One of the most controversial issues for long-term stock performance is whether the presence of anomalies is against the efficient market hypothesis. The methodologies to measure abnormal returns applied in the long-run event studies are questioned for their reliability and specification. This thesis compares three major methodologies via a simulation process based on the UK stock market over a period of 1982 to 2008 with investment horizons of one, three and five years. Specifically, the methodologies that are compared are the event-time methods based on models (Chapter 3), the event-time methods based on reference portfolios (Chapter 4), and the calendar-time methods (Chapter 5). Chapter 3 covers the event-time approach based on the following models which are used to estimate normal stock returns: the market-adjusted model, the market model, the capital asset pricing model, the Fama-French three-factor model and the Carhart four-factor model. The measurement of CARs yields misspecification with higher rejection rates of the null hypothesis of zero abnormal returns. Although the application of standard errors estimated from the test period improves the misspecification, CARs still yield misspecified test statistics. When using BHARs, well-specified results are achieved when applying the market-adjusted model, capital asset pricing model and Fama-French three-factor model over all investment horizons. It is important to note that the market model is severely misspecified with the highest rejection rates under both measurements. The empirical results from simulations of event-time methods based on reference portfolios in Chapter 4 indicate that the application of BHARs in conjunction with p-value from pseudoportfolios is appropriate for application in the context of long-run event studies. Furthermore, the control firm approach together with student t-test statistics is proved to yield well-specified test statistics in both random and non-random samples. Firms in reference portfolios and control firms are selected on the basis of size, BTM or both. However, in terms of power of test, these two approaches have the least power whereas the skewness-adjusted test and bootstrapped skewness-adjusted test have the highest power. It is worth noting that when the non-random samples are examined, the benchmark portfolio or control firm needs to share at least one characteristic with the event firm. The calendar-time approach is suggested in the literature to overcome potential issues with event-time approaches like overlapping returns and calendar month clustering. Chapter 5 suggests that both three-factor and four-factor models present significant overrejections of the null hypothesis of zero abnormal returns under an equally-weighted scheme. Even for stocks under a value-weighted scheme, the rejection rate for small firms shows overrejection. This indicates the small size effect is more prevalent in the UK stock market than in the US and the calendar-time approach cannot resolve this issue. Compared with the three-factor model, the four-factor model, despite its higher explanatory power, improves the results under a value-weighted scheme. The ordinary least squares technique in the regression produces the smallest rejection rates compared with weighted least squares, sandwich variance estimators and generalized weighted least squares. The mean monthly calendar time returns, combining the reference portfolios and calendar time, show similar results to the event-time approach based on reference portfolios. The weighting scheme plays an insignificant role in this approach. The empirical results suggest the following methods are appropriately applied to detect the long-term abnormal stock performance. When the event-time approach is applied based on models, although the measurement of BHARs together with the market-adjusted model, capital asset pricing model and Fama-French three-factor model generate well-specified results, the test statistics are not reliable because BHARs show severe positively skewed and leptokurtic distribution. Moreover, the reference portfolios in conjunction with p-value from pseudoportfolios and the control firm approach with student t test in the event-time approach are advocated although with lower power of test. When it comes to the calendar-time approach, the three-factor model under OLS together with sandwich variance estimators using the value-weighted scheme and the mean monthly calendar-time abnormal returns under equal weights are proved to be the most appropriate methods.

This paper estimates how changes in macroeconomic variables impact the monthly stock returns of the full service, quick service, and fast casual restaurant segments. Market-capitalization-weighted stock indices are created to measure these effects. I also analyze how these changes influence each segment’s share of total valuation in the industry. These are changes in total segment market capitalization relative to changes in total industry market capitalization. My results suggest that the full service segment index is impacted by macroeconomic changes to the greatest extent of the three segments. The quick service segment index is the least affected. Changes in inflation, food commodity prices, consumer sentiment, and the federal funds rate have impacts on stock returns across all three segments.

In this study a two-part mixed probability density function was derived which described the relative changes in the Standard and Poor's 100 Stock Index over various intervals of time. The density function is a mixture of two different halves of normal distributions. Optimal values for the standard deviations for the two halves and the mean are given. Also, a general form of the function is given which uses linear regression models to estimate the standard deviations and the means. The density functions allow stock market participants trading index options and futures contracts on the S & P 100 Stock Index to determine probabilities of success or failure of trades involving price movements of certain magnitudes in given lengths of time.

The objectives of this thesis are threefold. First, this dissertation examines whether patterns (growth and consistency in growth) of firms' past financial performance influence investors' perceptions about stock values and future performance of these firms. Second, multiple estimation horizons of past performance variables (ranging from one to five years) are used to assess whether the interaction between growth patterns and measurement interval lengths of these variables influence investor expectations. Third, this thesis examines whether an intermediate price drifts (e.g. Jegadeesh and Titman [1993]) and subsequent long-horizon price reversal (e.g. DeBondt and Thaler (1985)] are manifestations of a market over-reaction as suggested in recent studies (e.g. Lee and Swaminathail [2000]).Annual data on sales, earnings, cash flow, and stock returns over various time periods from a sample of publicly traded firms listed on the NYSE, AMEX, and NASDAQ exchanges from 1983 to 1999 are used to address the research questions proposed in this thesis. The evidence provided in this study shows that low-growth firms outperform their high-growth firm counterparts across different performance variables, estimation intervals, and investment horizons except in the first post-formation year for firms ranked by their prior one-year financial growth rate (except for sales growth). These return differentials between low and high growth firms increase uniformly as more years of past financial performance added.Furthermore, when ranking firms based on the consistency of their prior financial growth rates over multiple estimation periods, this study finds that a firm consistently achieving low (high) growth rates that places it in the lowest (highest) growth 40 percent earns high (low) stock returns. The consistency in a firm's prior financial performance influences the behavior of its future stock returns, i.e. the longer the consistency of exceptionally strong (weak) performance of a firm, the greater (lower) its subsequent stock returns. However, the incremental impact of an additional year of growth consistency on future returns seems to dissipate after the third year of prior performance data, suggesting that it may not take investors longer than three years to assume that a firm's past growth will continue for many years to come. Thus, additional evidence confirming investors' prior beliefs will not lead to a significant price drift because their expectations are already reflected in market prices.First year returns for firms except SG exhibit a strong financial drift. The price drift seems to persist even with longer estimation horizons. Multiple regression analyses suggest that reported higher returns for low-growth firms is not due to risk as measured by market betas or book-to-market ratios, nor is it due to the disproportionate impact caused by relatively smaller firms. As well, the one-year-ahead size-adjusted abnormal returns are significantly and negatively related to the size-adjusted abnormal returns for years 2 through 5. This result indicates that the evidence of a price drift reported in the first post-formation year might be due to a market over-reaction, a finding consistent with results reported by Lee and Swaminathan (2000). In additional analysis, return performance for all growth portfolios for the month of January is compared to the remainder of the year. No evidence indicating that returns to these portfolios are driven by extraordinary performance of low-growth firms in the month of January.For all variables (except for past sales growth and to some degree past stock returns), the financial drift in year one return for portfolios based on prior-one year of past performance data, is significantly stronger than that reported in Chan et al. (2004). Results reported in this thesis indicate that the average abnormal return differential between low and high growth firms for the five-year estimation intervals (with exception of past sales growth) is greater than 10 percentage points. The evidence contradicts that documented in Chan et al. (2004). They find no discernable evidence of price reversals over the next 36-months after ranking firms by their five-year growth rates in sales, operating income, and net income. However, results of this study are consistent with the predictions of behavioral models (e.g. Daniel et al. [I998] and Lakonishok et al. [1994]) suggesting that investors put excessive weight on patterns of a firm's past financial performance when projecting its future prospects.

The paper analyzes effects of the financial crisis on stock market of the Czech Republic and Spain. We employ BEKK-GARCH model in order to study volatility spillovers and transmissions from the US stock market to stock markets of the Czech Republic and Spain. The multivariate GARCH models results show statistically significant, but relatively small, almost irrelevant volatility spillovers from the US stock market to stock markets of the Czech Republic and Spain. The Czech stock market exhibits higher conditional correlation coefficient than the Spanish stock market.

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Despite the rich literature on theories of stock splits, studies have omitted public utility firms from their analysis and only analyzed split by industrial firms when examining managerial motives for splitting their stock. I examine the liquidity-marketability hypothesis, which states that stock splits enhance the attractiveness of shares to individual investors and increase trading volume by adjusting prices to an optimum trading range. Changes in the regulatory process, resulting from EPACT, have opened a window of opportunity for the study and comparison of the two traditional motives for splitting stock --signaling versus liquidity-marketability motives. Public electric utility firms provide a clean testing ground for these two non-mutually exclusive theories as liquidity/marketability hypothesis should dominate before the enactment of the EPACT since the conventional signaling theory of common stock splits should not apply given the low levels of information asymmetry in regulated utility companies. In the post-EPACT period, however, the signaling effect is expected to play a more dominant role. Based on both univariate and multivariate analyses, my results are consistent with the hypothesis posed. For the pre-EPACT period, liquidity motive seems to predominate in explaining the abnormal announcement return of utility stock splits. On the other hand, the results support the signaling motive as a leading explanation of abnormal returns in the post-EPACT period.

This research report examines the effect of the announcement of corporate unbundling by South African corporations listed on the Johannesburg Stock Exchange. This research was carried out in order to update the literature and to analyse whether results confirm the previous research performed by Blount and Davidson (1996) or coincides with international trends, which displayed positive responses to unbundling announcements. The event study methodology was used for analysing the market’s reactions to corporate unbundling announcements. Abnormal returns were calculated using the market model approach with an event window of ten days and an estimation window of 120 days. A sample of 27 corporations were analysed in this research report during the period January 2002 to June 2011. The results indicated strong negative abnormal returns as a result of the corporate unbundling announcements. This finding confirms Blount and Davidson’s (1996) earlier research.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. January 2015. / Stylized facts play a significant role in the testing whether models agree with known statistical anomalies and phenomena that occur in financial markets or not. Thus, we can use these stylized facts as a modelling tool or just to understand the general behavior of financial markets better. In the paper by Bouchaud et al in 2004 [1] we see the promotion of a new stylized fact that correlations in trade signs fail to die out, even after large lags. In fact, Bouchaud et al expressed the correlations as a slow power-law decay over trade ticks. In the results of our empirical study of JSE and BM&FBOVESP we find that the selected stocks show the this same power-law decay of correlations of trade signs. We also find that the stocks behave in a way which may allow for price manipulation at high enough trading rates as discussed by Gatheral [2].

Student Number : 0300191P - PhD thesis - School of Accountancy - Faculty of Commerce, Law and Management / The main objective of the study has been to provide new insights into ongoing recent studies examining the impact of stock market liberalization at both macro and micro (firm) levels. The study focused on a single country, South Africa, whose exchange, the Johannesburg Stock Exchange (JSE), liberalized in the 1990s. Consistent with empirical evidence from other studies the study finds support at market, firm and sectoral level for the prediction by international asset pricing models that stock market liberalization reduces the cost of capital. More important, the study makes five major contributions to the literature on the impact of stock market liberalization in emerging markets. First, it demonstrates that some emerging market specific risks such as political and economic risks can act stronger binding constraints to foreign investment than direct legal barriers which foreign investors are frequently able to circumvent. The second contribution is the observation that there are some firms (in the minority however) that will experience a significant increase in the cost of capital following liberalization, a situation where the local price of risk is higher than the global price of risk, contrary to international asset pricing theory. The third contribution is that it has been empirically proved that the reduction in firms’ cost of capital following stock market liberalization is permanent. It is not a transitory phenomenon. The fourth contribution of the study highlights the influence of firm specific characteristics such as size of the firm, book-to-market ratios and leverage ratios on firms’ response to impact of stock market liberalization. The preference for large firms by foreign investors is supported, contrary to Merton’s (1987) recognition hypothesis, and hence highlights the inconclusiveness of the debate on whether stock market liberalization benefits both large firms and small firms. The fifth contribution is the observation that the effective liberalization date is not the same for all firms but varies from firm to firm.