This paper examines the efficiency of the Swedish stock market, by testing if it is possible to create an excess return by the use of technical trading rules. According to the efficient market hypothesis and the random walk theory, in an efficient market it is not possible to predict the future stock prices by analyzing historical stock prices. The profitability of tech-nical analysis and technical trading rules has been researched and debated extensively, but economists have yet to reach a consensus. Because of this we find it useful to continue to study technical trading rules, and in our case we will focus exclusively on the Swedish stock market. We have done this by applying the trading technique moving average on the Swe-dish stock exchange. We have used the OMX Stockholm 30 Index, OMXS30, the 30 most traded stocks on the Stockholm stock exchange. From Nasdaq OMX we have obtained the daily closing prices from 1986-09-30 - 2012-01-27. Our test shows support for technical trading rules. The best performing moving average is the (1,50,0), which substantially beats the buy-and-hold strategy while being statistically confident to 99%. We have also tested our data set for a unit root, if a unit root exists it implies that the data set is following a random walk. We cannot reject that there is a unit root with α = 0.10 in our data set, alt-hough it would be rejected with α = 0.11. Our result forces us to reject that the Swedish stock market is efficient which is consistent with previous research made one the Swedish stock market.

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Abstract During the late 1980¡¦s, the stock market and real estate market in Taiwan both went into enormous booms. The Taiwan Stock Market Weighted Price Index (TSMWPI) and real estate prices both reach the highest peaks that they never reach before. It seems that there is a certain relationship between the stock market and real estate market. Therefore, this thesis proposes to study the following two propositions: (1) if the stock market and real estate market are integrated, then a certain degree of asset substitution will occur. The price of the assets in the two markets will interacted due to the asset substitution. And this price interaction will reduce the effect of risk diversifying. (2) But if the two markets are segmented, the effect the diversifying risk will get significant increasing as long as having the assets of the two markets included in your portfolio simultaneously. Past studies commonly investigated the relationship between the price series in these two markets, and therefore make the conclusions of their relationships. However, any individual price series cannot represent the activities of the whole market. Consequently, we adopt the Arbitrage Pricing Model (APM) to examine the relationships between the stock market and the real estate market in Taiwan. Our study is the first one to discuss this topic from the view of the market. Our study also tests causality relationship between the price series, but we have some improvements compared to the past studies. Our model includes an exogenous variable which captures the influence affecting both the stock market and real estate market at the same time. The test of casualty is also based on the cointegration theory. We test four cities in Taiwan, including the Taipei City, Taipei County, Taichung City and Kaohsiung City. Our findings suggest that the house price of the Taipei City and the Taipei County are co-integrated with the TSMWPI, that is, there is a long-term equilibrium relationship between the two cities and TSMWPI. The test of Granger Causality indicates that TSMWPI only Granger causes the house price of the Taipei City. All other causality relationships are not existed in these four cities. Finally, we use the APM to examine the relationship between the two markets and find that no evidence of relationship is existed between the stock and the housing market, suggesting that the stock market and the housing market in Taiwan are segmented. Key Words: Real Estate Market, Stock Market, Causality, CAPM

Since its’ invention at Morgan Stanley in 1987 pairs trading has grown to be one of the most common and most researched strategies for market neutral returns. The strategy identifies stocks, or other financial securities, that historically has co-moved and forms a trading pair. If the price relation is broken a short position is entered in the overperforming stock and a long in the underperforming. The positions are closed when the spread returns to the long-term relation. A pairs trading portfolio is formed by combining a number of pairs. To detect adequate pairs different types of data analysis has been used. The most common way has been to study historical price data with different statistical models such as the distance method. Gatev et al (2006) used this method and provided the most extensive research on the subject and this study will follow the standards set by that article and add new interesting factors. This is done through an investigation on how the analysis can be improved by using the stocks fundamental data, e.g. P/E, P/B, leverage, industry classification. This data is used to set up restrictions and Lasso models (type of regression) to optimize the trading portfolio and achieve higher returns. All models have been back-tested using S&P 500 stocks between 2001-04-01 and 2015-04-01 with portfolios changed every six months. The most important finding of the study is that restricting stocks to have close P/E-ratios combined with traditional price series analysis increases returns. The most conservative measure gives annual returns of 3.99% to 4.98% depending on the trading rules for this portfolio. The returns are significantly (5%-level) higher than those obtained by the traditional distance method. Considerable variations in return levels is shown to be created when capital commitments are changed and trading rules, transaction costs and restrictions on unique portfolio stocks are implemented. Further research regarding how analysis of P/E-ratios can improve pairs trading is suggested. The thesis has been written independently without an external client and studied an area that the author found interesting.

The study was carried out to examine the effect of economic factors on stock price in a global economy - a case study of the Nigerian stock market. The main objectives of the study was to examine some peculiarities or differences in terms of economic variables that influence stock prices in the Nigerian stock market from those of the global economy. The study makes use of regression analysis and analysis of variance to analyze the secondary data obtained from the Nigerian Stock Market. There are numerous variables that can be identified to determine stock prices in any economy. / Mjeramgatan 2 lag 231 412 76 Göteborg

This thesis investigates issues relating to international portfolio diversification from the perspective of the Polish stock market in the context of the financial crisis. Beginning with an outline of the functioning of the Polish stock market, the first contribution of the thesis is to consider the risks, benefits and opportunities in this market. Within this context, trading strategies are considered with an emphasis on the impact on risk reduction or return enhancement of initial public offerings. Second, the thesis provides a model which may be relevant for measuring trend durations in equity prices. A third element of the thesis considers the influence of spill-over effects (from the financial crisis) on equity investments in Poland, incorporating country and industry specific factors. Finally, the thesis considers financial crisis contagion and policies that may be relevant for practitioners.

Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision making flows for stock market forecasting, with particular emphasis on stock selection and timing. To achieve this purpose, a number of stock selection and timing computational 'modules' are investigated. From a computational point of view, the investigation performed in this work encompass techniques such as artificial neural networks, genetic algorithms, chaos theory and fractal geometry, as well as more traditional methods such as clustering, screening, ranking, and statistics based models. From a financial data point of view, this research takes advantage of both fundamental and technical information to enhance the stock selection and timing processes and to cover several investment horizons. Three computational modules are proposed. First, a multivariate stock ranking module which uses fundamental information and is optimised through genetic algorithms. Second, a multivariate forecasting module which uses technical information and is based on artificial neural networks. Third, a univariate price time series forecasting module based on artificial neural networks. In addition, an integrated flow that takes advantage of some synergies and complementary properties of the devised modules is proposed. The effectiveness of the developed modules and the viability of the proposed integrated flow are evaluated over a number of investment horizons using (out-of-sample) historical data.

No description available.

This thesis will be concerned with investigating the empirical characteristics of stock returns, forUKfirms which are distinguished by market value. The primary aimof thisworkis to identify whether there are differences between the behaviour of large and small firm retums. A substantial amount of attention has recently focused upon how firm size influences the behaviour of stock returns in US markets, but, the role that firm size might have in determining the behaviour of stock returns in UK markets has received very little attention. The aim of this thesis is to redress this imbalance. The first part of this study will be concerned with showing that the returns of small firms are more predictable than the returns of large firms. The second part of this study will show that the relationship between risk and return depends on firm size. The third and final part of this thesis will show that not only are the mean returns of large and small firms different but that there are also important differences in the conditional variances of large and small firms. In all three parts of this thesis, important differences between the behaviour of large and small firm returns are documented for the first time.

Class of 2006 Abstract / Objectives: To examine the influence stock market crashes have on pharmaceutical security prices. More specifically, the objectives are to quantify the stock market abnormal returns and volatility of domestically listed drug companies during periods of stock market crashes. Methods: An event study methodology was performed to determine the impact of stock market crashes on security prices of pharmaceutical firms. Pharmaceutical security price data was obtained from the Center for Research in Securities Prices (CRSP) database. Stock market crashes were identified and economic considerations regarding the nature of security price returns were reviewed including normality, autocorrelation, heteroscedasticity and cross sectional dependence. Results: The estimation period for the study ranged over a period of thirty-five months (-45 to -5) prior to the stock market crash. Mean estimates of the SIMM mean beta parameter ranged from 0.51-1.18 for all companies analyzed within the period of 1929-2001. Mean monthly abnormal returns ranged from 0.0039-0.0348 during the estimation period. Over a period of one, three, and five months the majority of the pharmaceutical industry failed to consistently produce above average abnormal returns. Conclusions: The purpose of this study was to investigate the performance of the pharmaceutical industry during ten stock market crashes to verify and or quantify current literature statements about the recession proof nature of the drug sector. The current investigation found that during the estimation periods surrounding the stock market crashes, the pharmaceutical industry did not outperform the average market return.