Programación del HM

This thesis also employed a standard event study methodology to examine the impact of dividend announcements on share prices of Nigerian listed firms. An event study is an

13 _{As at June 2012, a total of 191 companies were officially listed on the Nigerian Stock Exchange. This number}

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empirical investigation of the relationship between security prices and economic events (Strong, 1992). This technique of financial research enables a researcher to assess the impact of an economic event, such as takeover bid, mergers and acquisitions, earnings announcement, and a change in dividend policy on a firm’s share price. According to the event study methodology, a statistically significant abnormal share return generated during the announcement period clearly indicates that the dividend announcement has not been fully anticipated and consequently, conveys important additional information to the market. Consequently, an observation of small and statistically insignificant post-announcement abnormal returns indicates that the market is information-efficient in the semi-strong sense, reacting quickly to new information releases, impounding that information into share prices rapidly and leaving no opportunity to earn above-average returns using publicly available information.

The analysis of the impact of firm-specific event on share prices requires the actual share return to be compared with the expected share return around the event announcement date in order to determine whether or not any stock market reaction has occurred. As regards dividend announcements, abnormal share returns around the event dates are estimated as the difference between the actual share returns and the expected returns that it would have earned in the event that no dividend news is disclosed. Strong (1992) noted that the actual share returns can be estimated using either a discrete or logarithmic approach14. The author also suggested two reasons why logarithmic returns are preferable:

“Theoretically logarithmic returns are more tractable when linking together sub- period returns to form returns over longer intervals (simply add up the sub- period returns) and empirically, logarithmic returns are more likely to be normally distributed and so conform to the assumptions of standard statistical techniques.”

(Strong, 1992, p. 535)

Consequently, the actual share returns for each of the companies in the sample are calculated using the logarithmic approach as follows:

Rjt= Ln(Pjt/Pjt-1) [4.1]

14

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Where; Rjtis the daily return of firm j on day t; Ln is the natural log; Pjt is the share price of firm j on day t; and Pjt-1 is the share price of firm j on day t-1.15Stock returns calculated from the above formula provided the total returns for the sample companies as share prices are automatically adjusted for cash dividends by the NSE on ex-dates. The daily stock market returns (returns on the market) was estimated using the NSE-ASI index (a value-weighted index)16 as follows:

Rm = Ln (NSE-ASIt/NSE-ASIt-1) [4.2]

As regards the estimation of the expected returns, Strong (1992) proposed five methods of calculating expected returns: mean adjusted returns, market adjusted returns, capital asset pricing model (CAPM), the match/control portfolio benchmark, and the market model. The market model benchmark17 is the most popular method used in calculating abnormal returns in event studies. The market model assumes a linear relationship between the expected return of an individual security and the market index. There are various motivations for using the market model when estimating abnormal returns. First, the market model produces same results for samples having trading frequencies systematically different from average – infrequently or frequently (Brown and Warner, 1985). In addition, the market model produces “smaller variance of abnormal returns and smaller correlations across security abnormal returns given closer conformity to standard statistical tests” (Strong, 1992, p. 538). Moreover, the market model will automatically control for size effect (Schwert, 1983). As a result, the present study calculates the expected return using the following market model:

E(Rjt) = αj + βjRmt + ejt [4.3]

15

Black and Scholes (1974) noted that share returns can be estimated as dividend plus capital gain instead of only in terms of share price, i.e. Rit= Ln (Pit+Dit)/Pit-1, where Dit is the dividend amount for the current year.

However, in calculating returns according to equation [4.1] dividends were not included for two reasons: (i) because of non-availability of reliable dividend amounts; and (ii) this method of calculating share prices was adopted to maintain comparability with other studies in the area e.g. Lonie et al (1996) and McCluskey et al. (2006).

16

In this study, the NSE-ASI index is constructed on the basis of a value-weighted index. This index was adopted in this study as there is “no evidence that the use of the value-weighted index increases the power of the tests” compared to equally-weighted index (Brown and Warner, 1980, p. 248).

17 _{Brown and Warner (1985) recommend the use of the market model under a variety of conditions when}

using daily returns. In addition, prior studies such as Petit (1972), Aharony and Swary (1980); Easton (1991), Lonie et al. (1996), McCluskey et al. (2006), Al-Yahyaee et al. (2011) used the market model in the estimation of the expected returns.

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Where:

Rjt = actual returns on stock j on day t,

αj, βj = the intercept and slope, respectively, of the linear relationship between the return

for stock share j and the returns of the NSE, Rmt = return on the NSE index on day t, and

ejt = error term of stock j at period t and is expected to have a value of zero.

The abnormal return (ARjt) for stock j on day t is then estimated by subtracting the actual return on day t from the expected return predicted from the market model:

ARjt = Rjt – (αj + βjRmt) [4.4]

Where; ARjt is the abnormal return on stock j on day t, Rjtis the expected return on stock j

on day t (obtained from the market model), and Rmt is the return on the market portfolio, which was proxy for in this study by the NSE-ASI index. For the purpose of this study, the coefficients α and βare ordinary least squares (OLS) estimates ofαj and βj estimated from a regression of daily stock returns on daily market returns over a 120 trading day period prior to the dividend announcement date (Dt-131 to Dt-11). Researchers have employed various estimation periods in the literature for calculating the market model parameters using the OLS. For example, Lonie et al. (1996) and McCluskey et al. (2006) used 180 days for dividend studies, while Amihud and Murgia (1997) and Dharmarathne (2013) included 120 observations in their estimation procedure. In a study by Al-Yahyaee et al. (2011), the market model was estimated over a 210 days centred on the dividend announcement day. According to Strong (1992), there is a trade-off between increasing the number of observations to improve the statistical accuracy of the estimatedαi and βi and not going too far back from the test period in case the parameters of the model change across time. This factor influenced the decision to include 120 observations in the estimation of the market model in the present study.

The daily average abnormal return for day t is calculated as follows:

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Where ARjt = the weighted average portfolio abnormal return for dividend-increasing firms, dividend-decreasing firms, and for dividend no-change firms, N is the number of events in the sample.

The cumulative average abnormal return in the days surrounding the dividend announcement dates is calculated by summing average ARs over time as follows:

[4.6]

Where; CARjt is for the period from t = K days, until t = L days.

In terms of the measurement interval, the daily share price data were used to detect the presence or absence of abnormal share performance in a 21-day event window, measured from day - 10 to day +10 surrounding the dividend announcement date. The daily return data was favoured to their weekly or monthly counterparts because daily returns data allow the impact of dividend news to be isolated, thereby diminishing the probability of contamination from other signals (Dyckman et al., 1984; Morse, 1984; Brown and Warner, 1985). However, there are few problems associated with daily share prices. First, daily security returns exhibit statistical problems such as deviation from normality18 and autocorrelation as compared to their weekly or monthly counterparts. In addition, there is a higher risk of bias and inconsistency in estimating model parameters when daily data are used. Although daily data deviates from normality, the mean abnormal returns on a large sample of shares converges on normality, enabling the researcher to use Ordinary Least Square (OLS) and t-statistics with a reasonable degree of confidence (Brown and Warner, 1985).

Some researchers have suggested that share betas (βs) vary dramatically over time. For example, Blume (1975) identified the problem of instability in beta estimates, which arises from errors in both equations and in variables. This is supported by a number of event studies which examined aspects of market overreaction and documented evidence that

18 _{Brown and Warner (1985, p.25) noted that “the non-normality of daily returns has no obvious impact on}

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beta can alter dramatically between successive test periods (Chan, 1988; Ball and Kothari, 1982). Strong (1992) also noted that numerous event studies have documented that the explanatory power of the market model regression equations and the mean cross-sectional value of beta rise as the measurement interval increases. Moreover, serious bias arises where beta estimates are calculated for shares that are infrequently traded (Dimson, 1979). The author documented evidence that the estimated betas of infrequently traded UK shares rise as the interval increases, while to a lesser extent, the opposite holds for the frequently traded shares. The above analysis suggests that infrequently traded shares have a beta estimate which is biased downwards, while the measure for frequently traded share is biased upwards. However, thin trading effects on beta estimates can be corrected using the following suggestions proposed in the literature. Scholes and Williams’ (1977) suggested a beta estimator which assumes a simple regression of the return on the security against the return on the market. Thin trading effects on bet estimates can also be corrected by using an aggregate coefficient estimator, which does not require that a trade occurs in every return interval and also by running multiple regressions of share returns against lagged, matching and leading values of the market index (Dimson, 1979).

An important characteristic of an emerging market such as Nigeria is the existence of thin trading problems, where corporate information is often neither reliable nor available to all traders. As a robustness check and to test the sensitivity of the results of this study to beta estimation, an alternative approach to calculating the abnormal return was employed following Charest (1978), Woolridge (1982), and Al-Yahyaee et al. (2011). Specifically, abnormal returns were generated on the assumption that the abnormal return for a firm’s share was equal to the market return as proxy by the NSE-ASI index. Therefore, abnormal returns were also calculated as the difference between the actual return earned by a stock on a particular day and the return on the market for the same day. Using this approach, stocks is assumed to have a beta of 1.0. The abnormal returnswere calculated as follows:

ARjt = Rjt - Rmt [4.7]

Where; ARjt is the abnormal return of stock j on time t, Rjt is the actual return of stock j on time t and Rmt is the return on the market index on time t.

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For the purpose of this study, day t0 is designated as the dividend announcement date, where t is defined as the occasion of the very first official statement on dividends that can be identified in press releases such as nationally circulated financial papers or other databases (Gurgul et al., 2003). According to the signalling hypothesis, if changes in dividends convey information, the abnormal return earned on the dividend announcement date should be significantly different from zero (Lonie et al., 1996; McCluskey et al., 2006). The abnormal returns which are significantly different from zero indicate the prominent market reaction to the information conveyed by the announcement of dividends. For the purpose of testing the “information content” hypothesis of dividend announcements, the dividend announcement news was divided into three main categories based on the character of their changes in the dividend level: (i) dividend-increasing companies (DI); (ii) dividend-decreasing companies (DD); and (iii) dividend-no-change companies (DnC). The signalling hypothesis predicts that the shares of dividend-increasing companies should, on average, earn positive abnormal returns, and the shares of dividend-decreasing companies should, on average, earn negative abnormal returns, while the shares of dividend-no change companies should, on average, earn normal returns (zero abnormal returns).

Numerous studies conducted on the impact of dividend announcements on share prices in developed capital markets have documented that the dividend news is not conveyed to investors in isolation; rather dividend disclosure is usually accompanied by the announcement of company earnings and other events that may generate a certain amount of market noise (Kane et al., 1984; Easton, 1991; Lonie et al., 1996; McCluskey et al., 2006).19 In Nigeria, both dividends and earnings are announced at the same time, usually during the financial-year end for companies listed on the NSE. As Chapter 3 highlighted, the occurrence of such confounding events around firm-specific announcements is a problem for event studies in general. This contemporaneous release of both dividends and earnings news requires the interaction between dividend and earnings to be analysed in order to observe the influence of the two signals on share values. Disentangling the importance of the dividend component of the joint signal may be problematic (Kane et al., 1984; Easton, 1991; Lonie et al., 1996, McCluskey et al., 2006). In an attempt to disentangle these

19_{One of the few studies that analysed the isolated announcements of dividends and earnings was a US study}

by Aharony and Swary (1980), which identified 149 firms that made no earnings announcements for 10 days before and after the dividend announcements.

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confounding signals, prior studies have applied two measures: (i) examining the abnormal returns for different dividend-earnings groups and (ii) employing regression analysis to analyse the interaction between dividends and earnings news. In Nigeria, both dividend and earnings are announced concurrently to the market. This may create problem when attempting to study the impact of dividend announcements in the market without considering any confounding earnings signal. However, in the current study, the researcher did not analyse this interaction effect because of the non-availability of reliable earnings per share (EPS) data of the sample companies.

In summary, the purpose of the standard event study methodology employed in this thesis was to investigate how the Nigerian stock market reacts to cash dividend announcements in order to determine whether or not dividends convey price-sensitive information to the market. Specifically, the study tests the null hypothesis that the daily average abnormal return is zero. In other words, cash dividend announcements have no systematic impact on corresponding share prices. This hypothesis was tested by performing a parametric t-test where the t-statistics are calculated using the cross-sectional standard deviation. In the current thesis, both the market model and the market adjusted returns were employed in the calculation of the abnormal returns. This objective was accomplished by employing data from all the companies listed on the Nigerian stock market that announced cash dividends between January 1, 2008 and December 31, 2012. Chapter 6 of this thesis reports the findings of this part of the study.