A number of previous studies have assessed whether insider trades convey information to the market. Whilst lots of research has been undertaken in the past three decades, the issue is still contentious and there is ongoing debate concerning the legality and profitability of insider trading. Regulators restrict insider trading before any material news announcements, so that insider cannot take advantage of private information. Motivated by the studies in individual and aggregate insider trading, the objective of this thesis is to understand the role of signals that insiders send to the financial market about their companies when they trade, in general and to assess the role of insider trading in the context of initial public offerings (IPOs), particularly under lockup restrictions and the high information asymmetries inherent in IPOs.

The aim of this thesis is to investigate the effects of foreign exchange order flows on exchange rate and stock market changes, in particular to examine the forecasting power of order flows and better understand the nature of the private information conveyed in order flows in the foreign exchange market. Chapter 1 investigates the performance of foreign exchange customer order flows (six major exchange rates over 3.5 years) as an additional explanatory variable to technical analysis to forecast exchange rate changes by applying genetic algorithm non-linear methodology. Using the interval permutations technique, we suggest that the improvement of order flows to the performance of technical analysis is not consistently present. Chapter 2 examines the role daily customer GBPUSD order flows play in explaining concurrent and future stock market changes in both UK and US, and discusses the heterogeneous effects from different groups of customers. The basic hypothesis tested is that if foreign exchange order flows have days-ahead effects on future stock market changes, it suggests that at least a part of the information carried by foreign exchange order flows is relevant for stock markets. Using daily GBPUSD order flows over 3.5 years from 2002 to 2006 provided by RBS, we find that: 1) impacts of order flows from corporate customers on stock markets are positive, while impacts of order flows from unleveraged financial institutions are negative; 2) impacts of corporate order flows are longer than those of financial order flows, especially for the US stock market, suggesting that the two groups of customers may hold different types of private price-relative information. We hypothesize that corporate customers of the bank are mainly based in the UK. When the world economy is doing well, multi-national companies are selling more goods in the US and repatriate more foreign currencies back to UK, during which more GBP or EUR are converted from US Dollars. More sales of US Dollars then reflect the good future prospects of the world economy and stocks listed in both US and UK will rise in value. For unleveraged financial institutions, when the world economy is going bad, clients of those mutual funds which are based in the UK will ask for redemptions of their funds. Assuming the bank services a client base that is UK oriented, this leads to the repatriation of money from abroad back to UK. The buying of GBP or EUR in exchange for US Dollars then takes place alongside sales of US and UK stocks. Foreign exchange flows into GBP or EUR from unleveraged funds forecast poor future stock market returns globally. Chapter 3 empirically tests the effects of EURUSD order flows from different groups of counterparties on the US stock market changes at high frequencies ranging from 1-minute to 30-minute, using a unique set of tick-by-tick order flows data obtained from a leading European commercial bank. We find that: 1) Order flows from “corporates” are positively related to exchange rate changes, while order flows from “financials” are negatively signed, which contradict many well-documented papers such as Evans and Lyons (2002a) (this high frequency forecasting power partly explain the failure of the trading strategy based on our daily order flows data in chapter); 2) The effects of order flows from “financials” are negative on stock market changes, while the effects of orders from “corporates” are positive on stock market changes, which further confirms our findings in chapter 2. Similar to chapter 2, the cross market effects documented in chapter 3 also suggest that there is information content in foreign exchange order flows and that it is likely to be macroeconomic in nature, relevant for stock markets. Chapter 4 concludes and suggests some directions of refinements and further research.

This thesis addresses the problem of quantitatively evaluating the temporal dynamics that characterized financial time series. In particular, we perform an accurate analysis of the Fourier estimator, a newly proposed nonparametric methodology to measure ex-post volatility and cross-volatilities as functions of time, when financial assets are observed at different highfrequency levels over the day. The estimator has the peculiar feature to employ the observed data in their original form, therefore exploiting all the available information in the sample. We first show how to considerably improve the numerical performance of the Fourier method making possible the analysis of large sets of data, as it is usually the case with high-frequency series. Secondly, we use Monte Carlo simulation methods to study the behavior of three driving parameters in the estimation procedure, when the effects of both irregular sampling and microstructure noise are taken into account. The estimator is showed to be particularly sensitive to one of these quantities, which is in turn used to control the contribution of the above effects. Integrated financial correlation is also analyzed within two distinct comparative studies that involve other multivariate measures. The analysis is then extended to consider the entire evolution of the underlying correlation process. Finally, we propose a new class of nonparametric spot volatility estimators, which is showed to include the Fourier method as a particular case. The full limit theory under infill asymptotics in the pure diffusive settings of the class is derived. Empirical evidence in support of our conclusions is also provided.

This thesis investigates the factors influencing an academics involvement with industry and how these collaborations a¤ect research outputs in terms of publications and patents. It employs a longitudinal dataset that comprises more than 4000 engineering academics over 20 years and a smaller subsample of 479 academics over 12 years and uses robust econometric approaches to address issues of unobserved heterogeneity and endogeneity. Collaboration with industry is measured through two funding modes: (1) funding received from industry directly and (2) funding from the research council that involves business partners. The thesis is unique in its ability to measure two distinct types of funding over a long time period and compare these to commercialisation efforts of academics. It analyses the relationship between both activities and relates it to academics publication numbers. Considering all three activities jointly allows some new insights into their complementarities as well as identifying possible substitution effects.

This thesis addresses the topic of open market share repurchases in Europe over the period 1997 to 2006. This thesis strives to document and clarify the managerial motives as well as the market perception and respective reaction to open market share repurchases, in a cross country framework. Therefore this thesis delves into the hypotheses that have been developed in the literature for interpreting these issues. The theories and hypotheses investigated in this thesis are mainly the information asymmetry and signalling for undervaluation, the tax hypothesis, the dividend substitution, the capital structure adjustment, and agency costs hypotheses under varying regulatory and institutional frameworks. Consistent with the U.S. evidence, share repurchases are popular in the U.K., but I find that the market does not have the same level of reaction as in the U.S. For Germany and France, share repurchase activity has been a more recent phenomenon, but not common. Nevertheless due to recent regulatory changes, this trend seems to be changing in favour of share repurchases. The empirical evidence in this thesis shows that market reaction to the announcement of intention to repurchase shares in the open market varies significantly among countries, and that the market becomes more accustomed to subsequent announcements made by the same firms. Furthermore, I find that ownership concentration, firm size, leverage, and in some cases past share price performance, have a significant impact on the market reaction, as well as on the managerial motives for announcing an open market share repurchase programme. Moreover, the evidence shows that not all the managerial motives and drivers of the market reaction have a uniform impact throughout the varying markets. Rather, it is only a number of firm characteristics that consistently influence the likelihood of an open market share repurchase in all three countries. Furthermore, I find that firms on average repurchase approximately three quarters of the shares targeted at the time of the announcement, suggesting that on average, firms repurchase a substantial portion but not the intended amount. In addition, I find that managers repurchase shares in order to provide price support. Finally, this thesis provides evidence that it is the actual trades and their respective reporting, and not the repurchase announcement itself that convey risk related information to the market. Therefore, the reporting of the actual repurchase trades sends positive signals to the market, which are reflected on the reduction of firms’ systematic risk.

Traditional models of analyzing the general insurance market often focus on the behavior of a single insurer in a competitive market. They assume that the major players in this market are homogeneous and have a common goal to achieve a same long-term business objective, such as solving profit (or utility) maximization. Therefore these individual players in the traditional models can be implemented as a single representative economic agent with full rationality to solve the utility optimization. To investigate insurance pricing (or underwriting) cycles, the existing literature attempts to model various isolated aspects of the market, keeping other factors exogenous. We and that a multi-agent system describing an insurance market affords a helpful understanding of the dynamic interactions of individual agents that is a complementary to the traditional models. Such agent-based models (ABM) try to capture the complexity of the real world. Thus, economic agents are heterogeneous and follow divergent behavioral rules depending on their current unique competitive situations or comparative advantages relating to, for example, their existing market shares, distribution channels, information processes and product differentiations. The real-world continually-evolving environment leads agents to follow common rules of thumb to implement their business strategies, rather than completely be utility-maximizer with perfect foresight in an idealized world. The agents are adaptively learning from their local competition over time. In fact, the insurance cycles are the results of these dynamic interactions of agents in such complex system.

This thesis consists of three inter-related chapters designed to study the effects of fiscal policy on unemployment, the distribution of income, and social welfare in heterogeneous agent models incorporating unemployment. Each chapter employs a different setup for unemployment in a general equilibrium framework. These include models of equilibrium unemployment, right-to-manage union bargaining, and search and matching. Chapter 1 develops a model with equilibrium unemployment to study the effects of optimal taxation under commitment. Two models are explored: a model with zero economic profits and a model with non-zero economic profits due to the presence of productive public investment. We find that the optimal policy in these two models results in a different labour wedge which defines the gap between the marginal rate of substitution between labour and consumption and the marginal product of labour. In particular, the labour wedge can only be completely eliminated when the profits are absent from the model. It is further demonstrated that there exists a trade-off between efficiency and equity for the government in the model with non-zero economic profits. Chapter 2 examines the importance of imperfect competition in labour and product markets in determining the welfare effects of tax reforms assuming agent heterogeneity in capital holdings. The analysis shows that each of these market distortions, independently, results in welfare losses for at least one segment of the population after a capital tax cut and a concurrent labour tax increase. However, with both present in the model, the tax reform is Pareto improving in a realistic calibration to the UK economy. Chapter 3 extends a Mortensen-Pissarides search-and-matching framework with household heterogeneity to investigate the importance of search frictions in determining the welfare and distributional effects of tax reforms which re-allocate the tax burden from capital to labour income. The optimal tax policy under commitment is also analysed. We find that the tax reforms are Pareto improving in the long run, despite welfare losses for at least one segment of the population in the transition period. Finally, the long-run Ramsey policy implies a negative capital tax which is associated with a rise in the labour tax and a fall in the unemployment benefit.

This thesis investigates characteristics of the prices of shares traded on the Istanbul Stock Exchange (ISE), an important and fast-growing market. We look at five issues: the shape of the distribution of daily returns the predictability of these returns the presence of day-of-the-week effects in the mean and variance of returns the behaviour of the mean and variance of returns around stock split and dividend dates and the predictability of variances, and in particular the performance of adaptive models relative to the GARCH models. Our main findings can be summarised as follows. First, the hypothesis of normality is rejected, mainly due to excess kurtosis. To explain excess kurtosis, we used an autoregressive conditional heteroskedastic (ARCH) model, and a GARCH(1,1) model is found to fit the ISE index data well. A significant further finding, based on a t-GARCH-M model is that in the early years of the exchange, mean returns were significantly influenced by the returns variance. Second, standard tests for serial correlation, and for runs of same-sign returns, show that the hypothesis of a random walk can be rejected, with index returns showing significant first and second order serial correlation. Again, these effects are stronger in the early years of the exchange. Third, using a GARCH model, we find no strong evidence of the day of the week effect in mean returns on the index or on the 20 actively traded companies. But there is evidence to suggest that the market is more volatile on Mondays and after holidays. Again, these effects are not stable over time. Taken together, these results point to the market becoming progressively more efficient and more integrated with the international capital market over the period of the study. Fourth, the results from the EV-GARCH model, a GARCH model with event dependent intercept terms, a technical novelty, show that there is no effect on mean returns from stock dividends. Surprisingly, cash dividends do cause returns to rise/fall after their payment. On the other hand, stock dividends do significantly increase the variance of returns around the event day, and for several weeks thereafter. Finally, although we have characterised the daily returns series by an autoregressive model with a GARCH process for volatility, it turns out that the GARCH model does not unambiguously dominate alternatives in forecasting and trading applications. In 5- to 20-day ahead forecasts, the GARCH model is slightly more accurate than four alternatives, including exponential smoothing models (RiskMetrics) and historic volatility. However, it is (inevitably) less accurate than a model which pools forecasts from all models. In a simulated options market - another technical innovation of the thesis - we find that traders using a GARCH model would on balance lose money to traders using other methods, in spite of the apparently greater accuracy of the GARCH forecasts. This confirms for volatility forecasts an important result which is already know to hold for mean forecasts - that in forecasting financial markets, there is little correlation between meansquare accuracy and trading profitability.

Implementation of the database concept has organisational implications, the most widely recognised being the emergence of the specialist function of data or database administration (DBA) 'usually in data processing departments. This research study is an investigation of U.K. database projects, undertaken to obtain a view of the managerial issues and organisational consequences which arise in practice. The findings are based on the completion of a simple postal Questionnaire by 212 organisations with database projects and in-depth interviews in 21 of these organisations with the staff member bearing overall responsibility for the development. The surveys were conducted in 1976-1981. The study employs qualitative methods to explore the inter-dependencies between organisations' objectives for embarking on a database project, features of the project environment, approaches to staffing including characteristics of the DBA function and the nature of the problems faced. The major finding is that organisations experience more serious 'political ~ problems than technical ones. The predominant type of political problem is not resistance to data sharing, but rather related to the emergence of a DBA function-dissatisfaction over organisational placemen.t and the working relationship with data processing functions such as systems development. Moreover these organisational problems occur from the earliest phase of database projects - the feasibility stage. The study concludes that with management backing, analysis and design skills and control over database usage, the DBA function has been able to acquire technical and political weight in organisations in a relatively short period of time. The current data processing environment however contains unique opportunities for the function - in the areas of systems planning, systems development and the administration of shared data - which suggests that the notion of the DBA as the organisation's overall data resource manager is still a viable one.

This thesis examines the economic evaluation of forecasting strategies based on past prices, bringing together academics and practitioners techniques Forecasting methods based on past prices are convex and path-dependent dynamic strategies Therefore, they must be able to profitably exploit positive serial dependences in financial prices The most important measure of financial forecasting ability is the rate of return achieved by the predictor The expected return of forecasting strategies is first investigated by applying stochastic modelling Then, the presence of serial dependences in financial prices is tested by comparing expected and observed rates of returns of forecasting strategies According to the academic literature, the expected return of investment strategies is best established by applying stochastic modelling That is done analytically for linear forecasters, assuming that the underlying process of asset returns is not only a random walk with drift but any Gaussian processes The rate of return from financial strategies is zero under the assumption of a random walk without drift, and non-zero in all the other cases Then, it is shown that many forecasting techniques used by market participants are in fact linear forecasters and consequently fall in the scope of this study. Minimising the mean squared error is a sufficient but not necessary condition to maximise returns Under the random walk without dnft assumption, error measures and profits arenegatively correlated but very few in absolute value Only the directional accuracy exhibits high degree of linear association with profits When the true Gaussian process is not known, there are cases for which a decrease in mean squared error does not imply an increase in returns Therefore the mean squared error criterion is of poor use to maximise returns when the true model is not known The directional accuracy is of no further help Market timing ability tests based on the percentage of correct forecasts have very low power in presence of low positive autocorrelations. It is why a test of the random walk hypothesis based on the joint profitability of trading rules is investigated It happens to be powerful against a broad range of linear alternatives Its ruee feature is to exhibit a power almost equal to the best of its components unknown when the true model is unknown It constitutes as well a tool to separate mean from variance non-hnear models Simple tests of adequacy of Gaussian processes are subsequently proposed from the joint profitability of trading rules Applying previous tests, the random walk hypothesis is rejected for daily exchange rates against Dollar, over the period 1982-1992 The hypothesis of normal underlying returns is very weak compared to the independence assumption Among a few Gaussian processes, the price-trend model along with some technical models appear to be the best alternatives to explain observed trading rule returns Statistical forecasters based either on ARMA(1,1) or fractional Gaussian processes do not outperform simple technical rules Taking Into account transaction costs reduce profits to zero for individual but not for institutional Investors who might have to act on strategies that assume the foreign exchange markets exhibit positive dependencies, if not inefficiencies.