 – [a jp + b jp1 (ΔREVip / Aip-1) + b jp2 (PPEip / Aip-1)] (8) Equation (7) differs slightly from Equation (4) where the constant term is also scaled by lagged total assets, resulting in estimating a regression with the true constant term suppressed. Peasnell et al. (2000a) argue that there is no theoretical reason to scale the intercept and hence force the regression through the origin. Furthermore, regressions estimated with the constant term suppressed preclude an analysis of the goodness-of-fit of the models because the associated R-squared values are unreliable (in Peasnell et al. (2000a), even when the alternative specification was used, the results turned out identical). Kothari et al. (2005) provide three arguments in favour of including a true constant term. First, it provides an additional control for heteroskedasticity not alleviated by using assets as the deflator. Second, it mitigates problems stemming from an omitted size (scale) variable. Finally, Kothari et al. (2005) find that discretionary accrual measures based on models without a constant term are less symmetric, making power of the test comparisons less clear-cut. Equation (7) is estimated using four alternative accrual measures: Total accruals obtained from the balance sheet approach: In this case, accruals are defined as the change in non-cash current assets (CA) minus the change in current liabilities (CL) minus depreciation & amortisation (D&A)16. In terms of WORLDSCOPE items17, that is:

[ΔCA (WC02201) – ΔCash (WC02001)] – ΔCL (WC03101) – D&A (WC04051)

Working capital accruals obtained from the balance sheet approach: In this case, accruals are defined as the change in non-cash current assets minus the change in current liabilities (again no adjustment is made for the current portion of long-term debt). In terms of WORLDSCOPE items, that is:

[ΔCA (WC02201) – ΔCash (WC02001)] – ΔCL (WC03101) When a working capital accrual measure is used to estimate Equation (7), the gross property, plant, and equipment (PPE) is not included as an explanatory variable in the model, since depreciation is not included in the calculation of accruals. Total accruals obtained from the cash flow approach:

16 Note that no adjustment is made for the current portion of long-term debt, as data are generally unavailable for this item across sample companies. Jones (1991) also measures accruals without adjusting for the current maturities of long-term debt. Subramanyam (1996) sets the value of the variable equal to zero, when the item is not available. However, this paper follows the first approach of not considering at all the current portion of long-term debt when calculating accruals, in order to ensure uniformity of accrual definition and measurement across the sample. 17 The items given below in parentheses refer to the actual levels of the variables and not to the changes in the level of the variables.

16

In this case, accruals are defined as the difference between net income before extraordinary items (NI)18 (as reported in the cash flow statement) and cash flow from operations (CFO)19 , 20. In terms of WORLDSCOPE items, that is:

NI (WC04001) – CFO (WC04201+ WC04831) Working capital accruals obtained from the cash flow approach: In this case, accruals are defined as the difference between net income before extraordinary items (as reported in the cash flow statement) and operating cash flows (excluding depreciation)21. In terms of WORLDSCOPE items, that is:

NI (WC04001) – CFO (WC04201+ WC04831) – D&A (WC04051) As with the balance sheet approach, when the working capital accrual measure is used to estimate Equation (7), the gross property, plant, and equipment (PPE) is not included as an explanatory variable in the model. (2) Cross-sectional modified-Jones model

TA ijp /A 1−ijp = α jp + β jp1 (ΔREV ijp /A 1−ijp ) + β jp2 (PPE ijt /A 1−ijp ) + ε ijp (7) where: TA ijp = Total accruals for estimation portfolio j for firm i in event year p

18 In defining net income exclusive of extraordinary items, the model follows, for example, Subramanyam (1996), Bradshaw et al. (2001), Hribar and Collins (2002), Dechow et al. (2003), and Kothari et al. (2005). 19 As far as the figure for the operating cash flow is concerned, WORLDSCOPE contains a variable labelled “Net Cash Flow – Operating Activities” (WC04860) which includes “Funds from Operations” (WC04201), “Funds from/for Other Operating Activities” (WC04831) – which in fact represents the net change in working capital –, and extraordinary items. As Hribar and Collins (2002) note, when computing accruals from the cash flow statement, the definition of cash flow should be consistent with the definition of net income. Given that the net income figure used in the calculation of accruals excludes extraordinary items, using variable WC04860 as the operating cash flow figure would be inappropriate. On the other hand, variable WC04201 excludes extraordinary items and conforms to the net income definition. Hence, the operating cash flow figure is computed as the sum of the variables WC04201and WC04831. 20 An alternative approach (which resembles more the balance sheet approach) in calculating accruals from the cash flow statement is to focus on changes in certain accounts (e.g. accounts receivable, inventory, accounts payable) as disclosed in the statement of cash flows, taking also into account the depreciation expense. However, as Hribar and Collins (2002) point out, the difference between net income and operating cash flows provides a more comprehensive measure of accruals, since under this approach accruals such as deferred taxes, restructuring charges, and special items (which may be important for certain types of earnings management) are included in the accrual measure. Therefore, Hribar and Collins (2002) suggest that the latter would be a more appropriate measure to use in accruals-based research. 21 A pure working capital accrual measure would be to focus on changes in working capital accounts (accounts receivable, inventory, accounts payable) as disclosed in the statement of cash flows. However, this kind of segregated information is not uniformly available for all sample companies. Hence, what differentiates total and working capital accruals under the cash flow approach adopted is the depreciation expense. This also implies that the cash flow approach adopted does not preclude “working capital” accruals from possibly including other long-term operating accruals apart from depreciation.

17

ΔREV ijp = Change in revenue (total sales) for estimation portfolio j for firm i in event year p

PPE ijt = Gross property, plant and equipment for estimation portfolio j for firm i in event year p

Ait-1 = Beginning of period total assets for estimation portfolio j for firm i in event year p

εit = error term for estimation portfolio j for firm i in event year p i = 1,…,N firm index j = 1,…,J estimation portfolio index p = 1,…,P year index (for years included in the event period) and estimated discretionary accruals are computed as: EDAip = u ip =TAip/Aip-1 – [a jp + b jp1 (ΔREVip/Aip-1 – ΔREC ip /Aip-1 )+ b jp2 (PPEip/Aip-

1)] (9) As with the standard-Jones model, four accrual measures are used, applying the same variable definitions as the ones discussed before22. Overall, 73 industry/event year portfolios were formed and each model (with the four alternative accrual measures) was estimated for each one of these portfolios. The minimum number of observations required for the estimation of each portfolio is six23. Descriptive statistics for the estimated regressions under the balance sheet approach are presented in Table 5 (as discussed, the Jones and modified-Jones models are identical for estimation purposes). The number of companies included in each SIC/event year cluster ranges from 6 to 89, with a mean of 20.67 and a median of 16. Under the total accruals approach, the coefficient on the change in revenue is, as expected, generally positive with a mean (median) of 0.0298 (0.0086). The coefficient on property, plant, and equipment is generally negative with a mean (median) of -0.0867 (-0.0965). The mean (median) R-squared is 0.24 (0.20), which is encouraging as an indicator of the degree of control achieved. Under the working capital accruals approach, the coefficient on the change in revenue is again generally positive. Its mean and median values of 0.0422 and 0.0163 respectively indicate that the revenue coefficient using a working capital accrual measure is greater than the one obtained under the total accruals approach. In contrast, both mean (0.13) and median (0.06) R-squared values are notably lower. Descriptive statistics for the estimated regressions under the cash flow approach are presented in Table 6. The number of companies included in each SIC/event year cluster ranges from 6 to 87, with a mean of 20.57 and a median of 16. Under both accrual measures (total and working capital) the estimated coefficients have the expected sign (positive for the change in revenue and negative for property, plant, and equipment). The magnitude of the revenue coefficient though is significantly greater than the one obtained under the balance sheet approach. More specifically, when a total accrual measure is used, the mean value of the coefficient is more than two times 22 When estimating the modified-Jones model using the cash flow approach, the change in accounts receivable (ΔREC) at the second stage is taken from the statement of cash flows, while under the balance sheet approach it is measured as the difference in subsequent balance sheets. 23 DeFond and Jiambalvo (1994), Subramanyam (1996), and Gore et al. (2001) also require a minimum of six observations when estimating discretionary accruals.

18

higher (0.0618 vs. 0.0298), while when a working capital accrual measure is used, the mean value of the coefficient almost doubles (0.0766 vs. 0.0422). Finally, the reported mean and median R-squared values are also higher than the ones derived from the balance sheet regressions. The results of these cross-sectional tests are presented in Tables 7 and 8. The reported residuals have a straightforward interpretation; namely, they measure the level of discretionary accruals as a percentage of lagged total assets. The results derived from the cross-sectional standard-Jones model (Table 7) under the balance sheet approach indicate that in Year 0 the mean and median residuals for working capital accruals are positive and statistically significant. Thus, both the mean residual of 0.0276 and the median residual of 0.0202 are indicative of earnings management (of approximately 2.8% and 2% of total assets respectively)24. Under the total accrual measure, discretionary accruals are again positive, but fail to become statistically significant. This finding appears to confirm prior empirical evidence that depreciation has a limited potential as an earnings management instrument. As for the positive sign of abnormal total accruals, we can argue that this is driven by the working capital accrual component. Thus, under the cross-sectional standard-Jones model and using a balance sheet-based working capital accrual measure, the hypothesis of income-increasing accrual manipulation in the first year with an earnings release preceding a merger announcement is consistent with the evidence25. In Year -1 (the second year with an earnings release preceding a merger announcement), for both total and working capital accruals, both mean and median discretionary accruals are negative but not statistically significant. The same results hold for Year 01 (which applies to 10 of the 42 sample firms), where, although estimated discretionary accruals are negative, not only are they not statistically significant, but also their absolute value (especially the median) is very small indeed. Finally, Table 7 reports discretionary accruals for Year 1, even though these measures may require careful interpretation. As Louis (2004) (who also reports discretionary accrual measures following the merger completion) notes, a merger affects the balance sheet accounts in such a complex way that it is difficult to explain and compare the changes in accruals. However, analysing the impact of such factors on accruals is beyond the scope of the study26. Mean and median residuals under both

24 Zhang (2002) argues that the importance of a particular type of earnings management should be evaluated not only on the basis of its magnitude, but also on the basis of its frequency; in the sense that earnings management of small magnitude is less likely to be caught by auditors and regulators, and hence “minor offences” are likely to be more rampant than “flagrant fouls”. As Clikeman (2003) notes: “With all the concern surrounding fraudulent financial reporting, fraud’s “innocent” little brother – earnings management – is often overlooked”. In this respect, it is interesting to note that none of the sample companies was at any later stage targeted by the authorities of the Financial Reporting Review Panel. Furthermore, as Healy and Wahlen (1999) note, even unexpected accruals of the magnitude of 2% of assets constitute surprising large values, since they represent 25% to 50% of typical asset returns. 25 Erickson and Wang (1999) report median unexpected total accruals (obtained from the standard-Jones model under the cash flow approach) in the quarter immediately preceding the offer announcement equal to 0.019. They also note that mean residuals suggest that acquiring firms may have high unexpected accruals as early as three quarters before the news of a merger is first revealed in the financial press. This is consistent with the current study’s income-increasing discretionary accruals detected the year immediately prior to the offer. In Louis’ (2004) study, median unexpected working capital accruals (obtained from the ROA-adjusted cross-sectional modified-Jones model under the balance sheet approach) are equal to 0.740. 26 Studies analysing the effect of these factors (not necessarily in an accrual context) such as the impact of transaction costs or the way the transaction was accounted for (acquisition versus merger

19

total and working capital accrual measures are negative and statistically significant. This finding is consistent with the hypothesis of mean reversion of accruals after the deal is completed. Table 7 also reports the results of the discretionary accrual tests under the cash flow approach27. The general conclusion from the earlier analysis – that acquiring firms manage earnings upward through manipulation of working capital accrual accounts – is confirmed by these further tests. This finding is important because it shows that the earnings management evidence discussed above reflects true earnings manipulation and is not due to the potential bias associated with accruals under the balance sheet approach. In fact, earnings management – as reflected in working capital discretionary accruals – prior to the merger announcement (Year 0) is more than 3% of total assets, a figure greater than the corresponding evidence from the balance sheet approach. These higher estimates of working capital discretionary accruals under the cash flow approach could be due to the way in which the cash-flow based working capital accruals have been defined; i.e. that apart from pure working capital accounts, they may also include some long-term operating accruals (e.g. provisions) whose amounts – as Beneish (1998) notes – are very sensitive to management’s key assumptions, and as such prone to manipulation. Two further points regarding the reported results should be made. First, that the evidence of mean reversion of accruals is less pronounced, as discretionary accrual estimates are marginally (if at all) negative and not statistically significant28. Second, in contrast to the balance sheet approach, estimated discretionary accruals for Year -1 (the second year with an earnings release prior to the merger announcement) are positive and in most cases statistically significant. This result should not come as a surprise, since personal discussions with mergers and acquisitions practitioners revealed that a company may decide on an expansion strategy via acquisitions without having a specific target in mind for a year or more before the date of the public offer. Furthermore, as Perry and Williams (1994) note in a management buyout context, the fact that the year prior to the offer announcement is the most likely period to reflect systematic earnings manipulation does not preclude the possibility that some firms may manage earnings for several years prior to the offer. Table 8 presents the results derived from the cross-sectional modified-Jones model. These results are qualitatively the same as the ones reported in Table 7. However, the results indicate a greater magnitude of earnings management in Year 0, as demonstrated by the higher mean and median values of discretionary accruals. More specifically, under the working capital accrual measure (balance sheet approach), mean (median) discretionary accruals account for approximately 3.2% (2.2%) of total assets, a little greater than the 2.8% (2%) in the standard-Jones model. When accruals are computed from the cash flow statement, the implied magnitude of earnings management – approximately 3.4% (3.1%) of lagged total assets for the mean (median) working capital discretionary accruals – is again higher than the accounting) include Hong et al. (1978), Robinson and Shane (1990), Aboody et al. (2000), and Meeks and Meeks (2001). 27 These results are based on the estimated discretionary accruals of 41 sample firms (as opposed to 42 firms under the balance sheet approach), because one firm’s absolute cash-flow based accruals scaled by lagged total assets were greater than one. 28 The empirical findings for Year 1 under the cash-flow approach could give rise to the following interpretation, also reported by Jones (1991) in her import relief investigation setting (but in the opposite direction); i.e. that managers may reverse “excessive” earnings–increasing accruals over a period more than one year after or that they face other incentives that conflict with the reversal, such as, in this case, the pursuit of another share swap acquisition.

20

corresponding figure in the standard-Jones model. These findings seem to align with Peasnell et al.’s (2000b) evidence that the cross-sectional version of the modified-Jones model is more capable of capturing relatively subtle instances of accrual management in UK data. The findings also appear to support prior evidence that equity issuing firms accelerate revenue recognition and use accounts receivable to manage earnings (e.g. Marquardt and Wiedman, 2004). Finally, not only are discretionary accruals under the total accrual measure positive, but they also become statistically significant (albeit marginally). This result is more likely to have been driven by the greater manipulation detected in the working capital component of accruals, rather than by the manipulation of the depreciation accrual.

7. RESULTS ON ABNORMAL STOCK RETURNS The incentive behind the earnings management behaviour documented in the previous section is that acquiring companies manage earnings in order to inflate their share price in the period preceding the announcement of a bid. On another stream of literature, most of the studies analysing the long-run performance of acquiring firms document negative abnormal returns for acquirers, especially for those financing their acquisitions with stock. Whether the apparent abnormal returns are due to mispricing or simply the result of measurement problems is a contentious and unresolved issue among financial economists (Kothari and Warner, 2004). As Brown and Warner (1980) note, a security’s price performance can only be considered “abnormal”

relative to a particular benchmark [i.e. the abnormal return AR it of company i at

month t is equal to the actual return R it of company i at month t minus the expected

return E(R it ) of company i at month t: AR it = R it - E(R it )] . As was pointed out in the review of the relevant literature, however, many of the results reported were sensitive to the benchmark specification adopted to measure abnormal performance. Barber and Lyon (1997) and Kothari and Warner (1997) find that the market-adjusted and portfolio-adjusted methods of computing long-run returns used in these studies are misspecified. More specifically, Barber and Lyon (1997) argue that “many of the common methods used to calculate long-run abnormal stock returns are conceptually flawed and/or lead to biased test statistics”. They, therefore, propose estimating long-run performance using buy-and-hold abnormal returns (BHARs) whereby the abnormal return of a specific firm is adjusted by the abnormal return of a control firm matched on the basis of size (as reflected in market capitalisation) and the book-to-market ratio. Mitchell and Stafford (2000) describe BHARs as “the average multiyear return from a strategy of investing in all firms that complete an event and selling at the end of a pre-specified holding period versus a comparable strategy using otherwise similar non-event firms”. Ikenberry et al. (1995) note that a buy-and-hold approach represents a more feasible investment strategy than that implied by the Cumulative Abnormal Return (CAR) model. Dissanaike (1994, 1997) also states that the “arithmetic” (CAR) method does not accurately measure the returns earned by an investor, and apart from simplifying computation, there appears to be little justification for using it. He further notes that the buy-and-hold method is better suited for the purpose of identifying stock-price reversals. Barber and Lyon (1997) and Lyon et al. (1999) argue that BHARs are important because they “precisely measure investor experience”. Kothari and Warner (2004) also note that an appealing feature of using BHARs is that buy-and–hold returns better resemble investors’ actual

21

investment experience than periodic (monthly) rebalancing entailed in other approaches to measuring risk-adjusted performance. Apart from similarity with the actual investment experience, the buy-and-hold approach also avoids biases arising from security microstructure issues when portfolio performance is measured with frequent rebalancing. In this section, therefore, buy-and-hold abnormal returns (BHARs) are used in order to analyse the acquirers’ pattern of share price behaviour in the five years surrounding the bid (pre-announcement and post-completion). Buy-and-hold abnormal returns are calculated for each sample company i over a period of τ months in the following way:

∏=

+τ

1

]1[t

itR ∏=

+τ

1

)](1[t

itRE

where

BHAR τi is the buy-and-hold abnormal return of company i over a period of τ months.

R it is the actual return of company i at month t, calculated as the change in price

adjusted for dividend payments, i.e. R it = 1

1

−

− +−

it

ititit

PDPSPP

where P it is the

company’s share price at month t, P 1−t is the company’s share price at month t-1, and

DPS it is dividends per share at month t.

E(R it ) is the expected return of company i at month t. In the analysis that follows, the expected return has been proxied by either the market return or the return of a portfolio of control firms matched to each sample firm on the basis of size and book-to-market ratio. According to the methodology proposed by Barber and Lyon (1997) – and adopted by Louis (2004) among others- the control firms are chosen as follows: Following Fama and French (1992, 1993), firm size is measured in June of each year as the market value of common equity (shares outstanding multiplied by June closing price). Size rankings based on market value of equity in year t are then used from July of year t through June of year t+1. The first step in selecting the control firms is to identify all firms with a market value of equity between 70% and 130% of the market value of equity of the sample firm at any given year t. The book-to-market ratio is measured using the book value of the common equity (WORDSCOPE item WC03501) reported on the firm’s balance sheet in year t-1 divided by the market value of common equity in December of year t-1. Rankings based on book-to-market ratios are then used from July of year t through June of year t+1. The calculation of book-to-market ratios precedes their use for ranking purposes by a minimum of six months to allow for delays in the reporting of financial statements by companies. Barber and Lyon (1997) argue that the control firm approach alleviates the new listing bias (since both the sample and the control firm must be listed in the identified month), the rebalancing bias (since the returns of the sample and the control firm are compounded in an analogous fashion), and the skewness bias (since abnormal returns calculated using this control firm approach are reasonably symmetric). Barber and

BHAR τi = –

22

Lyon (1997) find that filtering on size and then matching on the book-to-market ratio yields test statistics that are well specified in virtually all sampling situations that they consider. The results (both value- and equally-weighted) are presented in Tables 9 and 10. Abnormal returns calculated according to the CAR methodology are also reported for comparison. As can be seen (Table 9), sample companies benefit from significantly positive abnormal returns in the two years preceding the announcement of a share for share bid. Prima facie, these positive abnormal returns suggest that acquiring companies succeeded in their goal of inflating their share price during the periods in the run-up to a bid, and that the market was not able to see through the accounting distortions. In other words, the market may be misled in that it inefficiently prices value-irrelevant discretionary accruals and unintentionally rewards firms for engaging in earnings management. Fields et al. (2001) criticise the results of Erickson and Wang (1999), in their US study of earnings management ahead of share-financed acquisitions, as being unconvincing, because their research design does not allow one to test whether earnings management was successful. On this account, the evidence presented above is particularly important. On the contrary (Table 10), sample companies earn significantly less than their matched portfolio in the 24 and 36 months following completion of the bid. As Loughran and Vijh (1997) note, the negative abnormal returns in the post-takeover period are inconsistent with market efficiency, because they suggest that the market overestimated the potential gains from the takeover and did not fully impound the information conveyed by the announcement of a share for share deal (i.e. that the acquirer’s shares might have been overvalued).

8. EARNINGS MANAGEMENT AND ABNORMAL PERFORMANCE Prior research on earnings management in equity-issuing settings has linked abnormal accruals detected in the pre-issue period with the long-run stock performance of equity-issuing companies. Rangan (1998), Teoh et al. (1998b) and DuCharme et al. (2004) provide evidence that aggressive earnings management through accrual manipulation and the associated earnings reversals when companies can no longer maintain appearances led to the poor aftermarket performance of firms in seasoned equity offerings. Teoh et al. (1998a) and DuCharme et al. (2004) show that this pattern also holds for initial public offerers. More recently, Louis’ (2004) results indicate that the reversal of the effects of pre-merger earnings management can partially explain the long-term underperformance of stock-for-stock acquirers. If this is indeed the case, then we would expect acquirers in the most aggressive (most conservative) earnings management quartile to exhibit the highest (lowest) positive abnormal stock returns in the pre-announcement period, and also the highest (lowest) negative abnormal returns in the post-completion period. The stated hypothesis is indeed confirmed by the results presented in Table 11. Finally, Tables 12 to 16 attempt a disaggregation of the results on the basis of certain control variables which prior research has shown may have an impact on the acquirer’s long-run performance; for example industry, time and size effects, value vs.

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glamour acquirers (Rau and Vermaelen, 1998; Sudarsanam et al., 2003; Gregory, 2005) and the role of the takeover premium29.

9. SUMMARY AND CONCLUSIONS The paper provides evidence as to whether acquiring firms manage earnings ahead of share-financed mergers and acquisitions. The results suggest that acquiring companies engage in income-increasing accrual manipulation in the year immediately preceding the offer announcement. This manipulation is mostly concentrated on the working capital component of accruals, confirming prior empirical evidence that depreciation has a limited potential as an earnings management instrument, and that investment bankers’ focus on earnings before interest, taxes, depreciation, and amortisation provides acquiring firms with an incentive to manipulate working capital accruals rather than the more rigid and mechanical long-term accruals such as depreciation. This finding is robust to alternative methods of calculating working capital accruals (i.e. balance sheet vs. cash flow approach). Such evidence is important because it shows that the observed earnings management reflects true earnings manipulation and is not due to the positive bias associated with accruals under the balance sheet approach. Results from the cross-sectional cash flow models suggest that earnings manipulation may start as early as Year -1. This result does not come as a surprise, since personal discussions with mergers and acquisitions practitioners revealed that a company may decide on an expansion strategy via acquisitions without having a specific target in mind for a year or more before the date of the public offer. Hence, the fact that the year prior to the offer announcement is the most likely period to reflect systematic earnings manipulation does not preclude the possibility that some firms may manage earnings for several years prior to the offer. One possible interpretation of the results would draw on the signalling literature, where a stock offer is seen as indicating to the market that the bidding firms believe their own stock to be over-valued. Shivakumar (2000) argues that since issuers cannot credibly signal the absence of earnings management, investors treat all firms announcing an equity offering as having over-stated prior earnings, and consequently discount their stock prices. He argues that earnings management before equity offerings is not intended to mislead investors, but is instead the issuer’s rational response to anticipated market behaviour at offering announcements: they rationally overstate earnings at least to the extent expected by the market. In the same vein, Erickson and Wang (1999) suggest that stock for stock acquirers overstate earnings before merger announcements because the target firms anticipate it and adjust for the anticipated earnings management when negotiating on the purchase price. 29 There is growing belief that the poor long-run performance of acquirers may be attributed to overpayment in mergers. Premiums that are too high implicitly require massive growth targets and cost savings to materialise for M&A transactions to be justified (Antoniou et al., 2006). In this context, Schwert (2003) argues that one interpretation of the post-merger underperformance is that bidders overpay and that it takes the market some time to gradually learn about this mistake. However, Antoniou et al.’s (2006) results, based on a sample of 396 successful UK public mergers between 1985 and 2004, provide no evidence that firms paying high premiums underperform those paying relatively low premiums in the three years following the deal and after controlling for a variety of deal and firm characteristics such as the method of payment, the book-to-market ratio of the acquirer, the relative size of the target, and corporate diversification.

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A rather less benign interpretation comes in the model of Shleifer and Vishny (2003), in which acquisitions are driven by stock market valuations of the merging firms. The model assumes that financial markets are inefficient, so some firms are valued incorrectly. Managers, however, are rational, understand stock market inefficiencies (e.g. how their own firm’s and the prospective merger partner’s short-run valuation deviates from the efficient market price), and take advantage of them in their merger decisions. Mergers in this model are a form of arbitrage by rational managers operating in inefficient markets. And stock acquisitions are used especially by overvalued bidders who expect to see negative long-run returns on their shares (when the market corrects for the overvaluation), but are attempting to make these returns less negative. On this argument, firms have a powerful incentive to get their equity overvalued - even through earnings manipulation - so that they can make acquisitions through stock. The pattern of abnormal stock price performance surrounding an all-share offer revealed that acquiring companies succeeded in their goal of inflating their share price during the periods in the run-up to a bid, and that the market was not able to see through the accounting distortions. In other words, the market was misled in that it inefficiently priced value-irrelevant discretionary accruals and unintentionally rewarded firms for engaging in earnings management. Concurrently, the evidence on the negative post-merger performance called once again into question the efficient market hypothesis: the market did not fully anticipate the reversing implications of the high discretionary accruals observed in the pre-announcement period, and responded as if surprised when seemingly predictable earnings reversals occurred in the following years.

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34

Table 1 Industry and event year distribution for a sample of 147 UK-listed acquirers undertaking share swap deals between 1st January 1997 and 31st December 2001 Panel A: Distribution by industry (SIC Classification)

SIC Code Description No of

companies

10 Metal mining 2 13 Oil and gas extraction 5 14 Mining and quarrying of nonmetallic minerals, except fuels 1 15 Building construction-general contractors and operative builders 3 20 Food and kindred products 3 21 Tobacco products manufacturing 1 22 Textile mill products manufacturing 1 26 Paper and allied products manufacturing 1 27 Printing, publishing and allied industries 4 28 Chemicals and allied products manufacturing 15 29 Petroleum refining and related industries 1 30 Rubber and miscellaneous plastics products manufacturing 1 32 Stone, clay, glass and concrete products manufacturing 1 33 Primary metal industries manufacturing 1 34 Fabricated metal products, except machinery and transportation equipment 1 35 Industrial and commercial machinery and computer equipment 1

36 Electronic and other electrical equipment and components, except computer equipment 1

37 Transportation equipment manufacturing 2

38 Measuring, analyzing and controlling instruments; photographic, medical and optical goods; watches and clocks manufacturing 5

41 Local and suburban transit and interurbain highway passenger transportation 2 47 Transportation services 3 48 Communications 16 49 Electric, gas and sanitary services 1 50 Wholesale trade, durable goods 2 51 Wholesale trade, nondurable goods wholesale dealing in 2

52 Building materials, hardware, garden supply, and mobile home dealers wholesale dealing in 1

53 General merchandise stores 1 54 Food stores 4 57 Home furniture, furnishings, and equipment stores 5 58 Eating and drinking places 4 59 Miscellaneous retail 3 70 Hotels, rooming houses, camps, and other lodging places 2 73 Business services 23 75 Automotive repair, services, and parking 1 78 Motion pictures 2 79 Amusement and recreation services 4 80 Health services 1 82 Educational services 1 87 Engineering, accounting, research, management, and related services 3 95 Administration of environmental quality and housing programs 1

N/A 15 Total 147

35

Panel B: Distribution by year 1997 1998 1999 2000 2001 TotalNo of Share Deals 41 40 38 49 8 176Total No of Deals 970 1,204 1,083 1,144 863 5,264% of Share Deals 4.23 3.32 3.51 4.28 0.93 3.34Value of Share deals (£m) 12,276.46 36,682.36 34,074.34 220,494.68 1,600.15 305,127.99Total Value of Deals (£m) 46,005.00 84,442.00 137,356.00 288,201.00 70,467.00 626,471.00% of Share Deals 26.69 43.44 24.81 76.51 2.27 48.71

% of Share Deals (Value in £m)

26.69

43.44

24.81

76.51

2.271997 1998 1999 2000 2001

% of Share Deals (Value in £m)

% of Share Deals (Number)

4.23

3.32 3.51

4.28

0.93

1997 1998 1999 2000 2001

% of Share Deals (Number)

Source of data on national totals: Office for National Statistics (ONS): Time-series data on mergers and acquisitions

36

Table 2

Analysis of how the final sample size was obtained

No of Deals No of Companies

Initial Sample 176 147

Reverse Takeovers 12 11

No data a 57 47

No date 30 22

Industry portfolio with fewer than 6 companies 16 13

Other exclusions b 13 12

Final Sample 48 42

a By lack of full accounting data we refer to the following instances: the company did not have any

data at all on DATASTREAM/WORLDSCOPE, the company did not have data for all event years,

and/or the company did not have all the necessary data to calculate the various accrual measures (for

example the figures for PPE, ΔREV, or depreciation could be missing). b Other exclusions refer to: (1) Following Louis (2004) and Kothari et al. (2005) observations in which

the absolute value of total accruals scaled by lagged total assets are greater than one are deleted. (2)

Observations where the event year coincides with other events (such as a reverse takeover, a greater

cash deal) that could confound the results of accrual tests are also deleted.

37

Table 3 Descriptive statistics for a sample of 42 UK-listed acquirers undertaking share swap deals between 1st January 1997 and 31st December 2001 Mean Median Std.Dev. Min. Max.Acquirer’s Turnover (£m) 1,123.48 303.26 1,589.39 0.70 6,409.40Acquirer’s Earnings (£m) 131.22 16.02 294.63 -273.00 1,134.00Acquirer’s Assets (£m) 1,194.41 226.58 1,795.71 9.66 7,806.00Acquirer’s Market Value (£m) 3,019.69 403.30 6,469.67 10.85 35,612.18Acquirer’s Return on Assets (%) 8.54 11.06 17.05 -49.85 38.62Acquirer’s Book-to-Market Ratio 0.32 0.23 0.39 -0.30 1.62Relative Size 0.84 0.47 1.11 0.04 4.85Deal Value (£m) 4,267.77 239.63 19,708.61 2.60 126,955.004-Week Premium (%) 37.10 33.85 35.45 -35.60 134.30 No of

companies %

Cross-border Deals 13 30.95 Industry-related Deals 31 73.81 Hostile Bids 1 2.38 Contested Bids 3 7.14

Turnover, earnings, assets and market value are the acquirer’s net sales, earnings before interest and tax, total assets and market capitalisation the year before the deal is announced (Y0 as defined in section 3 (ii)). The corresponding WORLDSCOPE items are WC01001, WC18191, WC02999, and WC08001 respectively. Return on Assets is computed as Earnings Before Interest and Tax (WC18191) at Year 0 over the average of opening and closing Total Assets (WC02999). Book-to-Market Ratio is defined as the book value of common equity (WC03501) over the market value of common equity of the acquirer the year before the merger announcement (Year 0). Relative size captures the size of the target relative to the acquirer and is defined as the ratio of the target’s assets to the acquirer’s assets the year before the deal announcement. Deal value is the total consideration paid for the target company as reported in Acquisitions Monthly. Four-week premium is the percentage premium paid by the acquirer with respect to the target’s share price four weeks before the deal announcement. A cross-border deal (as opposed to a domestic deal) is one where the target is a foreign company (either publicly traded or privately owned). An industry-related deal (also referred to as an horizontal deal as opposed to a conglomerate deal) is one where the acquirer and the target have the same two-digit SIC Code. A hostile bid (as opposed to a friendly bid) is one where the target company opposes the acquirer’s approach. A contested bid is one where a rival bidder is involved in the process of the negotiations.

38

Announcement of a pending/agreed merger

Merger completed

EA-1 EA0 EA1

Year 1Year 0Year -1

Announcement of a pending/agreed merger

Merger completed

EA0 EA01 EA1EA-1

Year 1Year 01Year 0Year -1

EA: Earnings Announcement

Table 4: Identifying manipulation years

39

Table 5 Descriptive statistics for the estimated regressions

under the cross-sectional standard/modified-Jones model Balance Sheet Approach

Total Accruals Mean Median Std. Dev. Min Max a 0.0101 0.0209 0.0857 -0.3302 0.1933 (0.1597) (0.1043) b1 0.0298* 0.0086 0.1746 -0.4336 0.5104 (0.0744) (0.2874) b2 -0.0867*** -0.0965*** 0.1116 -0.3004 0.3916 (0.0000) (0.0000) R-sq. 0.2391 0.2001 0.1967 0.0070 0.8630 Adj. R-sq. 0.1139 0.0440 0.2125 -0.2114 0.8326 No of obs. 21 16 13.98 6 89 Working Capital Accruals Mean Median Std. Dev. Min Max a 0.0038 0.0070* 0.0323 -0.1333 0.0705 (0.1586) (0.0744) b1 0.0422** 0.0163* 0.1724 -0.2921 0.7577 (0.0200) (0.0974) R-sq. 0.1301 0.0588 0.1578 0.0000 0.7808 Adj. R-sq. 0.0627 0.0055 0.1685 -0.1767 0.7589 No of obs. 21 16 13.98 6 89

Parametric (t-test for the means) and non-parametric (Wilcoxon signed-ranks test for the medians) tests are reported. All tests are one-tailed and the corresponding p-values are given in parenthesis. Significant results are marked in bold: * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

40

Table 6

Descriptive statistics for the estimated regressions under the cross-sectional standard/modified-Jones model

Cash Flow Approach Total Accruals Mean Median Std. Dev. Min Max a 0.0195** 0.0298** 0.0921 -0.3906 0.2184 (0.0312) (0.0109)

b1 0.0618** 0.0497*** 0.2568 -1.1253 1.5058 (0.0172) (0.0007)

b2 -0.0503*** -0.0457*** 0.1120 -0.3712 0.3824 (0.0001) (0.0000) R-sq. 0.2766 0.1970 0.2231 0.0005 0.9390 R-sq. Adj. 0.1642 0.1152 0.2478 -0.2583 0.9084 No of obs. 21 16 13.87 6 87 Working Capital Accruals Mean Median Std. Dev. Min Max a 0.0403*** 0.0421*** 0.0311 -0.0747 0.1421 (0.0000) (0.0000)

b1 0.0766*** 0.0592*** 0.2237 -0.4622 1.5169 (0.0015) (0.0001) R-sq. 0.1730 0.1100 0.1820 0.0000 0.8133 R-sq. Adj. 0.1103 0.0514 0.1976 -0.2254 0.7977 No of obs. 21 16 13.87 6 87

Parametric (t-test for the means) and non-parametric (Wilcoxon signed-ranks test for the medians) tests are reported. All tests are one-tailed and the corresponding p-values are given in parenthesis. Significant results are marked in bold: * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

41

Table 7

Year -1 Year 0 Year 01 Year 1 Year -1 Year 0 Year 01 Year 1

Mean -0.0032 0.0124 -0.0911 -0.0927*** 0.0302* 0.0319 0.1189 0.0097(0.4281) (0.1549) (0.1860) (0.0039) (0.0744) (0.1342) (0.1616) (0.3490)

Median -0.0038 0.0195 0.0156 -0.0761*** 0.0353* 0.0331 0.0244 -0.0064(0.6571) (0.2324) (0.8785) (0.0025) (0.0985) (0.1012) (0.2604) (0.5818)

Std. Dev. 0.1124 0.0780 0.3067 0.2145 0.1315 0.1822 0.3339 0.1586Min -0.2500 -0.1872 -0.9448 -0.6450 -0.2966 -0.3990 -0.1410 -0.3713Max 0 3007 0 2235 0 0866 0 6712 0 4024 0 9564 1 0003 0 5421

Event period discretionary accruals derived from the cross-sectional standard-Jones model (Successful Bids)

Balance Sheet Approach Cash Flow Approach

Total Accruals

Max 0.3007 0.2235 0.0866 0.6712 0.4024 0.9564 1.0003 0.5421

Mean -0.0100 0.0276** -0.0692 -0.0993*** 0.0295* 0.0306** 0.0647 0.0196 (0.2835) (0.0192) (0.1923) (0.0006) (0.0698) (0.0372) (0.1550) (0.1840)

Median -0.0177 0.0202* 0.0161 -0.0808*** 0.0202 0.0304* 0.0317 -0.0001 (0.2228) (0.0633) (0.9594) (0.0008) (0.1279) (0.0687) (0.3743) (0.7119)

Std. Dev. 0.1125 0.0836 0.2394 0.1863 0.1251 0.1070 0.1791 0.1378Min -0.2517 -0.1141 -0.7118 -0.6486 -0.2976 -0.1445 -0.1119 -0.2772Max 0.2918 0.3347 0.0826 0.4346 0.4232 0.5129 0.5141 0.4721

Working Capital Accruals

Results for the cross-sectional standard-Jones model under the balance sheet (cash flow) approach are based on the estimated discretionary accruals of 42 (41) UKpublicly traded companies undertaking share swap M&A during the period 1997-2001. Significance is based on t-tests for the means and on Wilcoxon signed-rankstests for the medians. Significant results are marked in bold and the corresponding p-values are given in parenthesis; ***, **, and * indicate 1%, 5%, and 10% level ofsignificance respectively.Year 0 (-1) is the first (second) year with an earnings release preceding the announcement of the deal (pending or agreed). Year 1 is the first year with an earningsrelease following the announcement of the deal (for most sample companies this is also the first earnings release following the deal completion). Year 01 applies onlyto those companies which had an earnings release between the announcement of the deal and its completionto those companies which had an earnings release between the announcement of the deal and its completion.

42

Table 8

Year -1 Year 0 Year 01 Year 1 Year -1 Year 0 Year 01 Year 1

Mean -0.0008 0.0167* -0.0736 -0.0899*** 0.0296* 0.0327* 0.1109 0.0085 (0.4808) (0.0881) (0.2205) (0.0043) (0.0741) (0.0945) (0.1586) (0.3636)

Median 0.0016 0.0207 0.0161 -0.0770*** 0.0351 0.0329** 0.0234 -0.0050 (0.7785) (0.1384) (0.8785) (0.0029) (0.1154) (0.0413) (0.2604) (0.5730)

Std. Dev. 0.1106 0.0786 0.2888 0.2111 0.1285 0.1567 0.3118 0.1554Min -0.2394 -0.1869 -0.8705 -0.6862 -0.2628 -0.3697 -0.1492 -0.3647M 0 3287 0 2590 0 1195 0 6773 0 4074 0 7751 0 9166 0 5433

Event period discretionary accruals derived from the cross-sectional modified-Jones model (Successful Bids)

Total Accruals

Balance Sheet Approach Cash Flow Approach

Max 0.3287 0.2590 0.1195 0.6773 0.4074 0.7751 0.9166 0.5433

Mean -0.0069 0.0319** -0.0421 -0.0754*** 0.0295* 0.0337** 0.0643 0.0186 (0.3455) (0.0103) (0.2671) (0.0047) (0.0647) (0.0161) (0.1400) (0.1918)

Median -0.0174 0.0219** 0.0255 -0.0774** 0.0283 0.0311** 0.0385 -0.0008 (0.2965) (0.0362) (0.7520) (0.0033) (0.1311) (0.0207) (0.3743) (0.7313)

Std. Dev. 0.1112 0.0859 0.2168 0.1905 0.1219 0.0972 0.1664 0.1350Min -0.2386 -0.1000 -0.6570 -0.6794 -0.2623 -0.1456 -0.0933 -0.2708Max 0.3279 0.3382 0.1127 0.4386 0.4284 0.4384 0.4798 0.4712

Working Capital Accruals

Results for the cross-sectional modified-Jones model under the balance sheet (cash flow) approach are based on the estimated discretionary accruals of 42 (41) UKpublicly traded companies undertaking share swap M&A during the period 1997-2001. Significance is based on t-tests for the means and on Wilcoxon signed-rankstests for the medians. Significant results are marked in bold and the corresponding p-values are given in parenthesis; ***, **, and * indicate 1%, 5%, and 10% level ofsignificance respectively.Year 0 (-1) is the first (second) year with an earnings release preceding the announcement of the deal (pending or agreed). Year 1 is the first year with an earningsrelease following the announcement of the deal (for most sample companies this is also the first earnings release following the deal completion). Year 01 applies onlyt th i hi h h d i l b t th t f th d l d it l tito those companies which had an earnings release between the announcement of the deal and its completion.

43

Table 9Analysis of long-run returns relative to bid month

Raw returns:Mean

Median

Benchmark:

Size/BTM EW VW EW VW EW VW EW VW EW VW EW VWPortfolios

Mean 30.08*** 23.27*** 14.56* 28.24*** 64.40*** 69.06*** 24.97*** 19.06*** 11.10* 20.55*** 36.07*** 39.60***[0.0006] [0.0032] [0.0654] [0.0046] [0.0030] [0.0030] [0.0003] [0.0017] [0.0693] [0.0073] [0.0002] [0.0003]

Median 16.92*** 10.08** 20.170* 14.17** 19.21** 17.08** 17.71*** 9.30** 23.49 16.97** 24.06*** 24.12***[0.0040] [0.0330] [0.0926] [0.0218] [0.0135] [0.0106] [0.0017] [0.0117] [0.1095] [0.0126] [0.0013] [0.0009]

Market Return

Mean 28.23*** 18.83** 15.29* 23.67** 63.52*** 59.32*** 23.90*** 14.69** 10.86* 16.16** 34.76*** 30.84***[0.0018] [0.0178] [0.0543] [0.0135] [0.0049] [0.0099] [0.0007] [0.0153] [0.0699] [0.0223] [0.0005] [0.0030]

Median 14.38** 5.13 20.06* 12.47* 12.90** 1.82 14.03*** 7.99* 20.90 14.87** 22.91*** 17.39**[0.0151] [0.1522] [0.0985] [0.0748] [0.0290] [0.1928] [0.0041] [0.0748] [0.1012] [0.0453] [0.0023] [0.0263]

27.43*** 44.58*** [0.0019] [0.0000]

27.65***[0.0007]

55.33***[0.0000][0.0000]

19.98***[0.0002]

27.68***185.30*** [0.0000]132.55***[0.0000]

135.12*** [0.0000]125.15*** [0.0000]

132.09*** [0.0000]

117.42*** [0.0000]

-24m to -1mCAR (%)Buy-and-Hold (%)

-24m to -13m -13m to -1m -24m to -1m -24m to -13m -13m to -1m

44

Table 10Analysis of long-run returns relative to completion month

Raw returns:Mean

Median

Benchmark:

Size/BTM EW VW EW VW EW VW EW VWPortfolios

Mean -1.38 0.65 1.02 -0.45 -6.01 -19.96** -5.10 -27.25***[0.4262] [0.4634] [0.5451] [0.4800] [0.3001] [0.0232] [0.3027] [0.0022]

Median -5.36 -4.08 -0.37 -3.49 -20.12 -31.69** -4.59 -43.02***[0.6481] [0.5278] [0.6845] [0.3647] [0.2741] [0.0136] [0.5326] [0.0019]

Market Return

Mean -6.06 -3.03 -11.90* -4.15 -26.39*** -15.07* -25.81*** -6.59[0.1798] [0.3348] [0.0564] [0.2923] [0.0047] [0.0513] [0.0055] [0.2450]

Median -6.21 -4.93 -19.17** -8.20 -30.33** -26.76* -28.13*** -8.41[0.1184] [0.2207] [0.0237] [0.2064] [0.0100] [0.0660] [0.0021] [0.2143]

Raw returns:Mean

Median

Benchmark:

Size/BTM EW VW EW VW EW VW EW VWPortfolios

Mean -3.14 -1.88 -0.11 -2.88 -8.43 -23.31** -3.37 -25.28**[0.3138] [0.6155] [0.4942] [0.3605] [0.2498] [0.0284] [0.4003] [0.0372]

Median -2.64 -1.87 6.29 5.48 -6.70 -19.54* 8.46 -16.94[0.8408] [0.8307] [0.5730] [0.8307] [0.7415] [0.0733] [0.7410] [0.1098]

Market Return

Mean -6.56 -4.05 -8.63 -2.32 -23.45** -12.13 -23.18** -0.55[0.1346] [0.2624] [0.1201] [0.3818] [0.0255] [0.1558] 0.0386 [0.4836]

Median -1.80 -1.74 -3.35 3.36 -19.13* -11.39 -27.06* 9.67[0.2357] [0.5045] [0.2111] [0.9432] [0.0928] [0.4414] 0.0754 [0.7410]

[0.7706] [0.6270] [0.3622] [ 0.4476]-1.15 6.45 -5.77 -10.48

[ 0.4252] [0.4016] [ 0.0900 ] [0.1009]1.16 2.01 -16.46* -18.09

CAR (%)+1m to +6m +1m to +12m +1m to +24m +1m to +36m

[0.0000] [0.0000] [0.0000]64.27***[0.0000]

96.02*** 99.66*** 77.01*** [0.0000] [0.0000] [0.0000]

78.45***[0.0000]

102.20*** 100.24*** 81.85***

Buy-and-Hold (%)+1m to +6m +1m to +12m +1m to +24m +1m to +36m

45

Table 11Effect of the magnitude of earnings managementPanel A:

DWCA EW VW EW VW EW VW-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

Long-run returns relative to bid month

DWCA EW VW EW VW EW VWMean

EM Quartiles Q1 & Q2 -0.0270*** 31.32** 23.72** -2.73 14.27 39.08* 45.33**

[0.0013] [ 0.0117] [0.0280] [0.4158] [0.1110] [0.0650] [0.0398][0.0013] [ 0.0117] [0.0280] [0.4158] [0.1110] [0.0650] [0.0398]

EM Quartiles Q3 & Q4 0.0909*** 28.84** 22.81** 31.84** 42.21** 89.73** 92.79**

[0.0000] [0.0120] [0.0306] [0.0129] [0.0111] [0.0118] [0.0171]Diff. 0.1179*** -2.48 -0.91 34.57** 27.94* 50.65 47.46

[0.0000] [0.4435] [0.4781] [0.0332] [0.0902] [0.1298] [0.1631]Median

EM Quartiles Q1 & Q2 -0.0212*** 18.22* 9.46* 7.16 12.96 7.92 4.06

[0.0046] [0.0792] [0.1396] [0.7943] [0.3754] [0.3392] [0.2586]

EM Quartiles Q3 & Q4 0.0571*** 15.87* 10.70 23.65** 33.19** 28.62** 23.12**

[0.0001] [0.0190] [0.1060] [0.0386] [0.0250] [0.0143] [0.0157]

Q1 is the quartile with the most conservative earnings … Q4 is the quartile with the most aggressive earnings management (as measured bydiscretionary working capital accruals at event Year 0, DWCA). The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio. ***, **, * denote significance at the 1%, 5%, and 10%level respectively. Parametric tests for the means are based on t-tests. Non-parametric tests for the medians are based on Wilcoxon signed-rankstests

Q1 is the quartile with the most conservative earnings … Q4 is the quartile with the most aggressive earnings management (as measured bydiscretionary working capital accruals at event Year 0, DWCA). The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio. ***, **, * denote significance at the 1%, 5%, and 10%level respectively. Parametric tests for the means are based on t-tests. Non-parametric tests for the medians are based on Wilcoxon signed-rankstests.

46

Table 11 (continued…)Effect of the magnitude of earnings management

Panel B: Long-run returns relative to completion monthPanel B:

DWCA EW VW EW VW EW VW EW VWMeanEM Quartiles Q1 & Q2

+1m to +6mLong-run returns relative to completion month

+1m to +36m+1m to +24m+1m to +12m

Q1 & Q2 -0.0270*** 10.76 16.84* 16.98 18.29 20.51 4.87 13.78 -9.54[0.0013] [0.1918] [0.0668] [0.1352] [0.1052] [0.1373] [0.3854] [0.1431] [0.2501]

EM Quartiles Q3 & Q4 0.0909*** -13.52* -15.53** -14.93* -19.19** -28.71** -41.07*** -23.98* -44.96***

[0.0000] [ 0.0511] [ 0.0326] [0.0555] [ 0.0278] [ 0.0206] [ 0.0001] [ 0.0520] [ 0.0002] [0.0000] [ 0.0511] [ 0.0326] [0.0555] [ 0.0278] [ 0.0206] [ 0.0001] [ 0.0520] [ 0.0002] Diff. 0.1179*** -24.28* -32.37** -31.91** -37.48** -49.22** -45.94** -37.76** -35.42**

[0.0000] [0.0507] [0.0104] [0.0383] [0.0171] [0.0178] [0.0110] [0.0260] [0.0230]MedianEM Quartiles Q1 & Q2 0 0212*** 11 04 13 04 12 48 10 23 6 67 4 24 3 22 4 30Q1 & Q2 -0.0212*** 11.04 13.04 12.48 10.23 6.67 -4.24 3.22 -4.30

[0.0046] [0.2305] [0.1219] [0.3570] [0.4979] [0.6529] [0.8684] [0.4348] [0.2868]EM Quartiles Q3 & Q4 0.0571*** -14.08* -7.99** -8.69 -15.74** -26.98* -43.59*** -23.35 -49.74***

[0.0001] [0.0680] [0.0325] [0.1138] [0.0386] [0.0522] [0.0015] [0.1128] [0.0016][ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ]

Q1 is the quartile with the most conservative earnings … Q4 is the quartile with the most aggressive earnings management (asmeasured by discretionary working capital accruals at event Year 0, DWCA). The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio. ***, **, * denotesignificance at the 1%, 5%, and 10% level respectively. Parametric tests for the means are based on t-tests. Non-parametric testsfor the medians are based on Wilcoxon signed ranks tests

Q1 is the quartile with the most conservative earnings … Q4 is the quartile with the most aggressive earnings management (asmeasured by discretionary working capital accruals at event Year 0, DWCA). The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio. ***, **, * denotesignificance at the 1%, 5%, and 10% level respectively. Parametric tests for the means are based on t-tests. Non-parametric testsfor the medians are based on Wilcoxon signed-ranks tests.

47

Table 12Industry effects

Panel A: Long-run returns relative to bid month-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

DWCA EW VW EW VW EW VWMeanServices 0.0441** 55.51*** 44.36*** 42.44** 67.31*** 148.38*** 159.14***

[0.0478] [0.0017] [0.0066] [0.0131] [0.0024] [0.0029] [0.0033]

-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

Heavy Industry 0.0236* 12.79* 8.93 -4.41 1.67 7.29 7.81[0.0607] [0.0594] [0.1144] [0.3157] [0.3998] [0.221] [0.1686]

Diff. -0.0205 -42.72** -35.43** -46.85** -65.64*** -141.09***-151.33***[0.2427] [0.0130] [0.0273] [0.0123] [0.0033] [0.0043] [0.0047]

MedianMedianServices 0.0166 41.57*** 43.09** 49.72** 47.08** 102.95*** 133.13**

[0.1128] [0.0056] [0.0217] [0.0245] [0.0113] [0.0065] [0.0129]Heavy Industry 0.0289 8.82 2.17 7.16 9.50 7.92 12.70

[0.1500] [0.2109] [0.4758] [0.9678] [0.6377] [0.5272] [0.3002]

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t tests

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

48

Table 12 (continued…)Industry effects

Panel B: Long-run returns relative to completion month+1m to +6m +1m to +12m +1m to +24m +1m to +36m

DWCA EW VW EW VW EW VW EW VWMeanServices 0.0441** -3.76 -8.00 -4.66 -18.21** -12.46 -42.72*** -14.78 -41.97***

[0.0478] [0.3337] [0.1793] [0.2435] [0.0154] [0.1925] [0.0027] [0.1430] [0.0053]

+1m to +6m +1m to +12m +1m to +24m +1m to +36m

Heavy Industry 0.0236* 0.24 6.54 4.89 11.63 -2.23 -6.11 0.89 -18.14*[0.0607] [0.4916] [0.2673] [0.3694] [0.2005] [0.4476] [0.3194] [0.4741] [0.0635]

Diff. -0.0205 4.00 14.54 9.55 29.84** 10.23 36.61** 15.67 23.83*[0.2427] [0.3885] [0.8579] [0.2762] [0.0321] [0.3202] [0.0258] [0.2078] [0.0980]

MedianMedianServices 0.0166 -5.52 -8.75* -8.69 -29.62** -19.75 -51.78** -16.80 -49.74**

[0.1128] [0.5862] [0.0759] [0.356] [0.0352] [0.1401] [0.0110] [0.2489] [0.0131]Heavy Industry 0.0289 -5.20 2.40 3.73 -0.31 -20.12 -20.50 5.63 -35.44**

[0.1500] [0.8612] [0.7982] [0.9464] [0.7164] [0.6482] [0.3458] [0.9308] [0.0420]

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

49

Table 13Time effects

Panel A: Long-run returns relative to bid month-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

DWCA EW VW EW VW EW VWMean1997-1998 0.0151 19.77* 16.48* 14.51* 13.18* 32.32** 26.46**

[0.1396] [0.0770] [0.0927] [0.0626] [0.0870] [0.0212] [0.0395]

-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

1999-2001 0.0403** 35.23*** 26.66*** 14.58 35.77** 80.45*** 90.36***[0.0201] [0.0019] [0.0096] [0.1461] [0.0110] [0.0096] [0.0072]

Diff. 0.0252 15.46 10.18 0.07 22.59 48.13* 63.90**[0.1399] [0.1872] [0.2634] [0.4985] [0.1001] [0.0909] [0.0476]

MedianMedian1997-1998 0.0314 11.85 6.01 20.72 21.30 12.33** 15.62*

[0.3305] [0.1578] [0.2455] [0.124] [0.1240] [0.0303] [0.0640]1999-2001 0.0178* 18.10*** 10.08* 14.98 13.55* 23.06* 19.38*

[0.0835] [0.0088] [0.0558] [0.3164] [0.0720] [0.0877] [0.0529]

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

50

Table 13 (continued…)Time effects

Panel B: Long-run returns relative to completion month+1m to +6m +1m to +12m +1m to +24m +1m to +36m

DWCA EW VW EW VW EW VW EW VWMean1997-1998 0.0151 -12.43 -12.13 -5.53 -1.06 -41.88* -31.75* -44.54** -43.60***

[0.1396] [0.1444] [0.1638] [0.3862] [0.4759] [0.0710] [0.0692] [0.0182] [0.0042]

+1m to +6m +1m to +12m +1m to +24m +1m to +36m

1999-2001 0.0403** 4.14 7.04 4.30 -0.14 11.08 -14.30* 13.77* -19.43*[0.0201] [0.3329] [0.2123] [0.3334] [0.4945] [0.1430] [0.0993] [0.0778] [0.0507]

Diff. 0.0252 16.57 19.17 9.83 0.91 52.96** 17.45 58.31*** 24.17*[0.1399] [0.1343] [0.1024] [0.3237] [0.4821] [0.0412] [0.2254] [0.0064] [0.0900]

MedianMedian1997-1998 0.0314 -15.90 0.58 -10.45 -3.54 -50.88** -39.81** -53.59* -49.20**

[0.3305] [0.3003] [0.5098] [0.3627] [0.6832] [0.0342] [0.0342] [0.0505] [0.0128]1999-2001 0.0178* -1.51 -6.16 2.21 -3.49 6.71 -12.96 5.63 -35.44**

[0.0835] [0.8554] [0.7156] [0.8022] [0.4662] [0.4593] [0.1578] [0.2604] [0.0480]

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

51

Table 14Size effects

Panel A: Long-run returns relative to bid month-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

DWCA EW VW EW VW EW VWMeanSmall firms 0.0154 15.51 12.48 4.89 16.83* 35.76 42.22

[0.1502] [0.1087] [0.1628] [0.3482] [0.0897] [0.1528] [0.1352]

-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

Big firms 0.0485** 44.65*** 34.05*** 24.22* 39.64** 93.05*** 95.90***[0.0198] [0.0005] [0.0016] [0.0525] [0.0138] [0.0017] [0.0019]0.0331 29.14** 21.57* 19.33 22.81 57.29 53.68

[0.1085] [0.0450] [0.0933] [0.1558] [0.1377] [0.1003] [0.1320]MedianMedianSmall firms 0.0034 -4.62 -4.78 8.09 13.83 7.97 5.97

[0.4761] [0.5663] [0.9584] [0.4342] [0.1592] [0.4342] [0.2891]Big firms 0.0411** 28.31*** 20.13*** 21.86 14.20* 59.41*** 33.63**

[0.0325] [0.0012] [0.0051] [0.1138] [0.0537] [0.0078] [0.0117]

Size is proxied by market capitalisation at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Size is proxied by market capitalisation at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Size is proxied by market capitalisation at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

52

Table 14 (continued…)Size effects

Panel B: Long-run returns relative to completion month+1m to +6m +1m to +12m +1m to +24m +1m to +36m

DWCA EW VW EW VW EW VW EW VWMeanSmall firms 0.0154 -0.95 5.75 5.32 12.00 -1.73 -5.20 2.71 -17.00

[0.1502] [0.4711] [0.3222] [0.3747] [0.2304] [0.4669] [0.3767] [0.4373] [0.1393]

+1m to +6m +1m to +12m +1m to +24m +1m to +36m

Big firms 0.0485** -1.82 -4.45 -3.27 -12.90** -10.23 -33.95*** -12.04 -36.36***[0.0198] [0.4059] [0.2727] [0.3364] [0.0472] [0.1988] [0.0020] [0.1444] [0.0011]0.0331 -0.87 -10.20 -8.59 -24.90* -8.50 -28.75* -14.75 -19.36

[0.1085] [0.4769] [0.2396] [0.3195] [0.0837] [0.3611] [0.0730] [0.2353] [0.1482]MedianMedianSmall firms 0.0034 -6.97 -8.00 -2.19 4.17 -2.46 -6.82 0.18 -31.98

[0.4761] [0.7677] [0.9308] [0.9861] [0.7413] [0.5277] [0.4724] [0.9176] [0.1337]Big firms 0.0411** 2.17 -3.83 1.45 -5.04* -20.12 -36.88*** -7.24 -45.69***

[0.0325] [0.8757] [0.3945] [0.6143] [0.0680] [0.1590] [0.0062] [0.3061] [0.0043]

Size is proxied by market capitalisation at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Size is proxied by market capitalisation at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Size is proxied by market capitalisation at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

53

Table 15Book-to-market effects

Panel A: Long-run returns relative to bid month-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

DWCA EW VW EW VW EW VWMeanValue firms 0.0078 23.99** 18.77** -5.94 4.67 19.08 21.80

[0.2095] [0.0244] [0.0490] [0.2953] [0.3069] [0.1366] [0.1013]

-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

Glamour firms 0.0560** 36.17*** 27.76** 35.05** 51.80*** 109.72*** 116.33***[0.0149] [0.0056] [0.0173] [0.0119] [0.0036] [0.0054] [0.0066]0.0482** 12.18 8.99 40.99** 47.13** 90.64** 94.53**[0.0362] [0.2425] [0.2926] [0.0142] [0.0111] [0.0212] [0.0247]

MedianMedianValue firms 0.0060 8.82 -3.15 7.16 12.17 7.97 12.70

[0.4549] [0.1060] [0.4342] [0.8484] [0.7943] [0.3570] [0.2586]Glamour firms 0.0506** 26.34** 15.95* 33.49** 34.54*** 55.35** 24.65**

[0.0457] [0.0190] [0.0680] [0.0208] [0.0078] [0.0157] [0.0157]

Glamour/ value classifications are based on the book-to-market ratio at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.*** ** * denote significance at the 1% 5% and 10% level respectively

Glamour/ value classifications are based on the book-to-market ratio at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Glamour/ value classifications are based on the book-to-market ratio at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

54

Table 15 (continued…)Book-to-market effects

Panel B: Long-run returns relative to completion month+1m to +6m +1m to +12m +1m to +24m +1m to +36m

DWCA EW VW EW VW EW VW EW VWMeanValue firms 0.0078 -0.71 -1.38 1.20 -0.29 -16.98 -16.55 -9.91 -16.91

[0.2095] [0.4681] [0.4379] [0.4640] [0.4903] [0.1816] [0.1408] [0.2836] [0.1458]

+1m to +6m +1m to +12m +1m to +24m +1m to +36m

Glamour firms 0.0560** -2.05 2.69 0.85 -0.61 3.15 -22.86** -0.82 -36.44***[0.0149] [0.4335] [0.4077] [0.4735] [0.4824] [0.4171] [0.0477] [0.4709] [0.0008]0.0482** -1.34 4.07 -0.35 -0.32 20.13 -6.31 9.09 -19.53[0.0362] [0.4643] [0.3889] [0.4922] [0.4929] [0.1985] [0.3758] [0.3287] [0.1471]

MedianMedianValue firms 0.0060 -5.52 -3.83 3.96 -0.31 -34.60* -31.68* -11.04 -45.48

[0.4549] [0.8757] [0.8484] [0.9584] [0.8484] [0.0684] [0.0615] [0.4080] [0.1337]Glamour firms 0.0506** 2.17 -4.33 -8.69 -15.74 1.61 -43.59* 5.28 -40.53***

[0.0457] [0.6389] [0.3570] [0.6143] [0.3754] [0.7652] [0.0731] [0.9826] [0.0038]

Glamour/ value classifications are based on the book-to-market ratio at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.*** ** * d t i ifi t th 1% 5% d 10% l l ti l

Glamour/ value classifications are based on the book-to-market ratio at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Glamour/ value classifications are based on the book-to-market ratio at event Year 0.Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

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Table 16Takeover premium effects

Panel A: Long-run returns relative to bid month-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

DWCA EW VW EW VW EW VWMeanLow premium 0.0224* 21.80** 17.36** 1.19 16.06 27.18 35.87*

[0.0907] [0.0241] [0.0383] [0.4750] [0.1984] [0.1428] [0.0866]

-24m to -13m (%) -13m to -1m (%) -24m to -1m (%)

High premium 0.0613*** 49.47*** 39.59** 28.17*** 43.40*** 119.66*** 121.90***[0.0092] [0.0030] [0.0103] [0.0075] [0.0053] [0.0061] [0.0098]0.0389* 27.67* 22.23 26.98 27.34 92.48** 86.03*[0.0909] [0.0762] [0.1141] [0.1111] [0.1302] [0.0357] [0.0603]

MedianMedianLow premium 0.0110 17.05* 10.08* 7.63 -0.32 -6.20 11.20

[0.1841] [0.0582] [0.0854] [0.8446] [0.9826] [0.8446] [0.6791]High premium 0.0315*** 29.89** 23.86** 24.09** 32.04*** 47.04*** 51.73***

[0.0074] [0.0108] [0.0475] [0.0176] [0.0084] [0.0025] [0.0057]

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’s size/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

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Table 16 (continued…)Takeover premium effects

Panel B: Long-run returns relative to completion month+1m to +6m +1m to +12m +1m to +24m +1m to +36m

DWCA EW VW EW VW EW VW EW VWMeanLow premium 0.0224* 6.88 5.16 8.42 3.43 6.10 -11.47 25.30** 0.70

[0.0907] [0.2583] [0.3330] [0.2683] [0.4147] [0.3537] [0.2198] [0.0423] [0.4802]

+1m to +6m +1m to +12m +1m to +24m +1m to +36m

High premium 0.0613*** -5.66 -7.32 -6.33 -14.76* -29.80** -41.05*** -36.98*** -56.73***[0.0092] [0.3032] [0.2589] [0.2482] [0.0570] [0.0253] [0.0005] [0.0053] [0.0001]0.0389* -12.54 -12.48 -14.75 -18.19 -35.90* -29.58* -62.28*** -57.43***[0.0909] [0.2040] [0.2225] [0.1843] [0.1605] [0.0503] [0.0512] [0.0011] [0.0015]

MedianMedianLow premium 0.0110 5.67 -0.60 6.77 -2.72 6.71 -22.02 15.61** -4.57

[0.1841] [0.7112] [0.9479] [0.6475] [0.8446] [0.6874] [0.3812] [0.0355] [0.6378]High premium 0.0315*** -11.39 -11.69 -7.06 -14.51 -34.60** -42.83*** -37.56** -55.56***

[0.0074] [0.4997] [0.2668] [0.4204] [0.1221] [0.0409] [0.0038] [0.0146] [0.0015]

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.*** ** * denote significance at the 1% 5% and 10% level respectively

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

Earnings management is proxied by discretionary working capital accruals (DWCA).The benchmark for long-run returns is the equally-weighted (EW) or the value-weighted (VW) return of each sample firm’ssize/book-to-market matched portfolio.***, **, * denote significance at the 1%, 5%, and 10% level respectively.Parametric tests for the means are based on t-tests.Non-parametric tests for the medians are based on Wilcoxon signed-ranks tests.

57