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1 Predicting Australian Takeover Targets: A Logit Analysis Maurice Peat* Maxwell Stevenson* * Discipline of Finance, School of Finance, The University of Sydney Abstract Positive announcement-day adjusted returns to target shareholders in the event of a takeover are well documented. Investors who are able to accurately predict firms that will be the subject of a takeover attempt should be able to earn these excess returns. In this paper a series of probabilistic regression models were developed that use financial statement variables suggested by prior research as explanatory variables. The models, applied to in-sample and out-of-sample data, led to predictions of takeover targets that were better than chance in all cases. The economic outcome resulting from holding a portfolio of the predicted targets over the prediction period are also analysed. Keywords: takeovers, targets, prediction, classification, logit analysis JEL Codes: G11, G17, G23, G34 This is a draft copy and not to be quoted. 2 1. Introduction In this paper our aim is to accurately predict companies that will become takeover targets. Theoretically, if it is possible to predict takeovers with accuracy greater than chance, it should be possible to generate abnormal returns from holding a portfolio of the predicted targets. Evidence of abnormal returns of 20% to 30% made by shareholders of firms on announcement of a takeover bid is why prediction of these events is of interest to academics and practitioners alike. The modelling approach adopted in this study was based on the discrete choice approach used by Palepu (1986) and Barnes (1999). The models were based on financial statement information, using variables suggested by the numerous theories that have been put forward to explain takeover activity. The performance of the models was evaluated using statistical criteria. Further, the predictions from the models were rated against chance and economic criteria through the formation and tracking of a portfolio of predicted targets. Positive results were found under both evaluation criteria. Takeover prediction studies are a logical extension of the work of Altman (1968) who used financial statement information to explain corporate events. Early studies by Simkowitz and Monroe (1971) and Stevens (1973) were based on the Multiple Discriminant Analysis (MDA) technique. Stevens (1973) coupled MDA with factor analysis to eliminate potential multicollinearity problems and reported a predictive accuracy of 67.5%, suggesting that takeover prediction was viable. Belkaoui (1978) and Rege (1984) conducted similar analyses in Canada with Belkaoui (1978) confirming the results of these earlier researchers and reporting a predictive accuracy of 85% . Concerns were raised by Rege (1984) who was unable to predict with similar accuracy. These concerns were also raised in research by others such as Singh (1971) and Fogelberg, Laurent, and McCorkindale (1975). Reacting to the wide criticism of the MDA method, researchers began to use discrete choice models as the basis of their research. Harris et al. (1984) used probit analysis to develop a model and found that it had extremely high explanatory power, but were unable to discriminate between target and non-target firms with any degree of accuracy. Dietrich and Sorensen (1984) continued this work using a logit model and achieved a classification accuracy rate of 90%. Palepu (1986) addressed a number of methodological problems in takeover prediction. He suggested the use of statebased prediction samples where a number of targets were matched with non-targets 3 for the same sample period. While this approach was appropriate for the estimation sample, it exaggerated accuracies within the predictive samples because the estimated error rates in these samples were not indicative of error rates within the population of firms. He also proposed the use of an optimal cut-off point derivation which considered the decision problem at hand. On the basis of this rectified methodology, along with the application of a logit model to a large sample of US firms, Palepu (1986) provided evidence that the ability of the model was no better than a chance selection of target and non-target firms. Barnes (1999) also used the logit model and a modified version of the optimal cut-off rule on UK data. His results indicated that a portfolio of predicted targets may have been consistent with Palepu’s finding, but he was unable to document this in the UK context due to model inaccuracy. In the following section the economic explanations underlying takeover activity are discussed. Section 3 outlines our takeover hypotheses and describes the explanatory variables that are used in the modelling procedure. The modelling framework and data used in the study is contained in Section 4, while the results of our model estimation, predictions, classification accuracy and portfolio economic outcomes are found in Section 5. We conclude in Section 6. 2. Economic explanations of takeover activity Economic explanations of takeover activity have suggested the explanatory variables that were included in this discrete choice model development study. Jensen and Meckling (1976) posited that agency problems occurred when decision making and risk bearing were separated between management and stakeholders1, leading to management inefficiencies. Manne (1965) and Fama (1980) theorised that a mechanism existed that ensured management acted in the interests of the vast number of small non-controlling shareholders2. They suggested that a market for corporate control existed in which alternative management teams competed for the rights to control corporate assets. The threat of acquisition aligned management objectives with those of stakeholders as managers are terminated in the event of an acquisition in order to rectify inefficient management of the firm’s assets. Jensen and Ruback (1983) suggested that both capital gains and increased dividends are available to an 1 Stakeholders are generally considered to be both stock and bond holders of a corporation. 2 We take the interests of shareholders to be in the maximization of the present value of the firm. 4 acquirer who could eliminate the inefficiencies created by target management, with the attractiveness of the firm for takeover increasing with the level of inefficiency. Jensen (1986) looked at the agency costs of free cash flow, another form of management inefficiency. In this case, free cash flow referred to cash flows in excess of positive net present value (NPV) investment opportunities and normal levels of financial slack (retained earnings). The agency cost of free cash flow is the negative NPV value that arises from investing in negative NPV projects rather than returning funds to investors. Jensen (1986) suggested that the market value of the firm should be discounted by the expected agency costs of free cash flow. These, he argued, were the costs that could be eliminated either by issuing debt to fund an acquisition of stock, or through merger with, or acquisition of a growing firm that had positive NPV investments and required the use of these excess funds. Smith and Kim (1994) combined the financial pecking order argument of Myers and Majluf (1984) with the free cash flow argument of Jensen (1986) to create another motivational hypothesis that postulated inefficient firms forgo profitable investment opportunities because of informational asymmetries. Further, Jensen (1986) argued that, due to information asymmetries that left shareholders less informed, management was more likely to undertake negative NPV projects rather than returning funds to investors. Smith and Kim (1994) suggested that some combination of these firms, like an inefficient firm and an efficient acquirer, would be the optimal solution to the two respective resource allocation problems. This, they hypothesised, would result in a market value for the combined entity that exceeded the sum of the individual values of the firms. This is one form of financial synergy that can arise in merger situations. Another form of financial synergy is that which results from a combination of characteristics of the target and bidding firms. Jensen (1986) suggested that an optimal capital structure exists, whereby the marginal benefits and marginal costs of debt are equal. At this point, the cost of capital for a firm is minimised. This suggested that increases in leverage will only be viable for those firms who have free cash flow excesses, and not for those which have an already high level of debt. Lewellen (1971) proposed that in certain situations, financial efficiencies may be realized without the realization of operational efficiencies. These efficiencies relied on a simple Miller and Modigliani (1964) model. It proposed that, in the absence of corporate taxes, an increase in a firm’s leverage to reasonable levels would increase the value of the equity share of the company due to a lower cost of capital. By a 5 merger of two firms, where either one or both had not utilised their borrowing capacity, would result in a financial gain. This financial gain would represent a valuation gain above that of the sum of the equity values of the individual firms. However, this result is predicated on the assumption that the firms need to either merge or be acquired in order to achieve this result. Merger waves are well documented in the literature. Gort (1969) suggested that industry disturbances are the source of these merger waves, his argument being that they occurred in response to discrepancies between the valuation of a firm by shareholders and potential acquirers. As a consequence of economic shocks (such as deregulation, changes in input or output prices, etc.), expectations concerning future cash flow became more variable. This results in an increased probability that the value the acquirer places on a potential target is greater than its current owner’s valuation. The result is a possible offer and subsequent takeover. Mitchell and Mulherin (1996), in their analysis of mergers and acquisitions in the US during the 1980s, provided evidence that mergers and acquisitions cluster by industries and time. Their analysis confirmed the theoretical and empirical evidence provided by Gort (1969) and provided a different view suggesting that mergers, acquisitions, and leveraged buyouts were the least cost method of adjusting to the economic shocks borne by an industry. These theories suggested a clear theoretical base on which to build takeover prediction models. As a result, eight main hypotheses for the motivation of a merger or acquisition have been formulated, along with twenty three possible explanatory variables to be incorporated predictive models. 3. Takeover hypotheses and explanatory variables The most commonly accepted motivation for takeovers is the inefficient management hypothesis.3 The hypothesis states that inefficiently managed firms will be acquired by more efficiently managed firms. Accordingly, H1: Inefficient management will lead to an increased likelihood of acquisition. Explanatory variables suggested by this hypothesis as candidates to be included in the specifications of predictive models included: 1. ROA (EBIT/Total Assets – Outside Equity Interests) 3 It is also known as the disciplinary motivation for takeovers. 6 2. ROE (Net Profit After Tax / Shareholders Equity – Outside Equity Interests) 3. Earnings Before Interest and Tax Margin (EBIT/Operating Revenue) 4. EBIT/Shareholders Equity 5. Free Cash Flow (FCF)/Total Assets 6. Dividend/Shareholders Equity 7. Growth in EBIT over past year, along with an activity ratio, 8. Asset Turnover (Net Sales/Total Assets) While there are competing explanations for the effect that a firm’s undervaluation has on the likelihood of its acquisition by a bidder, there is consistent agreement across all explanations that the greater the level of undervaluation then the greater the likelihood a firm will be acquired. The hypothesis that embodies the impact of these competing explanations is as follows: H2: Undervaluation of a firm will lead to an increased likelihood of acquisition. The explanatory variable suggested by this hypothesis is: 9. Market to book ratio (Market Value of Securities/Net Assets) The Price Earnings (P/E) ratio is closely linked to the undervaluation and inefficient management hypotheses. The impact of the P/E ratio on the likehood of acquisition is referred to as the P/E hypothesis: H3: A high Price to Earnings Ratio will lead to a decreased likelihood of acquisition. It follows from this hypothesis that the P/E ratio is a likely candidate as an explanatory variable for inclusion in models for the prediction of potential takeover targets. 10. Price/Earnings Ratio The growth resource mismatch hypothesis is the fourth hypothesis. However, the explanatory variables used in models specified to examine this hypothesis capture growth and resource availability separately. This gives rise to the following: H4: Firms which possess low growth / high resource combinations or, alternatively, high growth / low resource combinations will have an increased likelihood of acquisition. The following explanatory variables suggested by this hypothesis are: 7 11. Growth in Sales (Operating Revenue) over the past year 12. Capital Expenditure/Total Assets 13. Current Ratio (Current Assets/Current Liabilities) 14. (Current Assets – Current Liabilities)/Total Assets 15. Quick Assets (Current Assets – Inventory)/Current Liabilities The behaviour of some firms to pay out less of their earnings in order to maintain enough financial slack (retained earnings) to exploit future growth opportunities as they arise, has led to the dividend payout hypothesis: H5: High payout ratios will lead to a decreased likelihood of acquisition. The obvious explanatory variable suggested by this hypothesis is: 16. Dividend Payout Ratio Rectification of capital structure problems is an obvious motivation for takeovers. However, there has been some argument as to the impact of low or high leverage on acquisition likelihood. This paper proposes a hypothesis known as the inefficient financial structure hypothesis from which the following hypothesis is derived. H6: High leverage will lead to a decreased likelihood of acquisition. The explanatory variables suggested by this hypothesis include: 17. Net Gearing (Short Term Debt + Long Term Debt)/Shareholders Equity 18. Net Interest Cover (EBIT/Interest Expense) 19. Total Liabilities/Total Assets 20. Long Term Debt/Total Assets The existence of Merger and Acquisition (M&A) activity waves, where takeovers are clustered in wave-like profiles, have been proposed as indicators of changing levels of M&A activity over time. It has been argued that the identification of M&A waves, with the corresponding improved likelihood of acquisition when the wave is surging, captures the effect of the rate of takeover activity at specific points in time, and serves as valuable input into takeover prediction models. Consistent with M&A activity waves and their explanation as a motivation for takeovers is the industry disturbance hypothesis: 8 H7: Industry merger and acquisition activity will lead to an increased likelihood of acquisition. An industry relative ratio of takeover activity is suggested by this hypothesis: 21. The numerator is the total bids launched in a given year, while the denominator is the average number of bids launched across all the industries in the ASX. Size will have an impact on the likelihood of acquisition. It seems plausible that smaller firms will have a greater likelihood of acquisition due to larger firms generally having fewer bidding firms with the resources to acquire them. This gives rise to the following hypothesis: H8: The size of a firm will be negatively related to the likelihood of acquisition. Explanatory variables that can be employed to control for size include: 21. Log (Total Assets) 22. Net Assets 4. Data and Method The data requirements for the variables defined above are derived from the financial statements and balance sheet date price information for Australian listed companies. The financial statement information was sourced from the AspectHuntley data base which includes annual financial statement data for all ASX listed companies between 1995 and 2006. The database includes industry classifications for all firms included in the construction of industry relative ratios. Lists of takeover bids and their respective success were obtained from the Connect4 database. This information enabled the construction of variables for relative merger activity between industries. Additionally, stock prices from the relevant balance dates of all companies were sourced from the AspectHuntley online database, the SIRCA Core Price Data Set and Yahoo! Finance. 4.1 The Discrete Choice Modelling Framework The modelling procedure used is the nominal logit model, made popular in the bankruptcy prediction literature by Ohlson (1980) and, subsequently, in the takeover prediction literature by Palepu (1986). Logit models are commonly utilised for dichotomous state problems. The model is given by equations [1] to [3] below. 9 [3] The logit model was developed to overcome the rigidities of the Linear Probability Model in the presence of a binary dependent variable. Equations [1] and [2] show the existence of a linear relationship between the log-odds ratio (otherwise known as the logit Li) and the explanatory variables. However, the relationship between the probability of the event and acquisition likelihood is non-linear. This non-linear relationship has a major advantage that is demonstrated in equation [3]. Equation [3] measures the change in the probability of the event as a result of a small increment in the explanatory variables, . When the probability of the event is high or low, the incremental impact of a change in an explanatory variable on the likelihood of the event will be compressed, requiring a large change in the explanatory variables to change the classification of the observation. If a firm is clearly classified as a target or non-target, a large change in the explanatory variables is required to change its classification. 4.2 Sampling Schema Two samples were used in the model building and evaluation procedure. They were selected to mimic the problem faced by a practitioner attempting to predict takeover targets into the future. The first sample was used to estimate the model and to conduct in-sample classification. It was referred to as the Estimation Sample. This sample was based on financial data for the 2001 and 2002 financial years for firms that became takeover targets, as well as selected non-targets, between January, 2003 and D

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