Issues relating to the methods used in estimating abnormal returns in the long-

It is evident from the extant literature that the question of which model is appropriate to assess the expected returns remains an unresolved issue. Fama (1998) concludes that all models for expected returns are incomplete descriptions of the systematic patterns in average returns which can lead to spurious indications of abnormal performance in an event study. Issues relating to the methods used to assess the long-term performance are presented below:

1) Market Adjusted Model

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Issues: This model is intuitive and relatively easy to use. However, as Barber and Lyon (1997) have pointed out, it suffers from three types of biases. First, the new listing bias arises because in event studies of long-run abnormal returns, sampled firms generally have a long post-event history of returns, while firms that constitute the index (or reference portfolio) typically include new firms that begin trading subsequent to the event month. Second, the rebalancing bias arises because the compound returns of a reference portfolio, such as an equally weighted market index, are typically calculated assuming periodic (generally monthly) rebalancing, while the returns of sample firms are compounded without rebalancing. Third, the skewness bias arises because long-run abnormal returns are positively skewed. Moreover, this model does not consider the ―size‖ and the ―book value to market value‖ factors while determining the abnormal returns.

2) Market Model

Issues: Since this model uses the market index return, this would also suffer from new listing bias and rebalancing bias as discussed above. Another issue is that this model uses the pre-bid period for the identification of and

parameters, whereas the characteristics of bidders‘ security may change as a result of the bid. Post outcome returns would reflect these changes and bias the results (Limmack, 1991).

3) Capital Asset Pricing Model (CAPM)

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Issues: Issues mentioned for the market model are again applicable for CAPM.

Moreover, this model assumes the stationarity of the risk-free rate (Loderer &

Martin, 1992). The risk free rate could be driven up if the acquisition intensity increases in a period of time and alternatively, it could decline if the acquisition activity subsides. In addition, the CAPM model has the ―joint hypothesis‖

problem, i.e., it assumes that the CAPM truly represents the expected return of the security (Dutta, 2006).

4) Fama French Three factor Model

Issues: Ikenberry, Lakonishok and Vermaelen (1995) made two observations against this approach. First, returns are rebalanced monthly, thus the abnormal performance measured under this approach is less representative of a realistic investment strategy. Second, this procedure assumes that the coefficients are stable over time, which implies that the characteristics of the portfolios are not changing.

Barber and Lyon (1997) identified two disadvantages of the three-factor model.

They are: First, given four parameters in the regression, it requires at least five observations of monthly returns post-event. This creates a survivor bias among remaining sample firm.¹⁹ The second, observation is similar to Ikenberry,

19 It is not clear, ex ante, what effect this survivor bias has on tests for long-run abnormal returns.

The direction of the bias depends on the returns of firms in the months immediately prior to delisting. In the case of a merger, acquisition, or private transaction, these returns are likely positive, while in the case of a bankruptcy or liquidation these returns are likely negative.

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Lakonishok and Vermaelen (1995) and further explains that in contrast to the size/book-to-market portfolios, in which a firm‘s portfolio assignment is allowed to change once per year, the regression approach assumes that a firm‘s market, size, and book-to-market characteristics are stable over time.²⁰

5) Buy-and-Hold-Abnormal Return (BHAR): Reference Firm Approach The BHAR approach and the characteristic-based matching approach (BHAR) have been in use widely following the works of Ikenberry et al. (1995), and Barber and Lyon (1997), Lyon et al. (1999). Mitchell and Stafford (2000) termed BHAR returns 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. An appealing feature for using BHAR is that buy-and-hold-returns better resemble investors‘ actual investment experience than periodic (monthly) rebalancing entailed in other approaches to measuring risk-adjusted performance.²¹ The joint-test problem remains in that any inference on the basis of BHAR hinges on the validity of the assumption that event firms differ from the otherwise similar

20 Barber and Lyon, considered an alternative application of the FF three factor model, which is analogous to a traditional market model approach. Post-event abnormal returns can be calculated using a sample firm‘s realised return less an expected return eg.,reference and control methods.

21Apart from similarity with the actual investment experience, the BHAR approach also avoids biases arising from security microstructure issues when portfolio performance is measured with frequent rebalancing (see Blume and Stambaugh, 1983, Roll, 1983, and Ball, Kothari, and Shanken, 1995).

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event firms only in that they experience the event. The researcher implicitly assumes an expected return model in which the matched characteristics (e.g., size and book-to-market) perfectly proxy for the expected return on a security. Since corporate events themselves are unlikely to be random occurrences, i.e., they are unlikely to be exogenous with respect to past performance and expected returns, there is a danger that the event and non-event samples differ systematically in their expected returns notwithstanding the matching on certain firm characteristics. This makes matching on (unobservable) expected returns more difficult, especially in the case of event firms experiencing extreme prior performance.

Issues: Barber and Lyon (1997) present two insights. First, it is problematic to calculate the abnormal returns using reference portfolios, such as an equally weighted market index or size decile portfolios. The abnormal returns calculated using reference portfolios yield test statistics that are mis-specified (empirical rejection rates exceed theoretical rejection rates).

The three reasons identified for the observed biases include:

1) New listing bias, which arises because in event studies of long-run abnormal returns, sampled firms generally have a long post-event history of returns, while firms that constitute the index (or reference portfolio) typically include new firms that begin trading subsequent to the event month;

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2) Rebalancing bias, which arises because the compound returns of a reference portfolio, such as an equally weighted market index, are typically calculated assuming (generally monthly) rebalancing, while the returns of sample firms are compounded without rebalancing; and

3) Skewness bias, which arises because long-run abnormal returns are positively skewed.