Online stock forums have become a vital investing platform on which to publish relevant and valuable user-generated content (UGC) data such as investment recommendations and other stock-related information that allow investors to view the opinions of a large number of users and share-trading ideas. This thesis applies methods from computational linguistics and text-mining techniques to analyse and extract, on a daily basis, sentiments from stock-related micro-blogging messages called “StockTwits”. The primary aim of this research is to provide an understanding of the predictive ability of stock micro-blogging sentiments to forecast future stock price behavioural movements by investigating the various roles played by investor sentiments in determining asset pricing on the stock market. The empirical analysis in this thesis consists of four main parts based on the predictive power and the role of investor sentiment in the stock market. The first part discusses the findings of the text-mining procedure for extracting and predicting sentiments from stock-related micro-blogging data. The purpose is to provide a comparative textual analysis of different machine learning algorithms for the purpose of selecting the most accurate text-mining techniques for predicting sentiment analysis on StockTwits through the provision of two different applications of feature selection, namely filter and wrapper approaches. The second part of the analysis focuses on investigating the predictive correlations between StockTwits features and the stock market indicators. It aims to examine the explanatory power of StockTwits variables in explaining the dynamic nature of different financial market indicators. The third part of the analysis investigates the role played by noise traders in determining asset prices. The aim is to show that stock returns, volatility and trading volumes are affected by investor sentiment; it also seeks to investigate whether changes in sentiment (bullish or bearish) will have different effects on stock market prices. The fourth part offers an in-depth analysis of some tweet-market relationships which represent an open problem in the empirical literature (e.g. sentiment-return relations and volume-disagreement relations). The results suggest that StockTwits sentiments exhibit explanatory power in explaining the dynamics of stock prices in the U.S. market. Taking different approaches by combining text-mining techniques with feature selection methods has proved successful in predicting StockTwits sentiments. The applications of the approach presented in this thesis offer real-time investment ideas that may provide investors and their peers with a decision support mechanism. Investor sentiment plays a critical role in determining asset prices in capital markets. Overall, the findings suggest that investor sentiment among noise traders is a priced factor. The findings confirm the existence of asymmetric spillover effects of bullish and bearish sentiments on the stock market. They also suggest that sentiment is a significant factor in explaining stock price behaviour in the capital market and imply the positive role of the stock market in the formation of investor sentiment in stock markets. Furthermore, the research findings demonstrate that disagreement is not only an important factor in determining trading volumes but it is also considered a very significant factor in influencing asset prices and returns in capital markets. Overall, the findings of the thesis provide empirical evidence that failure to consider the role of investor sentiment in traditional finance theory could lead to an imperfect picture when explaining the behaviour of stock prices in stock markets.

This thesis explores the characteristics of risk, risk aversion, and risk measures in the emerging stock market of Saudi Arabia.

Nanoelectrochemistry explores the differences in chemical behaviour at the nanoscale as compared to the macro-scale. This thesis is concerned with nanoelectrochemistry and aims to develop and apply novel experiments for the unambiguous identification of changed electrode kinetics at the nanoscale. This is challenging since electrochemical responses are controlled by diverse factors like enhanced mass transport and adsorption as well as electron transfer kinetics. A joint computational and experimental strategy is employed. Chapter 1, 2 and 3 cover essential introductory material and basic experimental details relevant to all experiment. Fuller descriptions and details are given in the following chapters as and when needed. Chapter 4 reports the development of an electrochemical characterization method, to achieve a fast and simple quantification of the average particle size and the number of nanoparticles deposited on a glassy carbon electrode. The method consists of surface area characterization by underpotential deposition of lead particles and the determination of the amount of gold from anodic stripping in HCl. This method is also proven to be effective by comparing the results with SEM measurements. Next, in chapter 5, a generic strategy combining computation and experimental approach is developed in order to study the electron transfer kinetics of gold nanoparticles. The modelling part considers the kinetics of the electrochemical process on the bulk materials for different regions in the electrode, that is, the substrate (glassy carbon) and the nanoparticles (gold). Comparison of experimental and theoretical results enables the detection of changes in the electrode kinetics at the nanoscale. This approach is applied into the electro-oxidations of nitrite and L-ascorbic acid for gold nanoparticles from 20 - 90 nm. In the former, analysing the system shows that no change in electron transfer kinetics is involved in the process, even though a decrease of the over-potential and an increase in the peak current are observed. But these changes reflect mass transport effects, not electrocatalysis. A case where an authentic enhanced electron transfer kinetic change occurs is shown for the ascorbic acid system. Finally, in chapter 6 , the above strategy is exploited further to apply a quantitative study of electron transfer kinetics for various sizes of gold nanoparticles in the oxygen reduction reaction system in sulphuric acid at 298 K. The latter is at the heart of energy transformation techniques (fuel cells, battery and so on). Compared with the electron transfer kinetics on macro gold electrodes, there is no change at gold nanoparticles from size 5 nm to 40 nm. However, in the presence of Pb(II), a strong enhancement of electron transfer kinetics is observed on 5 nm citrate capped gold nanoparticles for ORR. On the other hand, a significant decrease of electron transfer kinetics has been found for gold nanoparticles of size 2 nm for ORR. The latter observation of strong negative electrocatalysis is also observed for the hydrogen evolution reaction (HER). This represents the first report of such effects with the HER system. Overall the thesis has established a rigurous, theoretical basis for evaluating electrocatalysis in nanoparticulate system.

In the thesis, we investigate whether hedge fund managers have liquidity timing skills in the fixed income market, foreign exchange market and commodity market, respectively. Managers with the liquidity timing skills can strategically adjust hedge funds exposure to the target financial market based on their forecasts about the future changes in market liquidity. We find empirical evidence that hedge funds in certain categories have the skills to time the liquidity levels in the fixed income market, foreign exchange market and commodity market. We conduct a range of robustness tests, which show that hedge funds still exhibit liquidity timing skills after controlling for the factors that may affect timing ability. In particular, our findings are robust to the usage of leverage, funding constraints, investor redemption restrictions, hedge funds trades on market liquidity, financial crisis, hedge fund data biases, market return and volatility timing, liquidity risk factor, systematic stale pricing and option factors. We also conduct bootstrap analysis to ensure the results are not dependent on the normality assumption. Our investigation is helpful to understand the importance of market liquidity to hedge funds professional portfolio management.

This dissertation seeks to improve the current understanding of the ways in which institutional reform can promote the development of stock markets in Latin America. Over the past decade, policymakers sought to stimulate the growth of capital markets in the region through the promotion of a standardized set of formal institutions. An example of this approach in the field of company law was the introduction of modern corporate governance practices into nations without a solid enforcement infrastructure. By most accounts, these efforts did not deliver on their promise of stock market development. This work identifies areas for potential reform. As a means to better understand the operation of Latin American stock markets, this dissertation draws from different sources, including the historical experience of industrialized nations, the available literature on institutional reform, the documented shortcomings of legal reform programmes and hand-collected data from various Latin American countries. The resulting analysis suggests that the promotion of Latin American capital markets may require strategies different to those that were set in motion over the past decade. The main contribution of this work is twofold. First, this dissertation brings some nuance to the discussions concerning the challenges faced by Latin American capital markets. A proper understanding of these challenges is essential for policymakers in the region, particularly after the onset of the Latin American Integrated Market. Second, this dissertation explores the use of ‘transitional strategies’ to overcome some of the challenges identified here. The ultimate goal of this project is to inform future reform efforts in Latin America and to offer some insights for policymakers in other emerging countries.

This thesis presents three empirical chapters investigating the informativeness of aggregate insider trading activities in the UK’s stock and option markets. Chapter one examines the relationship between aggregate insider trading and stock market volatility. The results suggest a positive relationship between aggregate insider trading and stock market volatility, confirming the hypothesis that aggregate insider trading increases the rate of flow of information into the stock market which in turn increases stock market volatility. Given that insiders also trade for non-informational reasons, we distinguish between informative and noisy insider trades and examine whether they affect stock market volatility differently. We find that only aggregate insider buy trades and medium sized insider trades affect stock market volatility positively. Chapter two re-examines whether aggregate insider trading can help predict future UK stock market returns. The results suggest that there is information in aggregate insider trading that can help predict future stock market returns. This is due to aggregate insiders’ ability to time the market based on their possession of superior information about unexpected economy-wide changes. We also find that a positive shock in aggregate insider trading causes an increase in future stock market returns two months after the shock. We test whether there is information in medium insider trades that can help predict future stock market returns. The results suggest that medium insider trades, specifically medium insider buy trades can help predict future stock market returns. Lastly, chapter three explores the relationship between aggregate exercise of executive stock options (ESO) and stock market volatility. Insiders in possession of private information may use their informational advantage to trade in the option markets via their exercise of ESOs which may affect stock market volatility. We find that aggregate exercise of ESOs affect stock market volatility positively. This is due to an increase in the rate of flow of information released via private information motivated exercises which cause prices to move as they adjust to the new information thereby increasing volatility. When executives have private information about future stock performance, they are motivated to exercise and sell stocks post exercise to avoid losses. They are also motivated to exercise and sell only a proportion of their stocks, specifically more than 50% of the acquired stocks and they exercise near the money ESOs. We find that for all these private information motivated reasons to exercise ESOs, stock market volatility is positively affected.

Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and dynamic nature. In any investment activity, having an accurate prediction system will significantly benefit investors by guiding decision making, especially in trading, asset management and risk management. Thus, the attempts to build such systems have attracted the attention of practitioners in the market and also researchers for many decades. Furthermore, the purpose of this thesis is to investigate and develop a new approach to predicting financial time series with consideration given to their dynamic nature. In this thesis, the prediction procedures will be carried out in three phases. The first phase proposes a new hybrid dynamic model based on Ensemble Empirical Mode Decomposition (EEMD), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Support Vector Regression (SVR) and EEMD-Genetic Algorithm (GA)-Weighted Average (WA) to predict stock index closing price. EEMD in this phase is introduced as a preprocessing step to historical observation for the first time in the literature. The experimental results show that the EEMDD-GA-WA model performance is a notch above the other methods utilised in this phase. The second phase proposes a new hybrid static model based on Wavelet Transform (WT), RNN, Support Vector Machine (SVM), Nave Bayes and WT-GA-WA to predict the exact change of the stock index closing price. In this phase, the experimental results showed that the proposed WT-GA-WA model outperformed the rest of the models utilised in this phase. Moreover, the input data that are fed into the hybrid model in this phase are technical indicators. The third phase in this research introduces a new Hybrid Heuristic-Rules-based System (HHRS) for stock price prediction. This phase intends to combine the output of the hybrid models in phase one and two in order to enhance the final prediction results. Thus,to the best of our knowledge, this study is the only one to have carried out and tested this approach with a real data set. The results show that the HHRS outperformed all suggested models over all the data sets. Thus, this indicates that combining di↵erent techniques with diverse types of information could enhance prediction accuracy.

The standard approach to pricing financial derivatives is to determine the discounted, risk-neutral expected payoff under a model. This model-based approach leaves us prone to model risk, as no model can fully capture the complex behaviour of asset prices in the real world. Alternatively, we could use the prices of some liquidly traded options to deduce no-arbitrage conditions on the contingent claim in question. Since the reference prices are taken from the market, we are not required to postulate a model and thus the conditions found have to hold under any model. In this thesis we are interested in the pricing of American put options using the latter approach. To this end, we will assume that European options on the same underlying and with the same maturity are liquidly traded in the market. We can then use the market information incorporated into these prices to derive a set of no-arbitrage conditions that are valid under any model. Furthermore, we will show that in a market trading only finitely many American and co-terminal European options it is always possible to decide whether the prices are consistent with a model or there has to exist arbitrage in the market.

This dissertation is divided into an introductory chapter and four essays. Chapter one discusses the importance of the study and describes the development and growth of the market as well. The first part (Chapters 2 & 3) examines stock returns behaviour and trading activity around earnings announcements. The second part (Chapters 4 & 5) examines price impact asymmetry and the price effects of block trades in the market microstructure context. Each essay addresses some aspects of market microstructure and stock returns behaviour in order to aid researchers, investors and regulators to understand a market which lacks research coverage. The research provides empirical evidence on issues such as the efficiency of the market, information asymmetry, liquidity and price impact of block trades. In first part of the thesis, event study and regression analysis were used to measure the price reaction around earnings announcements and to examine trading activity, information asymmetry and liquidity. In second part the determinants of the price impact of block trades were examined with regard to trade size, market condition and time of the day effects using transaction data. Liquidity and information asymmetry issues of block trades were also studied in this part.

This thesis investigates the predictability of trading strategies in the European and American stock market from 2001 to 2013. More specific, we examine the indices CAC40, DAX, FTSE100, NASDAQ and S&P500 first with the simple moving averages, then with trading rules based on the forecasts of time – series models and finally with the combination of the technical trading rules and time –series models. The significance of the examined trading rules tested with standard t – tests. The standard tests results show that technical trading rules are the most profitable strategy, second follows the combined and then the time – series rules as the least profitable trading strategy related to buy – and – hold strategy. / Σκοπός της παρούσας εργασίας είναι η διερεύνηση της προβλεπτικής δυνατότητας στρατηγικών επενδύσεων που εφαρμόζονται στην Ευρωπαϊκή και Αμερικάνικη χρηματιστηριακή αγορά, για τη χρονική περίοδο 2001-2013. Πιο συγκεκριμένα θα εξετασθούν οι δείκτες CAC 40, DAX, FTSE 100, NASDAQ και S&P 500, με κανόνες κινητών μέσων όρων, με κανόνες που βασίζονται σε μοντέλα πρόβλεψης χρονολογικών σειρών και με κανόνες συνδυαστικών των δύο ανωτέρω. Οι παραπάνω στρατηγικές θα συγκριθούν με την στρατηγική διακράτησης (Buy-and-Hold), που έχει ορισθεί ως benchmark στρατηγική και η σημαντικότητα των αποτελεσμάτων θα εξετασθεί με στατιστικούς ελέγχους t-statistics.