A general issue with moving average trading is the assumption that all buy/sell signals result in a trading action. The argument that such trading rules are representative of trading practice is highly questionable. This thesis proposes two new moving average trading rules designed to capture trading practice. The first trading rule is the trade reduction rule and is based on the idea of allowing a trade to run. The second trading rule is the positive autocorrelation rule and is based on the idea of only trading if it is believed to be profitable to do so. The trading rules are tied to moving average trading via the buy/sell signal generating mechanism and alter the way the price crossover rule responds to the buy/sell signals. Simulations of portfolios of UK equities find that the trading rules uncover information that is missed by the price crossover rule and there is evidence that this information is financially exploitable. This motivates the argument that the information needed for trading to be economically viable is observable in the price. The trading rules also establish a link with the market microstructure literature. The trading rules uncover issues of informed trading (asymmetric information), liquidity, adverse selection and price impact. The strongest interpretation that can be applied to the trading rules in this context is that they are examples of informed trading. Compared to the price crossover rule, the trading rules are better able to extract meaning from or are better able to understand the same price information.

This thesis investigated reliable measures of market risk using high frequency data. The first part of the study, comprising of chapters 2,and 3, investigated the issue of risk measurement on a single time scale method basis. In Chapter 2, we explored the market risk measurement based on high-frequency measures of volatility with selected stocks in three different sectors. Parametric as well as non-parametric procedures are discussed. Furthermore, the backtesting results for comparing the risk forecasting performance of different risk measurements are also provided. Chapter 3 proceeds into the analysis of liquidity risk in high-frequency trading. We proposed an extended VaR measurement by incorporating the liquidity risk in intraday trading strategies when analysing limit order book data. The focus is on the integration of asymmetric information, upward or downward risks, into the input factors of forecast variables. We further find that there exists an asymmetric behavior of bid spread and ask spread between different trading volume. By taking account of the actual liquidity risk faced by investors with different trading size and positions, we proposed a liquidity adjusted intraday VaR (LAIVaR). We apply the bivariate analysis to investigate the asymmetric effect of the bid and ask side. The empirical results of liquidity adjustment show that the liquidity risk is a crucial factor in estimating VaR. The second part of the study, the analysis in Chapter 4, is extended to a multiple time scale framework by using an empirical scaling law method. This chapter proposed three new empirical scaling laws based on the original maximal price change (MPC) scaling law by Glattfelder, Dupuisy, and Olsen (2011). The most valuable property is the scale invariance which allows financial analysts to assess the maximum loss for any time interval. The strong evidence of the effectiveness of the new scaling law methods are provided in the model performance section. We find that the scaling law method with only one month in-sample data can already provide a good prediction of risk, whereas for the conventional VaR at least ten years length of data is needed to obtain a reasonable result.

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This thesis investigates the motives behind share buybacks in the UK, and examines this form of corporate payout in relation to dividends by extending traditional corporate dividend behaviour models to include payouts made through share repurchases. Using hand-collected data for 267 firms belonging to the FTSE 350 from 2001 to 2004, we show that the motives of firms in the UK to repurchase their shares appear to be different from those of US firms, which we mainly attribute to differences in corporate governance between the two countries. We find that the most plausible motive behind share buybacks in the UK is the distribution of surplus cash to shareholders although, contrary to US findings, UK share buybacks are related primarily to expected rather than unexpected earnings. Indeed, share buybacks appear to be paid out of the same expected earnings component as are dividends, which suggests that repurchases may be substitutes for (at least part of) regular dividend payouts. When we use behavioural dividend models, such as Lintner (1956) and Fama & Babiak (1968), to determine whether they can be extended to estimate changes in total payouts as opposed to just dividends, we find that firms that repurchase their shares tend to have a smoother dividend policy, which lends support to the substitution hypothesis of share repurchases. Further, we develop modified versions of these models in which we assume that dividends or total payout changes are the result of a full adjustment to expected changes in earnings and a partial adjustment to unexpected changes in earnings. We show that these models appear to better reflect the changes in total payout policy that have occurred in recent years in the UK.

This thesis considers the existence of, and potential causes of, continuation and reversal effects in stock market returns. In the first part of the research, a timeseries approach is used to consider the profitability of momentum trading strategies on fourteen major stock market indices. Momentum trading strategies exploit continuation in returns, and evidence of significant profits to such strategies therefore implies the presence of continuation effects in the data samples. Significant losses to momentum strategies, on the other hand, are indicative of reversal effects in returns. This part of the research identifies continuation effects in stock index returns over periods of 1 trading day and 10 through 252 trading days. The second part of the research explores the various behavioural and nonbehavioural theories proposed in the literature for the existence of continuation and reversal effects in returns. Such effects imply that stock market trends differ systematically from trends in random data with the same underlying distribution of daily returns. An algorithm from the information technology literature is adapted and used to identify turning points in trends in the fourteen data sets, and the statistical properties of daily returns within stock market trends are analysed. Important patterns are observed in the steepness of trends and the volatility of returns within trends as they develop. These patterns enable some inferences to be drawn as to the most probable factors driving the continuation effects observed in the first part of the research, with nonsynchronous trading and investor loss aversion highlighted as potential causes of very short-term and medium-term effects respectively.

This thesis applies continuous time Markov chain theory to develop three different models for financial applications.

A covariance matrix of asset returns plays an important role in modern portfolio analysis and risk management. Despite the recent interests in improving the estimation of a return covariance matrix, there remain many areas for further investigation. This thesis studies several issues related to obtaining a better estimation of the covariance matrix for the returns of a reasonably large number of stocks for portfolio risk management. The thesis consists of five essays. The first essay, Chapter 3, provides a comprehensive analysis of both old and new covariance estimation methods and the standard comparison criteria. We use empirical data to compare their performances. We also examine the standard comparisons and find they provide limited information regarding the abilities of the covariance estimators in predicting portfolio variances. It therefore suggests that we need more powerful comparison criteria to assess covariance estimators. The second and third essays, Chapter 4 and 5, are concerned with the alternative appraisal methods of return covariance estimators for portfolio risk management purposes. Chapter 4 introduces a portfolio distance measure based on eigen decomposition (eigen-distance) to compare two covariance estimators in terms of the most different portfolio variances they predict. The eigen-distance measures the ratio of the two extreme variance predictions under one covariance estimator for the portfolios that are constructed to have the same variances under the other covariance estimator. We show that the eigen-distance can be used to assess a risk measurement system as a whole, where any kind of the portfolios may need to be considered. Our simulation results show that it is a powerful measure to distinguish two covariance estimators even in small samples. Chapter 5 proposes a0 measure to distinguish two similar estimated covariance matrices from the observed covariance matrix. 0 is constructed based on the essential difference of the two similar covariance matrices: the two extreme portfolios that are predicted to have the most different variances under these two matrices. We show that 0 is very useful in evaluating refinements to covariance estimators, particularly a modest refinement, where the refined covariance matrix is close to the original matrix. The last two essays, Chapter 6 and 7, are concerned with improving the best covariance estimators within the literature. Chapter 6 explores alternative Bayesian shrinkage methods that directly shrink the eigenvalues (and in one case the principal eigenvector) of the sample covariance matrix. We use simulations to compare the performance of these shrinkage estimators with the two best existing estimators, namely, the Ledoit and Wolf (2003a) estimator and the Jagannathan and Ma (2003) estimator using both RMSE and eigen-distance criteria. We find that our shrinkage estimators consistently out-perform the Ledoit and Wolf estimator. They also out-perform the Jagannathan and Ma estimator except in one case where they are not much worse off either. Finally, Chapter 7 extends the analysis of Chapter 6, which is under an unchanging multivariate normal world, to consider implications of both fat-tails and time variation. We use a multivariate normal inverse Gaussian (MNIG) distribution to model the log returns of stock prices. This family of distributions has proven to fit the heavy tails observed in financial time series extremely well. For the time varying situation, we use a tractable mean reverting Ornstein- Uhlenbeck (OU) process to develop a new model to measure an interesting and economically motivated time varying structure where the risks remain unchanged but stocks migrate among different risk categories during their life circles. We find that our shrinkage methods are also useful in both situations and become even more important in the time varying case.

In this thesis, I examine the following three temporal influences on the cross-section of stock returns: disclosure and analyst regulations, the subprime credit crisis, and time-varying investor sentiment. The thesis consists of three essays. The first essay deals with the influence of regulation. Between 2000 and 2003 a series of disclosure and analyst regulations curbing abusive financial reporting and analyst behavior were enacted to strengthen the information environment of U.S. capital markets. I investigate whether these regulations benefited investors by increasing stock market efficiency. After the regulations, I find a significant reduction in short-term stock price continuation following analyst forecast revisions and past stock returns. The effect was more pronounced among higher information uncertainty firms, where I expect security valuation to be most sensitive to the regulations. Further analysis shows that analyst forecast accuracy improved in these firms, consistent with reduced mispricing being due to an improved corporate information environment following the regulations. My findings are robust to controlling for time trends, trading activity, the recent financial crisis, and changes in firms’ analyst coverage status and delistings. In the second essay, I examine whether the value premium survived the recent subprime credit crisis. I find that value stocks underperformed growth stocks during the crisis, resulting in a value discount, while the value premium was significantly positive before the crisis. This is consistent with value stocks being riskier than growth stocks because they are more vulnerable during bad times. The value premium reversal during the crisis worked primarily through financially constrained firms, suggesting that the effect was due to the adverse influence of the crisis rather than confounding effects. The results are robust to controlling for common risk factors and alternative financial constraint proxies. The third essay is related to time-varying investor sentiment. Recent literature in financial economics has examined whether investor sentiment affects asset pricing. An open question is whether an investor sentiment effect reflects mispricing or risk compensation. Currently, the literature supports the former view by documenting that investor sentiment predicts realized stock returns beyond the explanatory power of state-of-the-art factor models. But, despite its popularity, estimating expected returns from realized returns has limitations. I re-examine the evidence on investor sentiment using accounting-based implied costs of capital (ICCs). I find that ICCs cannot explain the sentiment effect on stock returns. If ICCs are reliable expected return proxies, this suggests that the investor sentiment effect does not exist ex ante and confirms previous evidence that mispricing is the driving force behind the investor sentiment effect on stock returns.

Few studies have examined the impact of portfolio concentration upon the realised volatility of stock index portfolios, such as the FTSE 100. Instead, previous research has focused upon diversification across industries, across geographic regions and across different firms. The present study addresses this imbalance by calculating the daily time series of four concentration metrics for the FTSE 100 Index over the period from January 1984 through March 2003. In addition, the value weighted variance covariance matrix (VCM) of daily FTSE 100 Index constituent returns is decomposed into four sub-components: two from the diagonal elements and two from the off-diagonal elements of the VCM. These consist of the average variance of constituent returns, represented by the sum of diagonal elements in the VCM, and the average covariance represented by the sum of off-diagonal elements in the VCM. The value weighted average variance (VAV) and covariance (VAC) are each subdivided into the equally weighted average variance (EAV) the equally weighted average covariance (EAC) and incremental components that represent the difference between the respective value-weighted and equally weighted averages. These are referred to as the incremental average variance (IAV) and the incremental average covariance (IAC) respectively. The incremental average variance and the incremental average covariance are then combined, additively, to produce the incremental realised variance (IRV) of the FTSE 100 Index. The incremental average covariance and the incremental realised variance are found to be negative during the 1987 crash and the 1992 ERM crisis. They are also negative for a substantial part of the study period, even when concentration was at its highest level. Hence the findings of the study are consistent with the notion that the value weighted, and hence concentrated, FTSE 100 Index portfolio is generally less risky than a hypothetical equally weighted portfolio of FTSE 100 Index constituents. Furthermore, increases in concentration tend to precede decreases in incremental realised volatility and increases in the equally weighted components of the realised VCM. The results have important implications for portfolio managers concerned with the effect of changing portfolio weights upon portfolio volatility. They are also relevant to passive investors concerned about the effects of increased concentration upon their benchmark indices, and to providers of stock market indices.