Neste trabalho os retornos de ativos financeiros são modelados com base em dados não-estruturados de notícias aperiódicas. O cerne do tratamento de tais dados está na Recuperação da Informação, que se importa em aplicar uma transformação suficientemente representativa das estruturas de contexto gramatical para o algébrico. Trabalhos anteriores mostram que não só o timing (GOODHART, HALL, et al., 1993), tipo (ÄIJÖ, 2008), ou contexto no momento de publicação da notícia (BEBER e BRANDT, 2010), mas também o conteúdo léxico (WÜTHRICH, PERMUNETILLEKE, et al., 1998; LAVRENKO, SCHMILL, et al., 2000; SCHUMAKER e CHEN, 2009) explicam movimentos de mercado. Este trabalho tem por objetivo estender a discussão já fundamentada sobre a semântica das publicações, com a proposta de aprofundar a avaliação da sensibilidade paramétrica do ajuste de modelos já encontrados na literatura. Questões sobre a metodologia de pré-processamento dos textos são exploradas por meio de diversos experimentos. Por fim, corrobora-se os resultados prévios sobre a possibilidade de aplicar-se uma classificação preditiva sobre movimentos do mercado acionário norte-americano, particularmente dentro da janela de 15 a 25 minutos após a publicação de um press release. Contudo, não são encontradas evidências com de que regressão tenha a mesma capacidade, nem de que exista antecipação pelo mercado da informação a ser anunciada. / In this paper the returns of financial assets are modeled based on data from unstructured aperiodic news. The core processing of such data in is information retrieval, which deals with applying a transformation sufficiently representative from structures of grammatical to algebraic contexts. Previous works show that not only the news timing (GOODHART, HALL, et al., 1993), its type (ÄIJÖ, 2008), or its context in the moment of announcement (BEBER e BRANDT, 2010), but also its lexical content (WÜTHRICH, PERMUNETILLEKE, et al., 1998; LAVRENKO, SCHMILL, et al., 2000; SCHUMAKER e CHEN, 2009) explain movements on the market. This work aims to extend the ongoing discussion over the semantics of publications, with the proposal of further evaluating the sensitivity of the model\'s parametric fitting found in the literature. Questions raised about the methodology of pre-processing of texts are explored through various experiments. Finally, previous results on the possibility of applying a predictive classification of the U.S. stock market movements are corroborated, particularly inside a time frame from 15 to 25 minutes after the publication of a press release. However, no evidence is found with ? <= 0.01 that regression has the same capacity, nor that there is anticipation of market information to be announced.

A crescente integração econômica e mobilidade de capital levam a uma maior exposição dos investidores a riscos externos. Com isso, ganha relevância a discussão sobre como se considerar, no cálculo do custo de capital, possíveis prêmios requeridos pelos riscos adicionais da realização de negócios em mercados emergentes. A existência de um adicional de risco é relativamente evidente, podendo ser constatada pela maior volatilidade que grande parte dos mercados acionários emergentes possuem em relação a mercados maduros, como o norte-americano. Entretanto, a existência de um prêmio requerido por esse risco adicional é menos óbvia e sua observação empírica, por dados passados, usualmente não gera resultados conclusivos. Nesse contexto, a presente pesquisa aborda o prêmio pelo risco país no mercado acionário brasileiro, apresentando as formas mais usualmente aplicadas para estimá-lo e discutindo sobre a possibilidade de eliminá-lo através da diversificação dos investimentos. A maior contribuição realizada está na aplicação do modelo de valoração de ativos AEG (Abnormal Earnings Growth) para se estimar esse prêmio. O AEG torna viável o cálculo do custo de capital implícito nas as expectativas de resultados futuros divulgadas por instituições financeiras em mídias especializadas. O prêmio pelo risco país foi, então, estimado através do diferencial entre o custo de capital das principais empresas brasileiras e o custo de capital de um grupo de empresas comparáveis norte-americanas, ambos calculados pelo AEG. Identificou-se um custo de capital maior em 2,09% (209 basis points) para as empresas brasileiras, que se mostrou estatisticamente significante. Esse resultado comprova empiricamente a existência de um prêmio específico do mercado brasileiro, indicando que ainda existem barreiras à diversificação internacional dos riscos domésticos. Adicionalmente, a estimativa ficou bastante próxima do prêmio pelo risco soberano brasileiro, o que valida a sua ampla utilização como proxy do prêmio do mercado acionário. / The increasing economic integration and capital mobility among countries lead investors to be more exposed to external risks. That grants relevance to the discussion on how to consider, in the cost of equitys estimation, premiums for additional risks of businesses performed in emergent markets. The existence of an additional risk in these markets is relatively clear, what is demonstrated by higher volatilities that the majority of emergent stock markets presents if compared to mature markets. Nevertheless, the existence of a risk premium is less obvious and its empirical observation, applying historical data, usually doesnt produce any conclusive result. Within this framework, the present research approaches the country risk premium in the Brazilian stock market, introducing the most usual means to estimate it and discussing about the potential elimination of the country risk through investment diversification. The greatest contribution of this research is the application of the Abnormal Earnings Growth Model (AEG) to estimate the country risk premium. AEG makes viable the inference of the cost of equity implied in future earnings expectations, published by financial institutions through specialized media. The country risk premium was, then, estimated through the difference between the implied cost of equity of the main Brazilian public companies and the implied cost of equity of a comparable north-American group of companies, both calculated using the AEG. Was perceived a cost of equity higher in 2,09% (209 basis points) for the Brazilian companies, what was shown statistically significant. This result proofs the existence of a country risk premium for the Brazilian market, indicating that there are still some barriers to the international diversification of domestic risks. Additionally, this result is very close to the default risk premium of the Brazilian government bonds, which validates its vast usage as a proxy of the country risk premium applied for the Brazilian stock market.

Volatility spillovers in stock markets have become an important phenomenon, especially in times of crises. Mechanisms of shock transmission from one mar- ket to another are important for the international portfolio diversification. Our thesis examines impulse responses and variance decomposition of main stock in- dices in emerging Central European markets (Czech Republic, Poland, Slovakia and Hungary) in the period of January 2007 to August 2009. Two models are used: A vector autoregression (VAR) model with constant variance of resid- uals and a time varying parameter vector autoregression (TVP-VAR) model with a stochastic volatility. Opposingly of other comparable studies, Bayesian methods are used in both models. Our results confirm the presence of volatility spillovers among all markets. Interestingly, we find significant opposite trans- mission of shocks from Czech Republic to Poland and Hungary, suggesting that investors see the Central European exchanges as separate markets. Bibliographic Record Janhuba, R. (2012): Volatility Spillovers in New Member States: A Bayesian Model. Master thesis, Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies. Supervisor: doc. Roman Horváth Ph.D. JEL Classification C11, C32, C58, G01, G11, G14 Keywords Volatility spillovers,...

Submitted by Elba Lopes (elba.lopes@fecap.br) on 2017-06-07T00:29:43Z No. of bitstreams: 2 FABR??CIO HENRIQUE DA SILVA .pdf: 895738 bytes, checksum: 40fb16e80d83d817a88e496f7894c6cc (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-06-07T00:29:43Z (GMT). No. of bitstreams: 2 FABR??CIO HENRIQUE DA SILVA .pdf: 895738 bytes, checksum: 40fb16e80d83d817a88e496f7894c6cc (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-10-05 / In a professionalizing business environment, i.e. one in which there is a clear separation between administrators and investors, there is a great chance to occur the agency conflict, where there is always the risk that administrators act in their own interests and not in favor of maximizing the value of the company. One of the ways that this conflict is revealed in the stock market is through the risk inherent in the company's activities, and this risk is called non-systematic risk. A measure of risk in the stock market is the volatility, and it is decreased through corporate governance mechanisms. The main purpose of this thesis is to check the impact of corporate governance mechanisms over the volatility of securities traded in the BM&F Bovespa over the period comprised between 2010 and 2015, and for such purpose data from 53 listed companies representing 81.15% of the Ibovespa index, and the model of pillars of corporate governance called Brazilian Corporate Governance Index (Brazilian CGI) were used. For the sake of good understanding of this thesis, volatility is the variation of returns over the period analyzed; panel data was used as statistical model, and the main internal mechanisms of corporate governance are based on IBGC's four pillars, namely: (1) Accountability; (2) Transparency; (3) Corporate Responsibility and (4) Accountability. The statistical results indicated that variables related to accounting indicators and macroeconomic variables have an association with volatility reduction, and that factors directly linked to the pillars were not statistically significant. / Em um ambiente de neg??cios profissionaliza, ou seja, em que h?? uma separa????o clara entre os administradores e os investidores, h?? uma possibilidade grande de ocorrer o conflito de ag??ncia, onde h?? sempre o risco de que os administradores ajam em favor dos seus pr??prios interesses e n??o em prol da maximiza????o do valor da empresa; Uma das formas que esse conflito se revela no mercado de a????es ?? atrav??s do risco inerente ??s atividades da companhia, a esse risco d??-se o nome de risco n??o sistem??tico. Dentro do mercado de a????es uma medida de risco ?? a Volatilidade e a mesma ?? reduzida pelos mecanismos de Governan??a Corporativa. Esta disserta????o tem por objetivo principal verificar o impacto dos mecanismos de Governan??a Corporativa na Volatilidade dos pap??is da BM&F Bovespa no per??odo de 2010 e 2015 para tal, foram utilizados dados de 53 companhias que representam 81,15% do ??ndice Ibovespa e o modelo dos pilares de Governan??a Corporativa chamado de Brazilian Corporate Governance Index (Brazilian CGI). Para entendimento dessa disserta????o a Volatilidade ?? a varia????o dos retornos ao longo do per??odo analisado e utilizou-se como modelo estat??stico de dados em painel e os principais mecanismos internos de Governan??a Corporativa se baseiam nos quatro pilares do IBGC a saber: (1) Presta????o de Contas; (2) Transparencia; (3) Responsabilidade Corporativa e (4) Presta????o de Contas. Os resultados estat??sticos indicaram que vari??veis relacionadas a indicadores cont??beis e macroecon??micas possuem associa????o com a redu????o da Volatilidade e que fatores diretamente ligados aos pilares n??o se mostraram estatisticamente significativos.

Este trabalho apresenta um sistema de trading que toma decisões de compra e de venda do índice Standard & Poors 500, na modalidade seguidor de tendência, mediante o emprego de redes neurais artificiais multicamadas com propagação para frente, no período de 5 anos, encerrado na última semana do primeiro semestre de 2012. Geralmente o critério usual de escolha de redes neurais nas estimativas de preços de ativos financeiros é o do menor erro quadrático médio entre as estimativas e os valores observados. Na seleção das redes neurais foi empregado o critério do menor erro quadrático médio na amostra de teste, entre as redes neurais que apresentaram taxas de acertos nas previsões das oscilações semanais do índice Standard & Poors 500 acima de 60% nessas amostras de teste. Esse critério possibilitou ao sistema de trading superar a taxa anual de retorno das redes neurais selecionadas pelo critério usual e, por larga margem, a estratégia de compre e segure no período. A escolha das variáveis de entrada das redes neurais recaiu entre as que capturaram o efeito da anomalia do momento dos preços do mercado de ações no curto prazo, fenômeno amplamente reconhecido na literatura financeira. / This work presents a trend follower system that makes decisions to buy and sell short the Standard & Poors 500 Index, by using multilayer feedforward neural networks. It was considered a period of 5 years, ending in the last week of the first half of 2012. Usually a neural networks choice criterion to forecast financial asset prices is based on the least mean square error between the estimated and observed prices in the test samples. In this work we also adopted another criterion based on the least mean square error for those neural networks that had a hit rate above 60% of the Standard & Poors 500 Index weekly change in the test sample. This criterion was shown to be the most appropriate one. The neural networks input variables were chosen among those technical indicators that better captured the anomaly of the short term momentum of prices. The annual rate of return of the trading system based on those criteria surpassed those selected by the usual criteria, and by a wide margin the buy-and-hold strategy. The neural networks inputs were chosen to capture the momentum anomaly of the prices on the short term that is fully recognized in the financial literature.

O presente trabalho tem como objetivo analisar a volatilidade dos mercados de renda fixa e renda variável no Brasil, no período de março de 1986 até fevereiro de 2006, por meio do CDI (Certificado de Depósito Interfinanceiro) e IRF-M (Índice de Renda Fixa de Mercado), como indicadores do mercado de renda fixa, e o IBOVESPA (Índice da BOVESPA), como indicador de renda variável. Por meio da comparação da volatilidade destes ativos é possível observar se há coincidência temporal entre os dois mercados, em relação aos picos de volatilidade devido, principalmente, a influência de variáveis macroeconômicas. Tal análise é importante para que os gestores de portfólios, que tomam decisões de como alocar os investimentos, conheçam o histórico e o corrente relacionamento entre as volatilidades dos dois mercados. As volatilidades do mercado de renda fixa e do mercado de renda variável foram calculadas por meio do desvio padrão anual dos retornos mensais e por meio de um modelo GARCH(1,1). Os resultados mostram que, no Brasil, durante o período analisado, os dois mercados apresentaram: períodos coincidentes de picos de volatilidade, grande mudança no padrão comportamental das volatilidades após a implantação do Plano Real e pouca estabilidade na relação entre as volatilidades. / This work aims to study the volatility of the fixed income market and the stock market in Brazil, from March 1986 to February 2006, through CDI (Interbank Interest Rate), IRF-M (Fixed Income Index), as a fixed income market indicators, and IBOVESPA (BOVESPA index), as a stock market indicator. Through the comparison of the volatility of these assets it is possible to observe if there is time frame coincidence between the two markets, in relation to the peaks of volatility due to, mainly the influence of macroeconomics variables. Such analysis is important so that portfolio managers, responsible for decisions such investments allocation, know the history and the actual relationship between the markets volatility. Such analysis is important so that portfolio managers, responsible for decisions such investments allocation, know the history and the actual relationship between the markets volatility. Those fixed income market and stock markets volatilities were calculated through the annual standard deviation of the monthly returns and from a GARCH(1,1) model. The results show that, in Brazil, during the studied period, both markets presents: coincident volatility peaks periods, high change in the behavioral pattern of volatility after the deployment of the Plano Real and little stability in the relationship between the volatility.

This thesis adopts a survival model to analyze China’s stock market. The data used are the capitalization-weighted stock market index (CSI 300) and the 300 stocks for creating the index. We define the recurrent events using the daily return of the selected stocks and the index. A shared frailty model which incorporates the random effects is then used for analyses since the survival times of individual stocks are correlated. Maximization of penalized likelihood is presented to estimate the parameters in the model. The covariates are selected using the Akaike information criterion (AIC) and the variance inflation factor (VIF) to avoid multicollinearity. The result of analyses show that the general capital, total amount of a stock traded in a day, turnover rate and price book ratio are significant in the shared frailty model for daily stock data.

This is a study about abnormal characteristics in the stock market and how to successfully use them in personal portfolios. Market anomalies are unexpected excess returns that occur in relation to certain variables. Five commonly known market anomalies (market cap, price-earnings ratio, price-book value, momentum, volatility) are tested to give evidence for their presence. Existing variables are then combined in different portfolios in order to observe whether they generate greater excess returns combined rather than individually. This study will also reveal whether long-term holding is possible and how the anomalies react in bullish and bearish markets.

The U.S. National Market System (NMS), the largest marketplace in the world for securities and exchange traded funds, suffers from geographic market fragmentation which leads to reduced market efficiency. Communication lines transmit price updates and other information between geographically isolated exchanges at varying speeds, bounded above by the speed of light. Market participants have access to federally mandated information provided by the Securities Information Processor (SIP) and privately offered information provided by the exchanges, often called direct feeds. These feeds are quantitatively and qualitatively distinct, with the direct feeds tending to provide more information at a faster rate than the SIP feed. Differences between the SIP and direct feeds can lead to information asymmetries between market participants, which in turn create arbitrage opportunities. Under the market conditions of the NMS in 2016, these arbitrage opportunities occur regularly and many can be captured by market participants with fast connectivity. Several methods exist which allow market participants to reduce their communication latency with trading centers, including the practice of co-location where market participants pay to have their trading infrastructure located in the same building as the matching engines of an exchange. Such regularly occurring and executable arbitrage opportunities run counter to the Efficient-Market Hypothesis (EMH) in all forms, where even the weak form of the EMH claims that market participants should not be able to systematically profit from market inefficiencies. This thesis investigates the market inefficiencies and related effects introduced by geographic market fragmentation in two baskets of stocks: the Dow Jones Industrial Average (Dow), and the 30 largest stocks by market capitalization in the Standard \& Poor's 500 index (S&P 30).

Doctor of Philosophy / The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is on the trade implementation problem faced by a trader who wants to transact a buy or sell order of a certain size. We use two approaches to build our models, top-down and bottom-up. The traditional, top-down approach is applied to develop an optimal order submission plan for an order which is too large to be traded immediately without a prohibitive price impact. We present an optimisation framework and some solutions for non-stationary and non-linear price impact and price impact risk. We find that our proposed transaction costs model produces fairly good forecasts of the variance of the execution shortfall. The second, bottom-up, or data mining, approach is employed for trade sign inference, where trade sign is defined as the side which initiates both a trade and the market order that triggered the trade. We are interested in an endogenous component of the order flow, as evidenced by the predictable relationship between trade sign and the variables used to infer it. We want to discover the rules which govern the trade sign, and establish a connection between them and two empirically observed regularities in market order submissions, competition for order execution and transaction cost minimisation. To achieve the above aims we first use exploratory analysis of trade and limit order book data. In particular, we conduct unsupervised clustering with the self-organising map technique. The visualisation of the transformed data reveals that buyer-initiated and seller-initiated trades form two distinct clusters. We then propose a local non-parametric trade sign inference model based on the k-nearest-neighbour classifier. The best k-nearest-neighbour classifier constructed by us requires only three predictor variables and achieves an average out-of-sample accuracy of 71.40% (SD=4.01%)1, across all of the tested stocks. The best set of predictor variables found for the non-parametric model is subsequently used to develop a piecewise linear trade sign model. That model proves superior to the k-nearest-neighbour classifier, and achieves an average out-of-sample classification accuracy of 74.38% (SD=4.25%). The result is statistically significant, after adjusting for multiple comparisons. The overall classification performance of the piecewise linear model indicates a strong dependence between trade sign and the three predictor variables, and provides evidence for the endogenous component in the order flow. Moreover, the rules for trade sign classification derived from the structure of the piecewise linear model reflect the two regularities observed in market order submissions, competition for order execution and transaction cost minimisation, and offer new insights into the relationship between them. The obtained results confirm the applicability and relevance of data mining for the analysis and modelling of stock market order submissions.