The study is based on the study of Jegadeesh and Titman (1993, 2001) which found evidence of succesfull trading strategies which yielded significant positive abnormal returns by exploiting a momentum pattern in stock prices. The purpose of this study is to contribute with empirical results to the discussions of efficient markets, momentum effects and behavioral finance by providing evidence from the Swedish stock market between the years 1998 and 2013. The conclusion is that there exists a Momentum Effect on the Swedish stock market. The utilization of momentum strategies yields significant positive abnormal returns. The Efficient Market Hypothesis is a model which might hold in the long-term, but shows limitations in the short-term. The implications of the results of this study are that short-term investor behavior and momentum profits might be partially explained by behavioral finance models but the origin of the momentum profits need to be further evaluated.

The thesis offers a framework for trading algorithm optimization and tests statistical and economical significance of its performance on American, Swedish and Russian futures markets. The results provide strong support for proposed method, as using the presented ideas one can build an intraday trading algorithm that outperforms the market in long term.

This diploma thesis deals with the testing of the success of individual candlestick patterns of technical analysis. In the first part the theoretical basis of technical analysis and candlestick patterns will be presented. The second part will define basic candlestick patterns and their program definition. Backtesting on historical data will verify the success of individual candlestick patterns on EURUSD currency pair. In the third part, a trading system will be built based on the results of the testing of the candlestick patterns. An optimal setting of the trading system will be proposed. The aim of the thesis is to test success rate of candlestick patterns and find out if the candlestick pattern trading system is able to generate profits.

The Halloween effect refers to higher stock returns during the period November to April compared to May to October. This is a well-known calendar anomaly that has gained a lot of attention due to the fact that the effect is persistent in the market in spite of the fact that investors are aware of the anomaly today. This evokes questions regarding the efficiency in the markets and the Efficient Market Hypothesis in particular. The main focus of this thesis was to investigate whether the Halloween effect still exists in the Swedish stock market and if the power of the effect deviates between different firm sizes. Furthermore, we examined risk differences between the summer -and the winter months, as well as the January effect in order to find out if these could be possible explanations for the Halloween effect and its existence. A trading strategy based on the Halloween effect was also tested in order to see if investors could use this strategy to outperform a buy and hold strategy. The method that was used to investigate the existence of the Halloween effect was Ordinary Least Squares regression models with dummy variables, standard deviation to ascertain risk-differences between the periods and the Sharpe ratio to determine the risk-adjusted returns of the trading strategies. The results showed that the Halloween effect could be found in all of the examined market-cap indices, and therefore the Efficient Market Hypothesis could be questioned. The Halloween effect turned out to be autonomous from the January effect and the risk measured in standard deviation had no significant difference between the summer -and the winter months, hence, both these possible explanations were rejected. The backtesting showed that the Halloween strategy would perform better than the buy and hold strategy in all indices except from the mid-cap index. The results regarding the Sharpe ratio indicated that the Halloween strategy would be a better strategy to use considering risk-adjusted returns as the Sharpe ratio was higher in all indices.

This dissertation consists of two parts. In the first chapter, we examine the relative performance of four options-based investment strategies versus a buy-and-hold strategy in the underlying stock. Specifically, using ten stocks widely held in 401(k) plans, we examine monthly returns from strategies that include a long stock position as one component. These strategies are long stock, covered call, protective put, collar, and covered combination. Ignoring early exercise for simplicity, we find that the covered combination and covered call strategies generally outperform the long stock strategy, which in turn generally outperforms the collar and protective put strategies regardless of the performance measure considered. Clearly, from the first chapter, strategies that involve writing options, in general, outperform the ones buying options. The second chapter provides a detailed study of the conditions where option writers can maximize returns while minimizing risk. The nonlinear nature of time value decay in options suggests that, theoretically, holding short positions only when the speed of time decay is high might improve the performance of option writing strategies. We examine monthly returns from five option strategies without a position in the underlying asset. These strategies are: short straddle, short strangle, short guts, “crash-neutral” short straddle, and long iron butterfly. The results from two portfolios are compared: a “benchmark” portfolio using standard SPX options that expire the following month and a weekly portfolio using SPXW options that expire at the end of the weekly holding period. The short strangle strategy with weekly options consistently outperforms the other strategies with both standard and weekly options, even after accounting for transaction costs. This finding suggests that short-dated out-of-the-money options can be useful in improving the risk-return characteristics of an option writing strategy. In an effort to improve the performance of the short straddle strategy, this chapter introduces an extremely short holding period portfolio, by stitching together three weekly option expirations into one week. Although the straddle still underperforms relative to the short strangle, the performance of the short straddle is improved by entering the market 15 minutes before the close and by using the extremely short holding period portfolios.

Yes / We introduce a continuous time model for stock prices in a general factor representation with the noise driven by a geometric Brownian motion process. We derive the theoretical hitting probability distribution for the long-until-barrier strategies and the conditions for statistical arbitrage. We optimize our statistical arbitrage strategies with respect to the expected discounted returns and the Sharpe ratio. Bootstrapping results show that the theoretical hitting probability distribution is a realistic representation of the empirical hitting probabilities. We test the empirical performance of the long-until-barrier strategies using US equities and demonstrate that our trading rules can generate statistical arbitrage profits.

Cette thèse consiste en trois chapitres distincts. Dans le premier chapitre, je teste l'hypothèse selon laquelle le format d'affichage de l'information financière affecte les décisions des investisseurs individuels. Je montre qu'un affichage plus efficace permet aux individus de mieux gérer leurs ordres à cours limité en minimisant le risque de sélection adverse encouru en utilisant ces ordres. Cela suggère que les investisseurs individuels ont une rationalité limitée. Dans le second chapitre, je teste si les stratégies de trading apporteuses de liquidité peuvent générer des profits, après coûts de transactions, pour les traders actifs qui les implémentent. Je montre que seuls les individus situés dans le plus haut décile de performance peuvent battre le marché de façon persistante en utilisant des stratégies hautement contrariantes qui nécessitent l'utilisation massive d'ordres à cours limité. Les limites-à-l'arbitrage semblent expliquer ce phénomène. Dans le troisième chapitre, j'étudie les stratégies des individus autour des annonces de résultats. Je montre que les allers-retours qui sont implémentés un jour avant une annonce génèrent en moyenne des profits plus élevés et sont plus courts en durée que ceux implémentés en temps normal. Les individus clôturent leurs positions gagnantes le jour de l'annonce, ce qui peut ralentir l'ajustement des prix suite à l'annonce. / This thesis is made of three distinct chapters. In the first chapter, I test whether the display format of financial information matters for the individual investor. I find that a more efficient information display allows investors to increase returns on their limit orders, because it becomes easier for them to mitigate the risk of adverse selection when trading with those orders. My findings suggest that retail investors have bounded rationality. In the second chapter I test whether liquidity provision to the market can be a profitable strategy, after fees, for active retail investors. I find that only individuals ranked in the top decile of performance can persistently beat the market using highly contrarian limit order strategies. Limits-to-arbitrage seem to explain why these top retail investors exploit trading opportunities before other more sophisticated arbitrageurs. In the third chapter, I study the retail trading strategies around stock earnings announcements. I find that round-trips started one day before an announcement are more profitable and much shorter in duration than those started during the non-announcement period. Retails reverse their winning trades on the event date, which can slow down the adjustment of prices to new information.

A comercialização de energia elétrica de fontes renováveis, ordinariamente, constitui-se uma atividade em que as operações são estruturadas sob condições de incerteza, por exemplo, em relação ao preço \"spot\" no mercado de curto prazo e a geração de energia dos empreendimentos. Deriva desse fato a busca dos agentes pela formulação de estratégias e utilização de ferramentais para auxiliá-los em suas tomadas de decisão, visando não somente o retorno financeiro, mas também à mitigação dos riscos envolvidos. Análises de investimentos em fontes renováveis compartilham de desafios similares. Na literatura, o estudo da tomada de decisão considerada ótima sob condições de incerteza se dá por meio da aplicação de técnicas de programação estocástica, que viabiliza a modelagem de problemas com variáveis randômicas e a obtenção de soluções racionais, de interesse para o investidor. Esses modelos permitem a incorporação de métricas de risco, como por exemplo, o Conditional Value-at-Risk, a fim de se obter soluções ótimas que ponderem a expectativa de resultado financeiro e o risco associado da operação, onde a aversão ao risco do agente torna-se um condicionante fundamental. O objetivo principal da Tese - sob a ótica dos agentes geradores, consumidores e comercializadores - é: (i) desenvolver e implementar modelos de otimização em programação linear estocástica com métrica CVaR associada, customizados para cada um desses agentes; e (ii) aplicá-los na análise estratégica de operações como forma de apresentar alternativas factíveis à gestão das atividades desses agentes e contribuir com a proposição de um instrumento conceitualmente robusto e amigável ao usuário, para utilização por parte das empresas. Nesse contexto, como antes frisado, dá-se ênfase na análise do risco financeiro dessas operações por meio da aplicação do CVaR e com base na aversão ao risco do agente. Considera-se as fontes renováveis hídrica e eólica como opções de ativos de geração, de forma a estudar o efeito de complementaridade entre fontes distintas e entre sites distintos da mesma fonte, avaliando-se os rebatimentos nas operações. / The renewable energy trading, ordinarily, is an activity in which mostly operations are structured under uncertainty conditions, for instance, in relation to the energy spot price and assets generation. Derives from this fact the search of the agents for strategies formulation based on computational tools to assist their decision-making process, not only seeking financial returns, but also to mitigate the risks involved. Investments analysis in renewable sources share the same challenges. In the literature, the study of optimal decision-making under uncertainty conditions is made through the application of stochastic programming techniques, which enable modeling problems with random variables and find rational solutions. These models allow the incorporation of risk metrics, as the \"Conditional Value-at-Risk (CVaR)\", to provide optimal solutions that weigh the expected financial results and the associated risk, in which the agent\'s risk-aversion becomes an essential condition for defining the operation strategy. From the perspective of generators, consumers and traders agents, the main purposes of this thesis are: (i) to develop customized optimization models with CVaR metric associated, optimized in stochastic linear programming; and (ii) to apply the models for strategic analysis of operations under the risk-return binomial, focusing on the management activities of each of these agents, and considering renewable sources as option. In this context, the emphasis is on analysis of the operations financial risks through the application of CVaR and based on the agent\'s risk-aversion level. Furthermore, the hydro and wind renewables sources are options of generation assets in order to study the seasonal generation complementarity effect among them and the consequences on energy trading strategies.

A comercialização de energia elétrica de fontes renováveis, ordinariamente, constitui-se uma atividade em que as operações são estruturadas sob condições de incerteza, por exemplo, em relação ao preço \"spot\" no mercado de curto prazo e a geração de energia dos empreendimentos. Deriva desse fato a busca dos agentes pela formulação de estratégias e utilização de ferramentais para auxiliá-los em suas tomadas de decisão, visando não somente o retorno financeiro, mas também à mitigação dos riscos envolvidos. Análises de investimentos em fontes renováveis compartilham de desafios similares. Na literatura, o estudo da tomada de decisão considerada ótima sob condições de incerteza se dá por meio da aplicação de técnicas de programação estocástica, que viabiliza a modelagem de problemas com variáveis randômicas e a obtenção de soluções racionais, de interesse para o investidor. Esses modelos permitem a incorporação de métricas de risco, como por exemplo, o Conditional Value-at-Risk, a fim de se obter soluções ótimas que ponderem a expectativa de resultado financeiro e o risco associado da operação, onde a aversão ao risco do agente torna-se um condicionante fundamental. O objetivo principal da Tese - sob a ótica dos agentes geradores, consumidores e comercializadores - é: (i) desenvolver e implementar modelos de otimização em programação linear estocástica com métrica CVaR associada, customizados para cada um desses agentes; e (ii) aplicá-los na análise estratégica de operações como forma de apresentar alternativas factíveis à gestão das atividades desses agentes e contribuir com a proposição de um instrumento conceitualmente robusto e amigável ao usuário, para utilização por parte das empresas. Nesse contexto, como antes frisado, dá-se ênfase na análise do risco financeiro dessas operações por meio da aplicação do CVaR e com base na aversão ao risco do agente. Considera-se as fontes renováveis hídrica e eólica como opções de ativos de geração, de forma a estudar o efeito de complementaridade entre fontes distintas e entre sites distintos da mesma fonte, avaliando-se os rebatimentos nas operações. / The renewable energy trading, ordinarily, is an activity in which mostly operations are structured under uncertainty conditions, for instance, in relation to the energy spot price and assets generation. Derives from this fact the search of the agents for strategies formulation based on computational tools to assist their decision-making process, not only seeking financial returns, but also to mitigate the risks involved. Investments analysis in renewable sources share the same challenges. In the literature, the study of optimal decision-making under uncertainty conditions is made through the application of stochastic programming techniques, which enable modeling problems with random variables and find rational solutions. These models allow the incorporation of risk metrics, as the \"Conditional Value-at-Risk (CVaR)\", to provide optimal solutions that weigh the expected financial results and the associated risk, in which the agent\'s risk-aversion becomes an essential condition for defining the operation strategy. From the perspective of generators, consumers and traders agents, the main purposes of this thesis are: (i) to develop customized optimization models with CVaR metric associated, optimized in stochastic linear programming; and (ii) to apply the models for strategic analysis of operations under the risk-return binomial, focusing on the management activities of each of these agents, and considering renewable sources as option. In this context, the emphasis is on analysis of the operations financial risks through the application of CVaR and based on the agent\'s risk-aversion level. Furthermore, the hydro and wind renewables sources are options of generation assets in order to study the seasonal generation complementarity effect among them and the consequences on energy trading strategies.

[Truncated abstract] This thesis examines and compares the performance of three classes of stock trading strategies in the Australian stock market from 1980 to 2002. ... The first segment of this thesis examines some simple technical trading rules with a twostep methodology ... Our standard test results show that technical trading rules generate excess returns higher than that of the buy-and-hold portfolio equivalent prior to 1991, but generate lower returns in the period post-1991. Bootstrap test results also show that addressing nonnormality, time-dependence and conditional heteroskedasticity in the data reverses the standard test outcome of predictability ... In addition, our sub-sample results also show technical trading rules becoming less profitable over time ... The second segment of this thesis examines trading rules based on the forecasts of four time-series models: the AR(1), AR(1)-GARCH(1,1), AR(1)-GARCH(1,1)-M and AR(1)- EGARCH(1,1) models. These time-series trading rules were examined with standard t-tests and found to be significantly less profitable compared to technical trading rules. Subsample results also show the time-series trading rules losing profitability over time, which supports the conjecture that the Australian stock market became increasingly efficient over time. The third segment of this thesis examines trading strategies based on various combinations of technical trading rules and time-series models ... Due to the weak performance of the time-series trading rules, our results show that combining technical rules with time-series models do not lead to improved forecast accuracy. Sub-sample results again show a strong decline in profitability post-1991, suggesting that technological advancements in the ASX since 1991 enhance market efficiency such that the above simple stock trading strategies are no longer profitable.