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.

Submitted by David Fernandes de Carvalho Gomes (dvdgomes@gmail.com) on 2014-09-08T11:59:46Z No. of bitstreams: 1 [FGV-EESP]Dissertação_David Gomes_Banca_V3.pdf: 1221952 bytes, checksum: c70f947903c277271f3fdecf9c44b90f (MD5) / Approved for entry into archive by JOANA MARTORINI (joana.martorini@fgv.br) on 2014-09-08T13:37:41Z (GMT) No. of bitstreams: 1 [FGV-EESP]Dissertação_David Gomes_Banca_V3.pdf: 1221952 bytes, checksum: c70f947903c277271f3fdecf9c44b90f (MD5) / Made available in DSpace on 2014-09-08T13:41:29Z (GMT). No. of bitstreams: 1 [FGV-EESP]Dissertação_David Gomes_Banca_V3.pdf: 1221952 bytes, checksum: c70f947903c277271f3fdecf9c44b90f (MD5) Previous issue date: 2014-08-07 / One of the daily challenges present in different kinds of companies is to valuate with reasonable precision the dynamic of the price of its assets and liabilities. Frequently those prices have commodities as underlying, given its relevance as production inputs and in financial derivatives. In this context, this work proposes to evaluate a model of stochastic volatility using trading strategies. That put, we propose to compare different trading strategies to evaluate the financial profit and loss resulting from the adoption of the model adopted. There are several stochastic volatility models, each presenting upsides and downsides. For this work, the adopted model was the one proposed by Oztukel and Wilmott (1998), for it is consecrated and was adopted and tested by Bodra (2012) and Salvador (2013) for the Brazilian market. / Um dos desafios diários enfrentados nas diferentes empresas é avaliar com razoável precisão a dinâmica dos preços de seus ativos e passivos. Em muitos casos esses preços estão atrelados ao preço de commodities, devido a sua presença tanto como insumos produtivos quanto em derivativos financeiros. Nesse contexto o presente trabalho se propõe a avaliar um modelo de volatilidade estocástico através da aplicação de estratégias de trading. Desta forma busca-se comparar diferentes estratégias de negociação para avaliar o resultado financeiro obtido a partir da adoção do modelo proposto.

Le but de cette thèse est de répondre au vrai besoin de prédire les fluctuations futures des prix d'actions. En effet, l'aléatoire régissant ces fluctuations constitue pour des acteurs de la finance, tels que les Market Maker, une des plus grandes sources de risque. Tout au long de cette étude, nous mettons en évidence la possibilité de réduire l'incertitude sur les prix futurs par l'usage des modèles mathématiques appropriés. Cette étude est rendue possible grâce à une grande base de données financières et une puissante grille de calcul mises à notre disposition par l'équipe Automatic Market Making de BNP Paribas. Dans ce document, nous présentons uniquement les résultats de la recherche concernant le trading haute fréquence. Les résultats concernant la partie basse fréquence présentent un intérêt scientifique moindre pour le monde académique et rentrent par ailleurs dans le cadre des résultats confidentiels. Ces résultats seront donc volontairement omis.Dans le premier chapitre, nous présentons le contexte et les objectifs de cette étude. Nous présentons, également, les différentes méthodes utilisées, ainsi que les principaux résultats obtenus. Dans le chapitre 2, nous nous intéressons à l'apport de la supériorité technologique en trading haute fréquence. Dans ce but, nous simulons un trader ultra rapide, omniscient, et agressif, puis nous calculons son gain total sur 3 ans. Les gains obtenus sont très modestes et reflètent l'apport limité de la technologie en trading haute fréquence. Ce résultat souligne l'intérêt primordial de la recherche et de la modélisation dans ce domaine.Dans le chapitre 3, nous étudions la prédictibilité des prix à partir des indicateurs de carnet d'ordre. Nous présentons, à l'aide des espérances conditionnelles, des preuves empiriques de dépendances statistiques entre les prix et les différents indicateurs. L'importance de ces dépendances résulte de la simplicité de la méthode, éliminant tout risque de surapprentissage des données. Nous nous intéressons, ensuite, à la combinaison des différents indicateurs par une régression linéaire et nous analysons les différents problèmes numériques et statistiques liés à cette méthode. Enfin, nous concluons que les prix sont prédictibles pour un horizon de quelques minutes et nous mettons en question l'hypothèse de l'efficience du marché.Dans le chapitre 4, nous nous intéressons au mécanisme de formation du prix à partir des arrivés des évènements dans le carnet d'ordre. Nous classifions les ordres en douze types dont nous analysons les propriétés statistiques. Nous étudions par la suite les dépendances entre ces différents types d'ordres et nous proposons un modèle de carnet d'ordre en ligne avec les observations empiriques. Enfin, nous utilisons ce modèle pour prédire les prix et nous appuyons l'hypothèse de la non-efficience des marchés, suggérée au chapitre 3. / The aim of this thesis is to address the real need of predicting the prices of stocks. In fact, the randomness governing the evolution of prices is, for financial players like market makers, one of the largest sources of risk. In this context, we highlight the possibility of reducing the uncertainty of the future prices using appropriate mathematical models. This study was made possible by a large base of high frequency data and a powerful computational grid provided by the Automatic Market Making team at BNP Paribas. In this paper, we present only the results of high frequency tests. Tests are of less scientific interest in the academic world and are confidential. Therefore, these results will be deliberately omitted.In the first chapter, the background and the objectives of this study are presented along with the different methods used and the main results obtained.The focus of chapter 2 is on the contribution of technological superiority in high frequency trading. In order to do this, an omniscient trader is simulated and the total gain over three years is calculated. The obtained gain is very modest and reflects the limited contribution of technology in high frequency trading. This result underlines the primary role of research and modeling in this field.In Chapter 3, the predictability of prices using some order book indicators is studied. Using conditional expectations, the empirical evidence of the statistical dependencies between the prices and indicators is presented. The importance of these dependencies results from the simplicity of the method, eliminating any risk of over fitting the data. Then the combination of the various indicators is tested using a linear regression and the various numerical and statistical problems associated with this method are analyzed. Finally, it can be concluded that the prices are predictable for a period of a few minutes and the assumption of market efficiency is questioned.In Chapter 4, the mechanism of price formation from the arrival of events in the order book is investigated. The orders are classified in twelve types and their statistical properties are analyzed. The dependencies between these different types of orders are studied and a model of order book in line with the empirical observations is proposed. Finally, this model is used to predict prices and confirm the assumption of market inefficiency suggested in Chapter 3.