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Various measures of earnings management used to estimate discretionary accruals and test the research hypotheses of this chapter are presented within this section. In addition, this section will also discuss in detail the regression model employed to investigate the relationship between discretionary accruals and deal premium.

A. Abnormal Accruals Measures of Earnings Management

In line with recent studies on earnings management in M&A (e.g., Louis, 2004; Ball and Shivakumar, 2008; Botsari and Meeks, 2008; Cohen and Zarowin, 2010), two models are adopted in this study to measure accruals earnings management.. These are namely the modified-Jones model (Dechow et al., 1995) and the performance-matched Jones model (Kothari et al., 2005)66_{. Both accruals models were discussed in detail in}

Chapter 4 Data and Research Methods. Abnormal accruals are estimated for event years -2, -1 and 0: the three years preceding a takeover, which are most likely to affect the deal value and premium.67

In this chapter both measures of abnormal accruals are used to estimate accruals earnings management, specifically abnormal total accruals and abnormal working capital accruals. These two proxies are estimated using the balance sheet

66_{The performance-matched Jones model (Kothari et al., 2005), which contains lagged return on assets in}

addition to the change in revenue adjusted for the change in accounts receivables and gross property, plant and equipment, is used to control for extreme performance.

approach and the cash flow approach. Given the fact that the balance sheet abnormal accruals estimates can be biased, accruals obtained from cash flow statement are also used to mitigate measurement error problems (e.g., Hribar and Collins, 2002; Ball and Shivakumar, 2008).

Total accruals and working capital accruals are defined following Botsari and Meeks (2008). Therefore, under the Balance sheet approach,total accruals are defined as the change in non-cash current assets, less the change in current liabilities, excluding the current portion of long-term debt, less depreciation; working capital accruals are defined as the change in non-cash current assets, minus the change in current liabilities. Under the Cash flow approach, total accruals are defined as the difference between income before extraordinary items and discontinued operations, and cash from operations; working capital accruals are the difference between net income before extraordinary items (as reported in the cash flow statement) and operating cash flow (excluding depreciation).

Abnormal accruals are computed as the difference between the actual accruals and the normal component of accruals i.e. estimated non-discretionary accruals. As mentioned before, following prior literature, the normal level of accruals for each two- digit SIC code industry/year portfolio with at least 6 observations is estimated using a control sample (e.g., DeFond and Jiambalvo, 1994; Subramanyam, 1996; Botsari and Meeks, 2008). The control sample consists of all UK publicly listed firms that have the necessary data on Datastream/Worldscope to estimate accruals, but excluding the sample firms which have experienced a takeover event. The industry grouping/event year parameter estimates from the equations of normal accruals are subsequently combined with firm-specific data to generate estimated prediction errors that represent the level of abnormal accruals for each firm. This approach controls for changes in economic conditions that affect total accruals across different industry groupings, but

allows for coefficients to vary over time (DeFond and Jiambalvo, 1994; Cohen and Zarowin, 2010). In order to reduce heteroscedasticity in the data, all variables in the accruals model are scaled by lagged total assets.

The results are generally similar across these two measures of abnormal accruals and across the balance sheet approach and cash flow approach. Therefore, only the results derived from the cross-sectional performance-matched Jones model (Kothari et al., 2005) under the cash flow approach are reported in this study. Based on the previous papers on earnings management in M&A cited above, this study investigates whether the average abnormal accruals are significantly positive or negative for UK targets in the three years preceding a takeover. As a robustness test, the empirical analysis is repeated by using a measure based on the performance-matched abnormal accruals as advanced in Kothari et al. (2005).68

B. Deal Premium Measure

The chapter next examines the hypothesis that the deal premium is negatively related to the targets’ abnormal accruals by regressing the deal premium on abnormal accruals and other control variables:

ܴܲܧܯ =ߚ଴+ߚଵܾܽ_ݓܿܽܿܿ_݌݉ ݂ܿ+ߚଶܵܫܼܧ+ߚଷܵܩܴܱܹ +ߚସܯ ܤܴ+ߚହܴܱܧ+

ߚ଺ܲ݁ݎ_ݏݐ݋ܿ݇+ߚ଻ܣܷܥܶܫܱܰ +ߚ଼ܱܰܰ −ܰܧܩܱܥ+ߚଽܯ ܹ +ߝ௜ (5.1)

68_{As suggested by Kothari et al. (2005), to estimate this additional measure of discretionary accruals first} each M&A firm-year observation is matched with a non-M&A firm-year observation from the same industry grouping based on 2-digit SIC code and year with the closest value of lagged return on assets (+/-20% of sample firm’s return on assets). Then, discretionary accruals for both an M&A firm and a non-M&A firm are computed. Finally, the discretionary accruals for an M&A firm are adjusted by the discretionary accruals for its matched firm. The results are qualitatively similar to those reported in this chapter, but they are not tabulated here for the sake of brevity.

Where:

PREM= bid price as a percentage of the closing price of the target four weeks before the announcement (from Thomson One Banker M&A);

ab_wcacc_pmcf = abnormal working capital accruals estimated using the cross-sectional performance-matched Jones Model under the cash-flow approach.

Controls for firm characteristics:

SIZE= the natural log of market capitalisation;

SGROW= sales growth;

ROE= return on equity;

MBR= market-to-book ratio.

Controls for deal characteristics:

Per_stock= percentage of takeover proceeds paid using equity;

AUCTION = dummy variable which equals 1 if the number of bidders reported by Thomson One Banker M&A is larger than 1, and 0 otherwise;

NON-NEGOC = dummy variable which equals 1 if the takeover deal is classified as hostile by Thomson One Banker M&A, and negotiated otherwise.

Time dummy:

MW = merger wave dummy variable which equals 1 if the deal year falls within the “fifth” merger wave period (1993-2001), and 0 otherwise (1990-1992 and 2002-2008, respectively);

ߝ௜= error term.

The main explanatory variable of interest is the abnormal working capital accruals from the cross-sectional performance-matched Jones Model (Kothari et al., 2005) under the cash-flow approach. Deal premium (PREM)is used as the dependent variable in the model as a proxy for short-run gains the targets’ shareholders receive from a transaction. Specifically, this proxy is an appropriate measure of the abnormal stock return realised by the targets’ shareholders in M&A and the data for the sample was obtained from the Thomson One Banker M&A database.

Controls for targets’ characteristics commonly associated with deal premium in the related literature (e.g., Raman et al., 2013), such as size, sales growth, return on equity and the market to-book ratio are included in the model. The controls for deal characteristics that the empirical and theoretical literatures have found important are also added to the model (e.g., Bargeron et al., 2008; Raman et al., 2013). The natural log of market capitalisation (SIZE) is included to control for targets’ size. The sales growth (GROW) and market-to-book ratio (MBR) are included to control for growth opportunities and informational asymmetry. The ratio of return on equity (ROE) is included to proxy for a firm’s profitability.

Since the impact of discretionary accruals on deal premium can differ across stock-for-stock and cash deals,Per_stockis included in the model to control for variation in the method of payment. As prior research finds that the relationship between deal premium and earnings quality differs across negotiated and non-negotiated deals and deals involving a single bidder and deals involving multiple bidders, as well, controls for the deal strategy and takeover method, dummy variables NON-NEGOC and

AUCTIONrespectively, are added to the model.

As the study period crosses the “fifth” merger wave (1993-2001)69_{, which is} well-documented in the literature (e.g., Owen, 2006; Martynova and Renneboog, 2008), a time dummy is included in the model to control for differences in the level of merger activity over time. Regarding the change in the financial reporting environment from UK GAAP to IFRS (which occurred in 2005 when the UK like all EU listed companies adopted the new standards), an additional time dummy was included initially to the model to control for differences between pre- and post-IFRS, but the coefficient was insignificant and therefore dropped from the model. However, this may not be a serious

69_{Chapter 2 presents an in depth overview of the M&A activity in the UK during the period 1990-2008}

concern within this study as accounting standards, which measure the quality of the disclosure of accounting information and reflect corporate governance, affect M&A activity, but not directly deal premium (Rossi and Volpin, 2004). Furthermore, prior research which investigates the impact of the adoption of IFRS on financial disclosure shows that the UK GAAP (pre-IFRS) was perceived to be generally high, therefore the switch to IFRS was not considered a major vector of improvement in terms of earnings quality (Jeanjean and Stolowy, 2008). Finally, all the targets in the sample are UK publicly listed, thus, no differential impact across companies is expected.