The paper evaluates whether the Lusaka Stock Exchange (LuSE) is weak form efficient, and whether stock price movements conform to the random walk hypothesis of non-predictability in future price movements based on past price information. The methods employed are the parametric and non-parametric individual as well as multiple variance ratio tests. In addition, the study incorporates the Runs Test. The study further examines seasonality in Zambian stock returns of the day of the week effect as well as monthly related effects. The period of analysis is from 3rd January, 2006 to 17th February, 2014. The study incorporates daily data as well as monthly data of the LuSE All share Index in order to investigate the random walk hypothesis as well as seasonality effects of the Zambian market. The period of analysis is broken down into two sub periods after accounting for multiple structural breaks in the data. The results of the study are mixed, the results of the Runs test finds the Zambian stock market price series to be mutually independent and conform to a random sequence, and are as such unpredictable. While the variance ratio tests reject the random walk hypothesis for the Zambian market, and as such, support the view of the use of technical trading strategies in order to outperform buy-and-hold strategies. The study finds no evidence of any seasonality in the data, either for daily data as well as monthly data. As such there is evidence that investors may acquire returns greater than those of the market, however, transaction costs and commissions would have to be minimal in order to exploit any patterns in the stock price series of the Lusaka stock exchange.

When a company decides to sell their shares to the public for the first time it is called an initial public offering. For quite some time, the literature on the subject has come to the conclusion that the companies going public often undervalue their share price prior to the initial public offering resulting in an abnormal positive return on the first trading day, also known as initial public offering underpricing.  This thesis aims to study whether initial public offering underpricing occurred in the Swedish markets during the selected time period of 2000-2022. The thesis also seeked to find whether there was a significant difference in underpricing depending if the company was listed on OMX Stockholm or First north growth market. Further, with the help of previous research on the topic, a few independent variables were retrieved and later regressed against the initial return on the first trading day and thus seeing if these variables explains if a company will see an increase in the share price on the first trading day or not. The independent variables collected were age of the company at the time of the initial public offering, deal size, the market the company was listed on and lastly the year the company was listed on the stock exchange. The study concluded that there was a significant underpricing in Swedish initial public offerings during the studied time period with an average first day initial return of 12.56%. However, the thesis further concluded that neither of the independent variables studied had a significant effect on the initial return on the first trading day. Neither could the thesis conclude that there was a significant difference in underpricing between the two studied markets, although, the sample from OMX Stockholm saw an average underpricing of 11.09% whilst first north saw an average underpricing of 13.79%.

This thesis investigates the impact of artificial intelligence (AI) tools on the stock market, focusing on its effects on risk, performance, and concerns. Through an analysis of existing literature and an experiment, this study aims to provide insights into the potential benefits and drawbacks of using AI in stock market trading. The research explores how AI can contribute to increased efficiency, accuracy, and profitability in stock trading, as well as the potential risks and concerns associated with its use, such as biased models and transparency. By examining the implications of AI in stock trading, this thesis aims to provide a comprehensive assessment of its overall impact on the financial industry. The literature study maps out and categorizes existing risks, concerns and the performance of AI in the stock market mentioned in studies and articles on the subject,while the experiment focuses on a LSTM (Long Short Term Memory) model implementation and the evaluation of its performance and risks. The findings in the study shows that a deep learning model of LSTM, does outperform the NASDAQ 100 index on all occurrences that it was tested on in a simulated stock market using the Backtrader framework. Results from the experiment also point towards the fact that the risks of implementing the model are mitigatable to a great extent, if the implementer are aware of them. The literature study also discusses and complements potential concerns with the model implementation and how to mitigate the identified risks as well as the AI performance.

This study investigates organizational response as a mediating factor in the relationship between environmental change and deprofessionalization of stockbrokers between the years 1975 and 1990. Both quantitative and qualitative content analysis are used to analyze 412 business news articles concerning three brokerage firms. It is hypothesized that the environmental changes of jurisdictional competition resulting from deregulation of the industry, technological changes, and declining client trust resulting from scandals, will not have a direct effect on deprofessionalization of stockbrokers in these firms. It is expected, instead, that these environmental changes will be mediated by organizational responses, resulting in variation among the firms in the deprofessionalization of stockbrokers. The quantitative portion of the study shows different patterns of organizational response among the three firms examined regarding the environmental changes being investigated. The qualitative portion of the study indicates variation in deprofessionalization for stockbrokers within these firms resulting from differences in organizational response. The results of this study suggest a need for further investigation of the effects of organizational responses on the extent of deprofessionalization for professionals working in organizations. Organizational responses are likely to influence not only stockbrokers but other professionals in times of change and flux due to events both internal and external to the organization. / Ph. D.

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The Purchasing Managers’ Index (PMI) has been shown to provide useful indications on where the economy is heading (Afshar et al., 2007; Koenig, 2002; Harris, 1991). We examine the predictive abilities of PMI on stocks that are sensitive to business cycle movements by testing if the index can contribute to forecasting a group of Swedish industrial machinery stocks during 2012-2023. We approach this by employing pairwise Granger Causality tests, using PMI data from five countries, and stock performance data for 11 industrial machinery stocks included in the OMXS30. The results indicate that PMI cannot contribute to forecasting the stock performance. Instead, we find empirical evidence of a predictive relationship in the opposite direction, suggesting that the stock performance can forecast the PMI.

Many portfolios diversify by including mutual funds and exchange traded funds counting equities linked to various world country market indices, e.g. the S&P 500 of the United States and the Nikkei 225 of Japan. However a necessary condition for an international portfolio to be beneficial is that the correlation of returns between markets be low or at least non unity. My thesis will explore through extensive correlation analyses how advanced and emerging country indices are interrelated. The inspiration for my thesis came from the dissertation of Yale Ph.D. Graduate Charles H. Yang's research published in 1999, Essays on International Market Correlations. Yang tested correlation market data from 1972-1997 and found in 16 advanced country indices that the markets were becoming steadily and increasingly correlated over the 26 years studied. My thesis will expand on Yang's research with 32 total indices from advanced and emerging markets such as the Jakarta index of Indonesia, the Merval index of Argentina, and All-Ordinaries of Australia to assess if the world's financial markets are continuing integration, with the strongest correlations increasing between advanced markets. One of the objectives of my correlation research is to equip novice investors in advanced markets such as the U.S. with up-to-date information to enable them to be cautious but more open to investing in emerging economies, which have typically been classified as too risky. My thesis will shed light on how correlation analysis can be used to improve investment strategies and thereby permit investors to exploit the current wave of globalization to earn superior returns.

Doctor of Philosophy / Department of Economics / Lance J. Bachmeier / This dissertation investigates the long run and short run relationships between Saudi stock market returns and eight macroeconomic variables. We investigate the ability of these variables to predict the level and volatility of Saudi stock market returns. A wide range of Vector autoregression (VAR) and generalized autoregressive conditional heteroskedasticity (GARCH) models estimated and interpreted. A Johansen-Juselius cointegration test indicates a positive long run relationship between the Saudi stock price index and the M2 money supply, bank credit, and the price of oil, and a negative long run relationship with the M1 money supply, the short term interest rate, inflation, and the U.S. stock market. An estimated vector error correction model (VECM) suggests significant unidirectional short run causal relationships between Saudi stock market returns and the money supply and inflation. The VECM also finds a significant long run causal relationship among the macroeconomic variables in the system. The estimated speed of adjustment indicates that the Saudi stock market converges to the equilibrium within half a year. Granger causality tests show no causal relationship between Saudi stock market returns and the exchange rate. Impulse response function analysis shows no significant relationship between Saudi stock market returns and the macroeconomic variables. Forecast error variance decompositions suggest that 89% of the variation in Saudi stock market returns is attributable to its own shock, which implies that Saudi stock market returns are largely independent of the macroeconomic variables in the system. Finally, a GARCH-X model indicates a significant relationship between volatility of Saudi stock returns and short run movements of macroeconomic variables. Implications of this study include the following. (i) Prediction of stock market returns becomes more difficult as the volatility of the macroeconomic variables increases in the short run. (ii) Investors should look at the systematic risks revealed by these macroeconomic variables when structuring their portfolios and diversification strategies. (iii) Policymakers should seek to minimize macroeconomic fluctuations considering the effect of macroeconomic variables changes on the stock market when formulating economic policy.

Financial markets, particularly capital and stock markets, play an important role in mobilizing and canalising the idle savings of individuals and institutions to the investment options where they are really required for productive purposes. The prediction of stock prices and returns is carried out in order to enhance the quality of investment decisions in stock markets, but it is considered to be tricky and complicates tasks as these prices behave in a random fashion and vary with time. Owing to the potential of returns and inherent risk factors in stock market returns. Various stock market prediction models and decision support systems such as Capital asset pricing model, the arbitrage pricing theory of Ross, the inter-temporal capital asset pricing model of Merton ,Fama and French five-factor model, and zero beta model to provide investors with an optimal forecast of stock prices and returns. In this research thesis, a stock market prediction model consisting of two parts is presented and discussed. The first is the three factors of the Fama and French model (FF) at the micro level to forecast the return of the portfolios on the Saudi Arabian Stock Exchange (SASE) and the second is a Value Based Management (VBM) model of decision-making. The latter is based on the expectations of shareholders and portfolio investors about taking investment decisions, and on the behaviour of stock prices using an accurate modern nonlinear technique in forecasting, known as Artificial Neural Networks (ANN). This study examined monthly data relating to common stocks from the listed companies of the Saudi Arabian Stock Exchange from January 2007 to December 2011. The stock returns were predicted using the linear form of asset pricing models (capital asset pricing model as well as Fama and French three factor model). In addition, non-linear models were also estimated by using various artificial neural network techniques, and adaptive neural fuzzy inference systems. Six portfolios of stock predictors are combined using: average, weighted average, and genetic algorithm optimized weighted average. Moreover, value-based management models were applied to the investment decision-making process in combination with stock prediction model results for both the shareholders’ perspective and the share prices’ perspective. The results from this study indicate that the ANN technique can be used to predict stock portfolio returns; the investment decisions and the behaviour of stock prices, optimized by the genetic algorithm weighted average, provided better results in terms of error and prediction accuracy compared to the simple linear form of stock price prediction models. The Fama and French model of stock prediction is better suited to Saudi Arabian Stock Exchange investment activities in comparison to the conventional capital assets pricing model. Moreover, the multi-stage type1 model, which is a combination of Fama and French predicted stock returns and a value-based management model, gives more accurate results for the stock market decision-making process for investment or divestment decisions, as well as for observing variation in and the behaviour of stock prices on the Saudi stock market. Furthermore, the study also designed a graphic user interface in order to simplify the decision-making process based upon Fama and French and value-based management, which might help Saudi investors to make investment decisions quickly and with greater precision. Finally, the study also gives some practical implications for investors and regulators, along with proposing future research in this area.

Four research projects have been carried out with the objective of providing insights into some of the popular Asian investment myths and beliefs. The studies also throw some light on the efficiency of one Asian stock market. At the same time, the results reported in these research papers provide pragmatic investment guidelines for Asian emerging stock market investors. These research efforts add depth and breath (sic) to the existing emerging stock market investment literature, especially on Asian emerging stock markets. The Four Research Papers were : Research Paper I : Stock Selection Criteria During the Bull Run in the Malaysian Stock Market; Research Paper II : How Important Were Political Factors for Asian Stock Market Investors Throughout the Recent Financial Crisis?; Research Paper III : Active Equity Management versus Passive Equity Management - The Case of Malaysia from the Perspective of Foreign Investors; Research Paper IV : Stock Selection Criteria during the Bear Phase of the Malaysian Stock Market. Four popular myths/beliefs (myliefs) were selected for in-depth study with the conviction that the findings from these four studies could provide an insight into the emerging Malaysia stock market. The selection of the myliefs is mainly based on the popularity of the mylief as well as the applicability of the research results in the view of a foreigner investor / Doctor of Business Administration