This dissertation consists of four papers that examine various aspects of the temporal patterns in the volatility of asset returns. The first paper compares the predictive performance of various parametric ARCH models. We find that ARCH models are generally good descriptions of the timevarying volatility of UK stock returns. There appears to be asymmetry in the conditional volatility, although no single model outperforms the rest in all instances. In the second paper, we uncover evidence of asymmetric predictability in the conditional variance of firms of different size. Large firms shocks affect the future volatility of small firms, but not vice versa. We also find that trading period shocks have a significant impact on future volatility, but not nontrading period shocks. In the third paper, we document a contemporaneous volatility-volume relationship. We find that volatility is related to change in trading volume, and we propose a conditional volatility model that incorporate this contemporaneous volatility-volume relationship. In the final paper, we examine the various method of adjusting for nontrading effects in ARCH models, and we propose a new diagnostic test to detect the validity of such adjustments. We also uncover evidence that conditional volatility increases prior to market closure, but declines after market opening.

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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.

This study examines whether short-sellers and financial analysts develop complementary information about future earnings and returns and assesses whether investors can improve predictions made by each of these intermediaries using information provided by the other. The first main result is that the relative short interest ratio (shares sold short divided by total shares outstanding) contains information that is useful for predicting future earnings, beyond (i.e., incremental to) the information in analyst forecasts. I also find that analysts do not fully incorporate short interest information into their forecasts and demonstrate that analyst forecasts can be improved (i.e., can be made to be less biased and more accurate) by adjusting for short interest information. The second main result is that analyst forecast revisions contain information that is useful for predicting future abnormal returns, beyond the information in the relative short interest ratio. I demonstrate that portfolios of stocks formed based on consistent signals from short-sellers and analysts produce abnormal return spreads that are significantly larger than spreads produced by portfolios formed using signals from short-sellers alone. Collectively, the evidence suggests that short-sellers and analyst provide complementary information about future firm performance that is useful to investors.

The finding of reversals in weekly returns has been attributed to a combination of microstructure issues and overreaction to information. I provide new evidence eliminating overreaction as a source of reversal. I show that well-known weekly contrarian profits are followed by a long run of momentum profits. In fact, these profits are strong enough to produce a significant momentum effect over the full year following portfolio formation. Thus, the market does not appear to view extreme weekly returns as excessive, as implied by an overreaction story. To the contrary, this return continuation is consistent with underreaction to the news driving extreme weekly returns. This is supported by cross-sectional tests in which I find this week's news is positively related to next week's returns. The evidence presented here is consistent with growing evidence that underreaction to firm-specific information is a pervasive feature of price formation. Therefore, if any short-run contrarian profits can be realized, they are better viewed as compensation for providing liquidity than as a reward for arbitrage.

Hedge fund investing is a relatively new phenomenon in Sweden. The first Swedish hedge fund was started in 1996. This new financial sector has since showed a steady growth. Due to the novelty of hedge fund phenomena, it is right to ask whether the investors are prepared for this kind of investments; how they choose their hedge funds investments and whether they have adequate knowl-edge in the field. This thesis provides a mapping of the investors’ behavior regarding hedge fund investments. We have concluded that Swedish hedge fund investors have a limited basis of knowledge required to fully utilize hedge funds in their portfolios.

The purpose of this paper is to investigate the long-term share price effects for Swedish companies that employed repurchases programs during 2000 - 2012. This paper applies a trading strategy where the investor invests in stocks of compa-nies that engage in repurchase of their own equity. We test buy-and-hold abnormal returns versus two different proxies for the control firm, using small sample t-statistics. Abnormal returns for one to five years are insignificant under sta-tistic tests, using the supersector indices. In contrast, they are significant using a proxy for the market index as control firm. Factors hypothesized to contribute to this result are incon-clusive using our method of comparison. As a whole, we suggest that investing in companies that repurchase stock is a solid strategy: It is on par or better than index.

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This study examines whether short-sellers and financial analysts develop complementary information about future earnings and returns and assesses whether investors can improve predictions made by each of these intermediaries using information provided by the other. The first main result is that the relative short interest ratio (shares sold short divided by total shares outstanding) contains information that is useful for predicting future earnings, beyond (i.e., incremental to) the information in analyst forecasts. I also find that analysts do not fully incorporate short interest information into their forecasts and demonstrate that analyst forecasts can be improved (i.e., can be made to be less biased and more accurate) by adjusting for short interest information. The second main result is that analyst forecast revisions contain information that is useful for predicting future abnormal returns, beyond the information in the relative short interest ratio. I demonstrate that portfolios of stocks formed based on consistent signals from short-sellers and analysts produce abnormal return spreads that are significantly larger than spreads produced by portfolios formed using signals from short-sellers alone. Collectively, the evidence suggests that short-sellers and analyst provide complementary information about future firm performance that is useful to investors.