Mergers and acquisitions (M&As) are important corporate strategy actions that are vital for the companies in order to survive in this competitive global world. The popularity of those actions has increased over the years, especially in the international domain. In the UK, both the number and value of cross-border M&As has increased significantly over the years. Despite this increase, there haven’t been enough studies or clear evidence about whether venturing abroad to acquire foreign targets leads the companies to better performance compared to staying domestically. Therefore, the purpose of this thesis is to investigate the M&A phenomenon deeply and compare between cross-border and domestic M&As made by UK public acquirer firms. More specifically, the thesis concentrates on three main issues which are: (1) the difference between the returns to shareholders of acquirer firms involved in cross-border and domestic M&As; (2) the difference between the operating performance of acquirer firms involved in cross-border and domestic M&As; and (3) the difference between the impacts of cross-border and domestic M&As on the operating performance of acquirer and target firms combined. Market-based and accounting-based approaches are used to investigate a sample of UK acquirer firms engaged in cross-border and domestic M&As both in the short-term and in the long-term periods. In general, the results reveal insignificant differences between the shareholders’ returns and operating performances of acquirer firms involved in cross-border and domestic M&As over the short- and long-term periods. On the other hand, the results for acquirer and target combined firms show that cross-border M&As have lower operating performances than domestic M&As. Recommendations are provided in order to help the decision and policy makers in the companies to decide whether cross-border M&As should be actively encouraged or discouraged in comparison with domestic M&As.

The use of multivariate discriminant analysis (MDA) and logistic regression procedure (Logit) in predicting business failure has been explored in numerous studies since 1960s. Recently, a newly developed technique, artificial neural networks (ANNs), has attracted much attention and has been applied to bankruptcy prediction area. At the same time, many papers attempted to compare the predictive ability of these two distinct classes of discriminators in order to find a best failure prediction method. However, most of their results, despite showing the superiority of ANNs, have been sharply criticised either for the unfair comparison or for their specific data selection. There is a need to undertake theory-based research to identify problem characteristics that predict when ANNs will forecast better than statistical models; to identify which input variable characteristics predict when ANNs will improve model estimation; and to identify when this advantage would give substantially improved forecasting performance. Motivated by the limited amount of research on investigating the relative effectiveness of traditional methods as compared to the ANNs under a wide variety of modelling assumptions, one of the objectives of this study is to compare their classification capacities on a theoretical basis, and to evaluate the robustness on certain situations through the simulation study. The investigation is conducted on two popular statistical techniques—the MDA and the Logit, as well as two different learning algorithms of ANNs—the standard generalised delta rule (GDR) and the Projection approach (Proj). This can be regarded as the horizontal assessments of bankruptcy prediction. The other aim of this thesis is to evaluate the impacts of variations in failure prediction models through the empirical study. These variations involve the issues we often encounter in the real world, such as the different sizes of sample, a choice-based sampling bias, the sensitivity of optimal cutoff points to misclassification costs of Type I and Type II errors, and the imbalance of the composition of failed to nonfailed firms between training and testing data sets. This can be viewed as the vertical assessments of bankruptcy prediction. The simulation results indicate that the neural networks are indeed competitive approaches on bankruptcy prediction. In particular, the Projection network, which was developed to overcome the drawbacks that a commonly used GDR backpropagation algorithm often experiences, proves its remarkable superiority not only quantitatively (i.e., lower overall accuracy), but also qualitatively (lower Type I and Type II errors). The Projection network holds a promise for future elaboration. Moreover, the outcomes of empirical experiments enhance our knowledge of some factors in constructing a failure forecasting model. This knowledge is related to both traditional statistical tools and modem neural networks and is essential for decision making.

The objective of this thesis is to develop and analyse two stochastic control problems arising in the context of investment capacity expansion. In both problems the underlying market fluctuations are modelled by a geometric Brownian motion. The decision maker’s aim is to determine admissible capacity expansion strategies that maximise appropriate expected present-value performance criteria. In the first model, capacity expansion has price/demand impact and involves proportional costs. The resulting optimisation problem takes the form of a singular stochastic control problem. In the second model, capacity expansion has no impact on price/demand but is associated with fixed as well as proportional costs, thus resulting in an impulse control problem. Both problems are completely solved and the optimal strategies are fully characterised. In particular, the value functions are constructed explicitly as suitable classical solutions to the associated Hamilton-Jacobi-Bellman equations

A decision maker, when facing a decision problem, often considers several models to represent the outcomes of the decision variable considered. More often than not, the decision maker does not trust fully any of those models and hence displays ambiguity or model uncertainty aversion. In this PhD thesis, focus is given to the specific case of asset allocation problem under ambiguity faced by financial investors. The aim is not to find an optimal solution for the investor, but rather come up with a general methodology that can be applied in particular to the asset allocation problem and allows the investor to find a tractable, easy to compute solution for this problem, taking into account ambiguity. This PhD thesis is structured as follows: First, some classical and widely used models to represent asset returns are presented. It is shown that the performance of the asset portfolios built using those single models is very volatile. No model performs better than the others consistently over the period considered, which gives empirical evidence that: no model can be fully trusted over the long run and that several models are needed to achieve the best asset allocation possible. Therefore, the classical portfolio theory must be adapted to take into account ambiguity or model uncertainty. Many authors have in an early stage attempted to include ambiguity aversion in the asset allocation problem. A review of the literature is studied to outline the main models proposed. However, those models often lack flexibility and tractability. The search for an optimal solution to the asset allocation problem when considering ambiguity aversion is often difficult to apply in practice on large dimension problems, as the ones faced by modern financial investors. This constitutes the motivation to put forward a novel methodology easily applicable, robust, flexible and tractable. The Ambiguity Robust Adjustment (ARA) methodology is theoretically presented and then tested on a large empirical data set. Several forms of the ARA are considered and tested. Empirical evidence demonstrates that the ARA methodology improves portfolio performances greatly. Through the specific illustration of the asset allocation problem in finance, this PhD thesis proposes a new general methodology that will hopefully help decision makers to solve numerous different problems under ambiguity.

This thesis examines the quality of Malaysian interim financial reports (interims) and the impact of corporate governance on the quality. The quality of interims is proxied by timeliness; compliance with the FRS 134, Interim Financial Reporting; compliance with the Bursa Malaysia Listing Requirements (BMLR); and comparability of profit and loss items when they were originally issued and placed in the next year’s corresponding quarter and comparison against the annual reports. Two methods are used to assess the quality of interims namely dichotomous and continuous. The first method provides one score for each proxy if it is in compliance and zero score otherwise and the latter method use the actual values. This thesis has found that the quality of interims is remarkably high for each proxy if a dichotomous method is used and it is moderate for continuous method. The lower quality is due to timeliness and comparability, because Malaysian companies are inclined to publish interims towards the end of the allowable period and most of the interims’is remarkably high for each proxy if a dichotomous method is used and it is moderate for continuous method. The lower quality is due to timeliness and comparability, because Malaysian companies are inclined to publish interims towards the end of the allowable period and most of the interims’ profit and loss items are not comparable. Consequently, compliance with the FRS 134 contributes the most to the quality of interims, while comparability contributes the least. Corporate governance is proxied by the frequency of directors’ meetings, independence, financial literacy, corporate governance expertise, and the ethnicity of directors. This thesis has found that all corporate governance variables are associated with the quality of interims except independence and corporate governance expertise. Despite these associations, multivariate regression reveals that the impact of corporate governance on the quality of interims is very low. These findings have implications for several users such as Malaysian regulatory bodies to ensure that PLC complied with the interim reporting standards; policymakers to ensure there is no misapplication of provision of accounting standards; protect shareholders to appoint appropriate composition of directors; and academicians for future research.

The thesis contributes to the literature in the following ways: First it contributes to the body of literature by extending our knowledge on the predictive ability of alternative Unconditional methodologies. Second it adds to the body of litareture by providing practical tests so as to assess the performance of Conditional models. Third the thesis extends our knowledge on the sensitivity of utilising different portfolio formation criteria, while testing both Unconditional and Conditional asset pricing inferences. Fourth it contributes to the body of literature by extending our knowledge on Unconditional and Conditional beta models and their comparative performance. Fifth the thesis adds to the existing literature by estimating the Industry cost of capital, using the following different models, Unconditional, Conditional, the Arbitrage Pricing Model and the Capital Asset Pricing model. Thus provides empirical evidence using a practical application, estimation of the Industry cost of capital, of which model provides a better description of UK returns. Chapter 4 introduces the portfolio returns used in the thesis and examines the size, price earnings ratio, dividend yield effect and their interactions. The time-series of the primary portfolios start in 1956 and ends in 1996. We find that for the 1976-1996 period, that the dividend yield and PE effect subsume the size effect. However the PE effect subsumes the dividend yield effect and it is the PE effect that is the most dominant. The best documented of all stock market effects, the small-firm premium went into reverse during 1989-1996. The size effect lives on, but for the latest decade, it is the largest firms that outperform the smallest ones by 10.26% per annum. Chapter 5, which examines Unconditional models, aims to examine the predictive ability of alternative Unconditional methodologies. Another objective that is explored is the sensitivity of results to different grouping techniques, of size; PE ratio and dividend yield portfolio groupings. The third issue examined entails the identification of priced factors in the UK market, over a twenty year of period, (1976-1996), and for a data-set (approximately 6000 companies), which provides a complete history of firms traded on the London Stock exchange, inclusive of Unlisted securities market. We find that that the choice of one methodology over another has important implications and that there is a sensitivity of results to different portfolio groupings. Chapter 6, which examines Conditional models, i. e., conditioned on a set of instrumental variables, models the dynamic behaviour of portfolio returns using a Conditional Asset Pricing Model and examine the behaviour of macroeconomic risk premiums over time. We provide practical tests of Conditional Asset Pricing Models and forecast (i) the sign of the price of risk using the probit model, (ii) the magnitude of the price of risk and (iii) portfolio returns for the size, PE ratio and dividend yield portfolios. We find that the instrumental variables show ability to predict variation of the price of risk of the return on FTSE, S&P 500, unexpected UK stock exchange turnover, change in money supply, imports, inflation and portfolio returns. Chapter 7 compares first Unconditional (constant) and Conditional (time-varying & conditioned on a set of instrumental variables) beta models and second the CAPM and the APM, estimates the industry cost of capital. We find differences, between constant unconditional betas and conditional betas cost of capital. The average Mean Square Error (MSE) for the conditional betas are smaller compared to constant betas. Moreover we find that the CAPM has larger MSE not only compared to the APT model with conditional betas, but with APT with unconditional betas. The Conditional beta model provides the best description of UK returns. We also run Monte Carlo simulations and test the statistical significance of the errors of the Conditional beta model. We find the errors to be statistically insignificant.

The purpose of this research was to use Bayesian statistics to develop flexible mortality models that could be used to forecast human mortality rates. Several models were developed as extensions to existing mortality models, in particular the Lee-Carter mortality model and the age-period-cohort model, by including some of the following features: age-period and age-cohort interactions, random effects on mortality, measurement errors in population count and smoothing of the mortality rate surface. One expects mortality rates to change in a relatively smooth manner between neighbouring ages or between neighbouring years or neighbouring cohorts. The inclusion of random effects in some of the models captures additional fluctuations in these effects. This smoothing is incorporated in the models by ensuring that the age, period and cohort parameters of the models have a relatively smooth sequence which is achieved through the choice of the prior distribution of the parameters. Three different smoothing priors were employed: a random walk, a random walk on first differences of the parameters and an autoregressive model of order one on the first differences of the parameters. In any model only one form of smoothing was used. The choice of smoothing prior not only imposes different patterns of smoothing on the parameters but is seen to be very influential when making mortality forecasts. The mortality models were fitted, using Bayesian methods, to population data for males and females from England and Wales. The fits of the models were analysed and compared using analysis of residuals, posterior predictive intervals for both in-sample and out-of-sample data and the Deviance Information Criterion. The models fitted the data better than did both the Lee-Carter model and the age-period-cohort model. From the analysis undertaken, for any given age and calendar year, the preferred model based on the Deviance Information Criterion score, for male and female death counts was a Poisson model with the mean parameter equal to the number of lives exposed to risk of dying for that age in that calendar year multiplied by a mortality parameter. The logit of this mortality parameter was a function of the age, year (period) and cohort with additional interactions between the age and period parameters and between the age and cohort parameters. The form of parameter smoothing that suited the males was an autoregressive model of order one on the first differences of the parameters and that for the females was a random walk. Moreover, it was found useful to add Gaussian random effects to account for overdispersion caused by unobserved heterogeneity in the population mortality. The research concluded by the application of a selection of these models to the provision of forecasts of period and cohort life expectancies as well as the numbers of centenarians for males and females in England and Wales. In addition, the thesis illustrated how Bayesian mortality models could be used to consider the impact of the new European Union solvency regulations for insurers (Solvency II) for longevity risk. This research underlined the important role that Bayesian stochastic mortality models can play in considering longevity risk.

This dissertation consists of a set of four sole-authored, reviewed and published papers which develop the theme that company accounting policies, particularly those relating to asset valuation, depreciation and dividend policy, developed in response to a change in the general perception of the nature of the property rights of the original owners. The original owners of commercial and industrial concerns in the early nineteenth century were the partners. After incorporation they and others became the shareholders of the company. The origin of commercial and industrial companies as partnerships influenced the British government and legal thinking for nearly a century from 1856 to 1945, the date of the Cohen Committee. It was the age of laissez-faire and has been much discussed: Parliament was less keen to intervene in connection with the generality of companies, where the view expressed in 1856 by Robert Lowe, then President of the Board of Trade, that `having given [companies] a pattern the State leaves then to manage their own affairs ... (quoted in Hein, 1978, p. 149) provided a rationalization of the widespread belief that it was no business of the state to interfere in what were seen as private contracts between shareholders (Sugarman and-Rubin, 1984, p. 12). Moreover, a laissez-faire approach on the part of the courts, where `formalist' views were at their height (Atiyah, 1979, pp. 388-97), seems also to have affected the attitude to accounts on the part of the courts. This may be observed in the series of `dividend' cases in the nineteenth century (French, 1977) where judges on the whole were loath to go beyond the companies' Articles of Association and the latter of the Companies' Acts (unless they could adduce fraudulent or improper behaviour on the part of directors) in assessing matters of valuation, income measurement and profit determination. (Napier and Noke, 1992, p. 38, emphasis added). These issues `matters of valuation, income measurement and profit determination' form the basis for much of this dissertation. There is one further paper extending the work of Jefferys (1938) and Cottrell (1980) on the format of share issues from 1914 (Pitts 2000).

This dissertation presents an effort to implement nonlinear dynamic tools adapted from chaos theory in financial applications. Chaos theory might be useful in explaining the dynamics of financial markets, since chaotic models are capable of exhibiting behaviour similar to that observed in empirical financial data. In this context, the scope of this research is to provide an insight into the role that nonlinearities and, in particular, chaos theory may play in explaining the dynamics of financial markets. From a theoretical point of view, the basic features of chaos theory, as well as, the rationales for bringing chaos theory to the attention of financial researchers are discussed. Empirically, the fundamental issue of determining whether chaos can be observed in financial time series is addressed. Regarding the latter, empirical literature has been controversial. A quite exhaustive analysis of the existing literature is provided, revealing the inadequacies in terms of methodology and the testing framework adopted, so far. A new "multiple testing" methodology is developed combining methods and techniques from the fields of both Natural Sciences and the Economics, most of which have not been applied to financial data before. A serious effort has been made to fill, as much as possible, the gap which results from the lack of a proper statistical framework for the chaotic methods. To achieve this the bootstrap methodology is adopted. The empirical part of this work focuses on the comparison of two markets with different levels of maturity; the Athens Stock Exchange (ASE), an emerging market, and London Stock Exchange (LSE). Our aim is to determine whether structural differences exist in these markets in terms of chaotic dynamics. In the empirical level we find nonlinearities in both markets by the use of the BDS test. R/S analysis reveals fractality and long term memory for the ASE series only. Chaotic methods, such as the correlation dimension (and related methods and techniques) and the largest Lyapunov exponent estimation, cannot rule out a chaotic explanation for the ASE market, but no such indication could be found for the LSE market. Noise filtering by the SVD method does not alter these findings. Alternative techniques based on nonlinear nearest neighbour forecasting methods, such as the "piecewise polynomial approximation" and the "simplex" methods, support our aforementioned conclusion concerning the ASE series. In all, our results suggest that, although nonlinearities are present, chaos is not a widespread phenomenon in financial markets and it is more likely to exist in less developed markets such as the ASE. Even then, chaos is strongly mixed with noise and the existence of low-dimensional chaos is highly unlikely. Finally, short-term forecasts trying to exploit the dependencies found in both markets seem to be of no economic importance after accounting for transaction costs, a result which supports further our conclusions about the limited scope and practical implications of chaos in Finance.

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.