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.

Background: The day-of-the-week effect has been a widely studied field ever since the concept was introduced in the early 1970s. Historically, negative returns on Mondays have been the most common finding. In line with improved market efficiency, researchers have started to question the existence of this anomaly. Purpose: The purpose of this study is to examine the weak-form efficiency level within the Swedish stock market by using sophisticated statistical approaches. The authors aim to investigate if the day-of-the-week effect was demonstrated between 2000 and 2017. Method: To properly provide answers to this investigation, a quantitative study has been conducted on the OMXS30. The data has been analysed by using different kind of sophisticated statistical methods such as GARCH and TGARCH. Conclusion: The results show that the day-of-the-week effect was not demonstrated within the OMXS30 during this time period, providing evidence for improved market efficiency.

Financial market anomalies are constant subjects of debate because of their devotion form the foundational financial theories. Fama and French (2008) referred to the momentum effect as the premier anomaly. Thus, this study sought to apply the concept of momentum to examine three investment strategies. The first strategy was price momentum, an existing investment strategy but which was used as a comparison to the returns of the second and third strategies. The second strategy applied momentum to return on equity, operating cash flow and earnings before interest, tax, depreciation and amortisation, whilst the third strategy combined stocks with momentum in both stock price and respective fundamental variable.Using a non-probability sampling method, a total of 109 stock listed on the JSE over the period 1999-2010 were tested. Momentum in stock price and respective fundamentals was used to rank stocks into quintiles. The viability of each investment strategy was measured by comparing its average and risk adjusted returns to the market.The results revealed that fundamental momentum can beat market returns, with the highest amount of significant differences found using momentum in return on equity. The combination strategy also reported results of beating the market, with the higest amount of significant differences found using the 12 month fundamental momentum combined with 6 month price momentum. / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

Value and momentum strategies have been heavenly researched in financial academic literature. In this essay, different portfolios based on value and momentum strategies have been constructed to examine if it is possible to exploit market anomalies to outperform market returns. Both value and momentum is seen as two market anomalies according to earlier literature. The test were made on the Swedish market, and all data were collected from the Nasdaq OMX Stockholm Large Cap list. The findings includes a significant outperformance of market returns in nearly all portfolio tested, as well as lower standard deviations for some. However, an empirical asset pricing model, based on four factors from the Swedish market were constructed to seek explanation for the results. Overall the factor variables were rejected on their statistical significances, except for the market factor which were statistical significant for all portfolios except one.

Background: Buying past winners and selling past losers has historically generated both profits and losses. The momentum strategy has been researched with risk measures and portfolio creation as fundamental components. While no definitive framework exists, prior research has explored industry segmentation within portfolio construction but has yet to reach a clear conclusion. Purpose: The purpose is to determine if there is a significant momentum effect in industry-portfolios, and if some industries are more prone to momentum strategy than others. Method: The research followed a positivistic paradigm with deductive reasoning using a quantitative approach. Secondary data of industry returns for 30 industries from the American stock market is collected from Kenneth R French database. The portfolios are analyzed from a statistical perspective to draw conclusions of the market anomaly. Findings: Three hypotheses were formed to address the research question and purpose. The winner-portfolio yielded significant raw returns in 14 of 16 tests for various periods, while loser and winner-loser portfolios showed negative raw returns. Accounting for systematic risk generated significant profits for all the winner portfolios. Further, industry-specific momentum was examined, revealing no momentum in some industries and momentum in others.   Conclusion: We find evidence that the industry portfolio can generate significant excess return over the market for 3–12-month periods, that can't be explained by the assets systematic risks. The study concludes that while industry-specific momentum is a viable strategy for diversification and capturing winners, its effectiveness varies across industries and has shown diminishing excess returns over the past two decades.

In 1981, Banz discovered evidence suggesting that small-cap firms outperform large-cap firms when considering risk-adjusted returns. Banz (1981), called this the “size effect” and raised concerns regarding the ability of current asset pricing models to set accurate prices for assets. This resulted in new models being developed, such as the Fama and French three-factor model which takes the size of a company into consideration (Fama & French, 1992). However, since the discovering of the size effect, several researchers have started to question its existence. (Asgharian & Hansson, 2008) Moreover, short after Banz findings, a study by Keim (1983) introduced results that complements the size effect. Keims study suggests that the size effect is present due to the fact that small-cap firms outperform large- cap firms during the month of January. This seasonal anomaly is called the “January effect” and could possibly be the reason for the existence of the size effect. The purpose of this study is to investigate if there is a size effect and/or a January effect present on the Swedish stock market (OMX) when using Enterprise Value as the measure for size. Enterprise Value has been chosen in order to consider the full capital structure of companies, hence, not solely the equity value. In order to answer these research questions, a quantitative study has been conducted on companies being listen on the OMX during the time period 2000-2019. The findings of the research are that there is no size effect present on the OMX. Furthermore, the research has found that there is a January effect present on the OMX. This paper suggests that the January effect might have been the reason for the presence of the size effect in history, but as of now, the size effect has diminished but the January effect still remains.

The present thesis is focused on speculative behavior of investors in financial markets. More precisely, the thesis consists of five papers and takes a closer look at two speculation driven financial market anomalies, the overreaction hypothesis and the momentum effect, and considers them in two financial markets, cryptocurrency and commodity markets.

In financial markets, momentum effect can be defined as the tendency of prices to maintain their short term movements. On the other hand, reversal effect is usually understood to be the change in direction of long term price movements. This paper examines whether momentum and reversal effects were in evidence in the Brazilian stock market between January 1999 and June 2012. After calculating 1296 trading strategies, no evidence of reversal effect is found. With regard to momentum effect, some weak evidence is presented for the very short term. Exposure to risk factors can explain returns on strategies, including returns on momentum strategies. The results are borne out with different market proxy specifications and size subsamples. When compared to previous studies, the results raise the question of whether the reversal effect is vanishing from the Brazilian stock market and whether the traces of momentum are sufficient to confirm its existence. Furthermore, evidence of seasonality is found for June in momentum strategies and for November in both reversal and momentum strategies. Subsequent tests reveal that the effects of seasonality are limited to small stocks. / Nos mercados financeiros, o efeito momento pode ser definido como a tendência dos preços em manter seus movimentos de curto prazo. Por outro lado, o efeito contrário é geralmente entendido como a mudança na direção dos movimentos de longo prazo dos preços. O presente trabalho examina a existência dos efeitos momento e contrário no mercado acionário brasileiro no período compreendido entre janeiro de 1999 e junho de 2012. A partir do cálculo de 1296 estratégias de investimento, nenhuma evidência de efeito contrário é encontrada. Com relação ao efeito momento, observou-se apenas uma fraca evidência no curtíssimo prazo. A exposição aos fatores de risco é capaz de explicar os retornos das estratégias, inclusive os retornos das estratégias de momento. Os resultados são robustos ao se utilizar diferentes especificações de proxy de mercado e subamostras de valor de mercado. Quando comparados a trabalhos anteriores, os resultados colocam em questão se o efeito contrário está desaparecendo no mercado acionário brasileiro e se as fracas evidências do efeito momento são suficientes para confirmar sua existência. Ademais, são observadas evidências de sazonalidade no mês de junho nas estratégias de momento e, no mês de novembro, em ambas as estratégias. Testes posteriores revelam que esses efeitos de sazonalidade estão restritos à subamostra de baixo valor de mercado.

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