The efficient market hypothesis and behavioural finance have been the cause of much debate for decades, with one theory advocating market efficiency and the other opposing it. The efficient market hypothesis (EMH) assumes that investors always act rationally and stock prices adjust rapidly to new information and should reflect all available information. In contrast, behavioural finance suggests that markets are not rational and investors make irrational decisions, which may lead them to over- or under-price stocks. Researchers for years have been empirically testing these assumptions in stock markets. However, there has been no consensus on which asset-pricing models perform better in capturing the effect of market anomalies and what impact these market anomalies have on the expected returns of different stock market’s sectors. The aim of the study was to test the effect of selected market anomalies on expected return in different sectors of the Johannesburg Stock Exchange (JSE). More specifically, the study aimed to compare the performance of different asset-pricing models and their ability to account for market anomalies in different sectors of the JSE. Additionally, this study tested the applicability of the recent Fama and French five (FF5-factor) model, in estimating the expected return on the JSE. The study used a quantitative approach with secondary data over a period of 12 years starting from January 2002 to December 2014. The sample used in the study consists of monthly data obtained from McGregor BFA and the South African Reserve Bank. The study examined for the effects of size, value, January and momentum variables across six sectors of the JSE. This was accomplished by the use of various asset-pricing models such as the Capital asset pricing model (CAPM), the Fama and French three-factor model (FF3-factor), the Carhart four-factor model (C4F) and the recent five-factor model of Fama and French (FF5-factor). The study showed that whenever the asset-pricing models were not restricted, they tend to capture the market anomalies in four out of the six sectors examined. However, no market anomalies were found present in two of the six sectors analysed. In contrast, when the asset-pricing models are restricted, the asset-pricing models only seem to capture the effects of market anomalies in one of the six examined sectors. The findings in this study suggest that market anomalies are sensitive to model specifications, as restricting the models tends to capture the different market anomalies across the sectors of the JSE. The study also found that market anomalies differ across sectors and that some sectors are more efficient than others. The study also reveals that the FF5-factor model is able to account for expected returns on the JSE. In addition, the FF5-factor model tends to perform better when the model is restricted. It is also evident from the findings presented in this study, that the value anomaly loses its predictive power when profitability and investment variables are included in the model. Overall, the study illustrated that market anomalies have an effect on returns of the JSE, that the model specifications play an important role in an asset-pricing model and that the FF5-factor model is applicable on the JSE, however, it is not certain whether four or five factors apply to the South African market.

The efficient market hypothesis and behavioural finance have been the cause of much debate for decades, with one theory advocating market efficiency and the other opposing it. The efficient market hypothesis (EMH) assumes that investors always act rationally and stock prices adjust rapidly to new information and should reflect all available information. In contrast, behavioural finance suggests that markets are not rational and investors make irrational decisions, which may lead them to over- or under-price stocks. Researchers for years have been empirically testing these assumptions in stock markets. However, there has been no consensus on which asset-pricing models perform better in capturing the effect of market anomalies and what impact these market anomalies have on the expected returns of different stock market’s sectors. The aim of the study was to test the effect of selected market anomalies on expected return in different sectors of the Johannesburg Stock Exchange (JSE). More specifically, the study aimed to compare the performance of different asset-pricing models and their ability to account for market anomalies in different sectors of the JSE. Additionally, this study tested the applicability of the recent Fama and French five (FF5-factor) model, in estimating the expected return on the JSE. The study used a quantitative approach with secondary data over a period of 12 years starting from January 2002 to December 2014. The sample used in the study consists of monthly data obtained from McGregor BFA and the South African Reserve Bank. The study examined for the effects of size, value, January and momentum variables across six sectors of the JSE. This was accomplished by the use of various asset-pricing models such as the Capital asset pricing model (CAPM), the Fama and French three-factor model (FF3-factor), the Carhart four-factor model (C4F) and the recent five-factor model of Fama and French (FF5-factor). The study showed that whenever the asset-pricing models were not restricted, they tend to capture the market anomalies in four out of the six sectors examined. However, no market anomalies were found present in two of the six sectors analysed. In contrast, when the asset-pricing models are restricted, the asset-pricing models only seem to capture the effects of market anomalies in one of the six examined sectors. The findings in this study suggest that market anomalies are sensitive to model specifications, as restricting the models tends to capture the different market anomalies across the sectors of the JSE. The study also found that market anomalies differ across sectors and that some sectors are more efficient than others. The study also reveals that the FF5-factor model is able to account for expected returns on the JSE. In addition, the FF5-factor model tends to perform better when the model is restricted. It is also evident from the findings presented in this study, that the value anomaly loses its predictive power when profitability and investment variables are included in the model. Overall, the study illustrated that market anomalies have an effect on returns of the JSE, that the model specifications play an important role in an asset-pricing model and that the FF5-factor model is applicable on the JSE, however, it is not certain whether four or five factors apply to the South African market.

Dado que o investimento no mercado acionário envolve incerteza, devíamos esperar que seu retorno médio fosse relativamente superior a uma aplicação livre de risco para compensar o investidor pelo risco adicional que ele incorre quando aplica seus recursos em ações. Entretanto, não encontramos tal evidência quando analisamos o comportamento do mercado acionário brasileiro. Isto porque, considerando os retornos realizados médio dos últimos vinte anos, o prêmio histórico foi relativamente baixo. Assim, naturalmente surge à questão se tal estimativa corresponde a um valor razoável para inferirmos o futuro comportamento do mercado acionário. Para responder a esta questão, nossa metodologia constituiu em três etapas. Na primeira, revisamos a literatura em busca de técnicas de estimação do prêmio e selecionamos as abordagens baseado em artigos recentes, citações e disponibilidade de dados. Além disso, também realizamos algumas propostas de estimação. Em seguida, apresentamos os resultados das metodologias selecionadas para os anos recentes e observamos que as estimativas apresentaram certo grau de heterogeneidade. Na segunda etapa, testamos o desempenho dos modelos empíricos estimados usando testes de previsão fora da amostra. Os resultados apontaram que alguns modelos foram superiores ao prêmio histórico. Desta forma, encontramos evidências de que o prêmio histórico representa apenas mais uma fonte de informação para inferir o prêmio esperado e, se tomado sozinho, não constitui um procedimento de inferência razoável. Visto que cada modelo apresenta uma estratégia empírica para inferir o prêmio, todos deveriam representar uma fonte informacional sobre o prêmio futuro. Consequentemente, uma corrente da literatura recente destaca que a estratégia ótima pode ser agregar informações dos modelos individuais. Com este intuito, o último passo da metodologia foi combinar informações dos modelos que apresentaram melhor desempenho em relação ao prêmio histórico e verificar se tal procedimento aumentou a performance do poder preditivo dos modelos. Como resultado, verificamos que tal abordagem melhora e estabiliza a previsão do prêmio. / Given that investment in the stock market involves uncertainty, we should expect that the average return was relatively higher than a risk-free investment in order to compensate investors for the additional risk they incur. However, we find no such evidence when we analyze the Brazilian stock market behavior. This is because, considering the realized average returns of the past twenty years, the historic equity risk premium was relatively low. So, naturally, the question of whether such an estimate corresponds to a reasonable value to infer the future behavior of the stock market arises. To answer this question, our methodology consists of three stages. At first, we review the literature on risk premium estimation techniques and select the different approaches based on recent articles, quotes and availability of data. We also made some estimation proposals. We then proceed and present the results of the methodologies selected for the recent years and find that the estimates presented some degree of heterogeneity. On the second step, we test the performance of our estimates using out-of-sample predictive tests. The results showed that some models performed better than the historical premium. Thus, we find evidence that the historical premium is just another source of information to infer the expected award and, if taken alone, does not constitute a reasonable inference procedure. Since each model presents an empirical strategy to infer the premium, every one of them should represent an information source on the future premium. Consequently, a recent literature points out that the current optimal strategy may be to aggregate information from individual models. To this end, the last step of the methodology was to combine information of the models that performed better against the historical premium and verify that this procedure increased the power of the predictive performance of the models. As a result, we find that this approach improves and stabilizes the premium forecast.

Dado que o investimento no mercado acionário envolve incerteza, devíamos esperar que seu retorno médio fosse relativamente superior a uma aplicação livre de risco para compensar o investidor pelo risco adicional que ele incorre quando aplica seus recursos em ações. Entretanto, não encontramos tal evidência quando analisamos o comportamento do mercado acionário brasileiro. Isto porque, considerando os retornos realizados médio dos últimos vinte anos, o prêmio histórico foi relativamente baixo. Assim, naturalmente surge à questão se tal estimativa corresponde a um valor razoável para inferirmos o futuro comportamento do mercado acionário. Para responder a esta questão, nossa metodologia constituiu em três etapas. Na primeira, revisamos a literatura em busca de técnicas de estimação do prêmio e selecionamos as abordagens baseado em artigos recentes, citações e disponibilidade de dados. Além disso, também realizamos algumas propostas de estimação. Em seguida, apresentamos os resultados das metodologias selecionadas para os anos recentes e observamos que as estimativas apresentaram certo grau de heterogeneidade. Na segunda etapa, testamos o desempenho dos modelos empíricos estimados usando testes de previsão fora da amostra. Os resultados apontaram que alguns modelos foram superiores ao prêmio histórico. Desta forma, encontramos evidências de que o prêmio histórico representa apenas mais uma fonte de informação para inferir o prêmio esperado e, se tomado sozinho, não constitui um procedimento de inferência razoável. Visto que cada modelo apresenta uma estratégia empírica para inferir o prêmio, todos deveriam representar uma fonte informacional sobre o prêmio futuro. Consequentemente, uma corrente da literatura recente destaca que a estratégia ótima pode ser agregar informações dos modelos individuais. Com este intuito, o último passo da metodologia foi combinar informações dos modelos que apresentaram melhor desempenho em relação ao prêmio histórico e verificar se tal procedimento aumentou a performance do poder preditivo dos modelos. Como resultado, verificamos que tal abordagem melhora e estabiliza a previsão do prêmio. / Given that investment in the stock market involves uncertainty, we should expect that the average return was relatively higher than a risk-free investment in order to compensate investors for the additional risk they incur. However, we find no such evidence when we analyze the Brazilian stock market behavior. This is because, considering the realized average returns of the past twenty years, the historic equity risk premium was relatively low. So, naturally, the question of whether such an estimate corresponds to a reasonable value to infer the future behavior of the stock market arises. To answer this question, our methodology consists of three stages. At first, we review the literature on risk premium estimation techniques and select the different approaches based on recent articles, quotes and availability of data. We also made some estimation proposals. We then proceed and present the results of the methodologies selected for the recent years and find that the estimates presented some degree of heterogeneity. On the second step, we test the performance of our estimates using out-of-sample predictive tests. The results showed that some models performed better than the historical premium. Thus, we find evidence that the historical premium is just another source of information to infer the expected award and, if taken alone, does not constitute a reasonable inference procedure. Since each model presents an empirical strategy to infer the premium, every one of them should represent an information source on the future premium. Consequently, a recent literature points out that the current optimal strategy may be to aggregate information from individual models. To this end, the last step of the methodology was to combine information of the models that performed better against the historical premium and verify that this procedure increased the power of the predictive performance of the models. As a result, we find that this approach improves and stabilizes the premium forecast.

One major criticism about the traditional mean-variance portfolio optimization is that it tends to magnify the estimation error. A little estimation error can cause the distortion of the whole portfolio. Two popular ways to solve this problem are to use a resampling method or the Black-Litterman method (Bayesian method). The clustering method is a newer way to solve the problem. Clustering means we group the highly correlated stocks first and treat the group as a single stock. After we group the stocks, we will have some clusters of stocks, then we run the traditional mean-variance portfolio optimization for these clusters. The clustering method can improve the stability of the portfolio and reduce the impact of estimation error. In this project, we will explain why it works and we will perform tests to determine if clustering methods do improve the stabilities and performance of the portfolio.

Determination of the stock expected return is an important element of asset management. This paper presents an Arbitrage Pricing Theory model, which strives to estimate the expected return explaining the historical volatility of the stock prices. This paper presents the model as it was introduced, necessary extension for application to a small market included. Statistical methods on which the model has been build are discussed -- factor analysis completed by principal component analysis. In the practical part, the model is applied to the Czech market with an assessment of the success of the application. The forces which were expected to represent risk factors for the market have been examined as well. It will be shown that the model may contribute to the understanding of risk behaviour of the stocks.

This study investigates, with a critical approach, if portfolios consisting of high beta stocks yields more than portfolios consisting of low beta stocks in the Swedish stock exchange market. The chosen period is 1999-2016, covering both the DotCom Bubble and the financial crisis of 2008. We also investigate if the Capital Asset Pricing Model is valid by doing a test similar to Fama and Macbeth’s of 1973. Based on earlier studies in the field and our own study we come to the conclusion that high beta stocks does not outperform low beta stocks in the Swedish stock market 1999-2016. We believe that this relationship arises from inefficiencies in the market and irrational investing. By doing this study we observe that, the use of beta as the only risk factor for explaining expected returns on stocks or portfolios is not correct.

The portfolio theory is microeconomic discipline which deals with the exploration of capital markets and assets that are traded on them. This diploma thesis is focused on optimal stock portfolio selection. The main aim is to find a final portfolio fulfilling the requirements. The first part provides the theory needed for the subsequent establishment of a practical case of the optimal portfolio. The second part is devoted to the actual calculations leading to finding the portfolio with the desired rate of return.

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The diploma thesis deals with the effectiveness of chosen fundamental analysis valuation methods on a sample of stocks. The sample consists of stocks traded on the Prague Stock Exchange and on the New York Stock Exchange. The Czech part of the sample consists of stocks of ČEZ, Erste Group Bank, Komerční banka, Philip Morris ČR and Telefónica O2. The American part of the sample includes stocks of Coca Cola, General Electric, Intel, Southern Company and Bank of America. These stocks are valued by dividend discount models and cash-flow models stepwise to the end of the years 2005 and 2006. As regards the dividend models, the Gordon model, the three-stage model and the H-model are applied whereas within the cash-flow models the DCF equity method was chosen. The effectiveness of the valuation process was subsequently tested over the course of three years by comparing the particular stock returns and the returns of the market portfolio represented by market index. The evaluation of effectiveness is then done in terms of the absolute, the relative and the portfolio effectiveness.