This study has been conducted in order to determine the existence of predictability for the Stockholm stock exchange. With this purpose the random walk theory has been raised against the theory of mean reversion in order to determine which theory is the most substantial. Data has been collected from Nasdaq OMX Nordic and furtherly been processed using the statistical software EViews. Swedish listed companies’ daily share values between 2000-2014 have been analyzed using two tests; an Augmented Dickey Fuller test and a Variance Ratio test. The results show generally that the null hypothesis - and thus the random walk - is rejected in the short term. This means that both on an aggregated level and on an individual level, the Stockholm stock exchange is predictable in the short term - in the form of mean reversion - and that it is most evident in small cap firms.

This thesis investigates the long-run stability of Cointegrated pairs in the Swedish Equity Market. Stability is evaluated by estimating pairs in an in-sample period then rolling the win- dow forward. A Pairs Trading strategy is then applied to the estimated pairs and traded out-of-sample. The relationships are found to diminish over time and most break o. Negative compound annual growth rates are obtained for the period. However there are enough lasting cointegrating relationships for the strategy to be applicable but the returns are highly dependent on the complexity of the trading rules.

This paper assesses the aspect of market neutrality for a pairs trading strategy built on cointegration. This was conducted by evaluating the strategy?s performance during a negative market environment, 2007-06-01 to 2008-12-30, and a positive market environment, 2013-05-31 to 2014-12-30, for the stocks listed in the OMXS30 index. The results indicate market neutrality and that profitability of pairs trading is higher in prolonged periods of turbulence.

This paper provides methods to select pairs potentially profitable within the frame of statistical arbitrage. We employ a cointegration approach on pairwise combinations of five large energy companies listed on the New York Stock Exchange for the period 27th September 2012 to 22nd April 2014. We find one cointegrated pair, for which we further investigate both short and long run dynamics. A vector-error correction model is constructed, supporting a long run relationship between the two stocks, which is also supported by the mean-reverting characteristic of a stationary linear combination of the stocks. Impulse response functions and variance decomposition are also studied to further describe the interrelation of the stocks, supporting a unidirectional causality between the stocks.

This thesis includes three chapters on electricity and natural gas prices. In the first chapter, we give a brief introduction to the characteristics of power prices and propose a mean reversion jump diffusion model, in which jump intensity depends on temperature data and overall system load, to model electricity prices. Compared to the models used in the literature, we find the model proposed in this chapter is better to capture the tail behavior in the electricity prices. In the second chapter, we use the model proposed in the first chapter to simulate the spark spread option and value the power generations. In order to simulate power generation, we first propose and estimate mean reversion jump diffusion model for natural gas prices, in which jump intensity is defined as a function of temperature and storage. Combing the model with the electricity models in chapter 1, we find that the value of power generation is closer to the real value of the power plants as reflected in the recent market transaction than one obtains from many other models used in literature. The third chapter investigates extremal dependence among the energy market. We find a tail dependence that exceeds the Pearson correlation ρ, which means the traditional Pearson correlation is not appropriate to model tail behavior of oil, natural gas and electricity prices. However, asymptotic dependence is rejected in all pairs except Henry Hub gas return and Houston Ship Channel gas return. We also find that extreme value dependence in energy market is stronger in bull market than that in bear market due to the special characteristics in energy market, which conflicts the accepted wisdom in equity market that tail correlation is much higher in periods of volatile markets from previous literature.

<p dir="ltr">Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spreads constructed from pairs or portfolios of assets. Utilizing statistical models and algorithms, statistical arbitrage exploits and capitalizes on the pricing inefficiencies between securities or within asset portfolios. </p><p dir="ltr">In chapter 2, We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework.</p><p dir="ltr">In chapter 3, we explore an optimal timing strategy for the trading of price spreads exhibiting mean-reverting characteristics. A sequential optimal stopping framework is formulated to analyze the optimal timings for both entering and subsequently liquidating positions, all while considering the impact of transaction costs. Then we leverages a refined signature optimal stopping method to resolve this sequential optimal stopping problem, thereby unveiling the precise entry and exit timings that maximize gains. Our framework operates without any predefined assumptions regarding the dynamics of the underlying mean-reverting spreads, offering adaptability to diverse scenarios. Numerical results are provided to demonstrate its superior performance when comparing with conventional mean reversion trading rules.</p><p dir="ltr">In chapter 4, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading.</p>

This thesis studies different aspects of firm decisions by using a dynamic model. I estimate a dynamic model of the firm based on the trade-off theory of capital structure that endogenizes investment, leverage, and payout decisions. For the estimation of the model I use Efficient Method of Moments (EMM), which allows me to recover the structural parameters that best replicate the characteristics of the data. I start analyzing the question of whether target leverage varies over time. While this is a central issue in finance, there is no consensus in the literature on this point. I propose an explanation that reconciles some of the seemingly contradictory empirical evidence. The dynamic model generates a target leverage that changes over time and consistently reproduces the results of Lemmon, Roberts, and Zender (2008). These findings suggest that the time-varying target leverage assumption of the big bulk of the previous literature is not incompatible with the evidence presented by Lemmon, Roberts, and Zender (2008). Then I study how corporate income tax payments vary with the corporate income tax rate. The dynamic model produces a bell-shaped relationship between tax revenue and the tax rate that is consistent with the notion of the Laffer curve. The dynamic model generates the maximum tax revenue for a tax rate between 36% and 41%. Finally, I investigate the impact of financial constraints on investment decisions by firms. Model results show that investment-cash flow sensitivity is higher for less financially constrained firms. This result is consistent with Kaplan and Zingales (1997). The dynamic model also rationalizes why large and mature firms have a positive and significant investment-cash flow sensitivity.

本研究的目的，在於檢視購併案產生後，企業的報酬是否會受到平均數復歸（Mean reversion）的影響，而逐漸回復到產業的平均報酬。並且分析主併公司與被併公司佔雙方總資產比例的高低差異，所受到的不同影響程度。 本次研究的樣本採用1987～2006年中的美國電子電機產業共77件購併案，並依照主併公司佔雙方總資產的比例不同，分成為兩個群集。將兩群集做比較分析。實證結果顯示，兩個群集均有顯著的平均數復歸現象。而主併佔雙方總資產比例較高的群集，在購併第一年即有顯著的優於產業平均報酬，而主併與被併資產相近的群集，則是於第四年開始才有顯著的績效改善。根據實證結果，我們做出以下的結論： 1.企業的報酬具有平均數復歸的特性，表現優良的公司會被他人學習，而使整體產業平均報酬逐漸提升，漸漸蠶食原先與產業平均之間的差距；相反的，表現不佳的公司亦透過學習，逐漸改善自己的績效，也使的整體產業平均報酬上升，最後雙方都將趨向產業的平均水準。 2.當研究者要分析企業遭受偶發性事件（如購併）的影響時，應當考慮平均數復歸的所帶來的效果。如果忽略了平均數復歸所帶來的效果，將會對事件產生的影響給予錯誤的解讀。 3.面對購併績效的研究時，必須移除平均數復歸的負面效果，如此才得以正確估計購併後的績效表現。且此時主併公司資產佔雙方資產比例較高者，於購併第一年就有明顯的績效改善的效果，而雙方資產相近者，則在購併成立後第四年開始才有較佳的績效表現。 / The purpose of this research is to look over the return of enterprise can receive influence from mean reversion on post-merger performance. And it analyzes the degree of influence on acquirer companies with different proportion in total assets of both acquirer and target companies. The sample of this research adopts 77 cases of electronic and electrical equipment industry of U.S.A. in 1987- 200, take according to the proportion of total assets of acquirer company in both total assets of acquirer and target company, divide into two clusters. Make comparative analysis of these two clusters. According to the empirical results, we make the following conclusions: 1.The return of enterprises has characteristic of mean reversion. Both well performed companies and worse performed companies will move toward the whole industry's average return. 2.As the researcher wants to analyze the influence of enterprises suffered from the sporadic incident (such as M&A), they should consider the phenomenon of mean reversion. Take off the negative result of the mean reversion in order to estimate correctly for the exactly performance after M&A. 3.The acquirer companies which total assets have a higher proportion in both acquirer and target companies, will have superior to industry average return apparently in first year, and the acquirer companies which total assets close to the target companies, will have apparent improvement in performances until the fourth year.

[pt] O agronegócio possui grande importância para a economia brasileira, representando uma parcela significativa do PIB e das exportações totais do país. Assim como em outros processos produtivos inseridos em ambiente de incerteza, a atividade agropecuária necessita de instrumentos que minimizem o risco, principalmente, o risco de preço e auxiliem no processo de tomada de decisão dos agentes participantes do agronegócio. Neste contexto, os mercados futuros constituem-se como alternativas financeiras no gerenciamento de riscos através das operações de hedge. Porém, a eficiência destas operações depende da aplicação de metodologias adequadas que conduzam ao conhecimento mais preciso sobre os preços futuros. Deste modo, o objetivo principal deste trabalho é avaliar a aplicação dos modelos de difusão de saltos, tão bem sucedidos para a estrutura a termo da taxa de juros, para o caso de commodities; focando na realização de previsões. A análise empírica será realizada a partir da série histórica de preços da commodity agrícola café arábica negociada na BM&F. A metodologia empregada é fundamentada no artigo de Sanjiv Das (1998). Nesta tese será estimada uma classe de modelos estocásticos descritos pela literatura, tais como o processo de reversão à média, o movimento geométrico browniano, bem como suas variantes com jumps. Efeitos ARCH e GARCH também serão considerados na modelagem. O processo de estimação será desenvolvido tanto por métodos tradicionais quanto por Algoritmos Genéticos. Diante do problema exposto e da escassez de estudos modernos direcionados a abordagem das commodities agrícolas no país, o tema proposto justifica- se como motivação para pesquisa científica e tecnológica. / [en] The agribusiness has great importance for the Brazilian economy, representing a significative share of GDP and total exports of the country. Like other production processes inserted in an environment of uncertainty, the agricultural activity needs instruments that minimize the risk, especially, the risk of price and assist in decision-making process of agents participating in agribusiness. In this context, the future markets constitute themselves as financial alternatives in risk management through the hedge operations. However, the efficiency of these operations depends on the application of appropriate methodologies that lead to more precise knowledge about future prices. Thus, the main objective of this work is to evaluate the application of jumps diffusion models, so successful for the structure of interest rate, in the case of commodities; focusing on the achievement of forecast. The empirical analysis will be carried out from the historical range of arabic coffee agricultural commodity´s prices traded on BM&F. The methodology used is based on the paper from Sanjiv Das (1998). In this thesis it will be estimated a class of stochastic models described in the literature, such as the mean-reversion process, the geometric Brownian motion and its variants with jumps. ARCH and GARCH effects will also be considered in the modeling. The process of estimation will be developed by traditional methods as well as by Genetic Algorithms. Facing the problem and the shortage of modern studies directed to the approach of the agricultural commodities in the country, the topic proposed is a motivation for scientific and technological research.

<p>In this paper we study the pecking order and tradeoff theories of capital structure on a sample of 121 Swedish, non-financial, listed firms over the period between 2000 - 2009. We find that the Swedish firms’ financing behavior appears to have features consistent with the predictions of both theories. The evidence shows a preference for a financing behavior consistent with the tradeoff theory for the whole sample and for a sample of small firms, whereas large firms appear to follow a pecking order on their financing decisions. We show that under sufficient conditions both theories might be seen as “reconciled” and not mutually exclusive, and we find evidence for the large firms of our sample consistent with this notion.</p>