Diante das evidências registradas na literatura de que, de forma geral, os fundos ativos não têm sido bem-sucedidos na tarefa de bater seus benchmarks, os fundos passivos – que buscam reproduzir as características de risco e retorno de um índice de mercado definido – vem ganhando espaço como alternativa de investimento na carteira dos investidores. A estratégia de reproduzir um índice é chamada de index tracking. Nesse sentido, o objetivo deste trabalho consiste em introduzir a técnica LASSO como método endógeno de seleção e otimização de ativos para a execução de um index tracking no mercado brasileiro e compara-lo com a execução de um index tracking pela técnica de seleção por participação dos ativos no índice de referência (otimizado por cointegração). A utilização da técnica LASSO, tal como proposta, constitui uma novidade na aplicação para o mercado financeiro brasileiro. Os testes comparativos foram realizados com as ações do índice Ibovespa entre os anos de 2010 e 2016. Sabendo das limitações relativas ao período de análise, os resultados sugerem, entre outros pontos, que o método LASSO gera tracking errors mais voláteis do que o método ad hoc tradicional e, dessa forma, gera menor aderência da carteira de réplica ao benchmark ao longo do tempo. / Given the evidence in the literature that, in general, the active funds have not been successful in the task of hitting their benchmarks, the passive funds - which seek to reproduce the risk and return characteristics of a defined market index - come gaining space as an investment alternative in the investor portfolio. The strategy of reproducing an index is called index tracking. In this sense, the objective of this study is to introduce the LASSO technique as an endogenous method of selection and optimization of assets for the execution of an index tracking in the Brazilian market and to compare it with the performance of an index tracking by the technique of selection by participation in benchmark index (optimized by cointegration). The LASSO technique, as proposed, is innovative as application to the Brazilian financial market. The comparative tests were carried out with the stocks of the Ibovespa index between 2010 and 2016. Regarding the limitations related to the analysis period, the results suggest, among others, that the LASSO method generates more volatile tracking than the traditional ad hoc proceding, and thus, generates a portfolio that is less adhered to the benchmark over time.

Quantitative analysis is one branch of portfolio management. The advantages of quantitative analysis are fast and objective. It has developed significantly in recent years because of the improvements in computer technology. This thesis applies the structure of a multi-factor model (MFM) to undertake quantitative analysis. Singapore has one of the most prosperous financial markets in Southeast Asia. The Singapore Stock Exchange (SGX) and Financial Times and the London Stock Exchange (FTSE) are now in cooperation, which has added vitality to this market. It has great influence in global financial markets, and this is why we select its security market to be our target in MFM. The model refers the multi-factor processes of Jeng and Tsai (2011) . For backtesting, we adopt an enhanced strategy as testimony. We transmit information from the MFM to the enhanced strategy. Then we create the stock weightings to constitute the enhanced portfolio. This model includes 68 significant descriptors, 14 composite factors and 7 industry factors. The Singapore MFM shows 43% adjusted R-Square in the sample period. The enhanced portfolio we suggested has an information ratio of 76.80% with a tracking error of 4.02% and 1.53% for monthly turnover rate.

Diante das evidências registradas na literatura de que, de forma geral, os fundos ativos não têm sido bem-sucedidos na tarefa de bater seus benchmarks, os fundos passivos – que buscam reproduzir as características de risco e retorno de um índice de mercado definido – vem ganhando espaço como alternativa de investimento na carteira dos investidores. A estratégia de reproduzir um índice é chamada de index tracking. Nesse sentido, o objetivo deste trabalho consiste em introduzir a técnica LASSO como método endógeno de seleção e otimização de ativos para a execução de um index tracking no mercado brasileiro e compara-lo com a execução de um index tracking pela técnica de seleção por participação dos ativos no índice de referência (otimizado por cointegração). A utilização da técnica LASSO, tal como proposta, constitui uma novidade na aplicação para o mercado financeiro brasileiro. Os testes comparativos foram realizados com as ações do índice Ibovespa entre os anos de 2010 e 2016. Sabendo das limitações relativas ao período de análise, os resultados sugerem, entre outros pontos, que o método LASSO gera tracking errors mais voláteis do que o método ad hoc tradicional e, dessa forma, gera menor aderência da carteira de réplica ao benchmark ao longo do tempo. / Given the evidence in the literature that, in general, the active funds have not been successful in the task of hitting their benchmarks, the passive funds - which seek to reproduce the risk and return characteristics of a defined market index - come gaining space as an investment alternative in the investor portfolio. The strategy of reproducing an index is called index tracking. In this sense, the objective of this study is to introduce the LASSO technique as an endogenous method of selection and optimization of assets for the execution of an index tracking in the Brazilian market and to compare it with the performance of an index tracking by the technique of selection by participation in benchmark index (optimized by cointegration). The LASSO technique, as proposed, is innovative as application to the Brazilian financial market. The comparative tests were carried out with the stocks of the Ibovespa index between 2010 and 2016. Regarding the limitations related to the analysis period, the results suggest, among others, that the LASSO method generates more volatile tracking than the traditional ad hoc proceding, and thus, generates a portfolio that is less adhered to the benchmark over time.

Diante das evidências registradas na literatura de que, de forma geral, os fundos ativos não têm sido bem-sucedidos na tarefa de bater seus benchmarks, os fundos passivos – que buscam reproduzir as características de risco e retorno de um índice de mercado definido – vem ganhando espaço como alternativa de investimento na carteira dos investidores. A estratégia de reproduzir um índice é chamada de index tracking. Nesse sentido, o objetivo deste trabalho consiste em introduzir a técnica LASSO como método endógeno de seleção e otimização de ativos para a execução de um index tracking no mercado brasileiro e compara-lo com a execução de um index tracking pela técnica de seleção por participação dos ativos no índice de referência (otimizado por cointegração). A utilização da técnica LASSO, tal como proposta, constitui uma novidade na aplicação para o mercado financeiro brasileiro. Os testes comparativos foram realizados com as ações do índice Ibovespa entre os anos de 2010 e 2016. Sabendo das limitações relativas ao período de análise, os resultados sugerem, entre outros pontos, que o método LASSO gera tracking errors mais voláteis do que o método ad hoc tradicional e, dessa forma, gera menor aderência da carteira de réplica ao benchmark ao longo do tempo. / Given the evidence in the literature that, in general, the active funds have not been successful in the task of hitting their benchmarks, the passive funds - which seek to reproduce the risk and return characteristics of a defined market index - come gaining space as an investment alternative in the investor portfolio. The strategy of reproducing an index is called index tracking. In this sense, the objective of this study is to introduce the LASSO technique as an endogenous method of selection and optimization of assets for the execution of an index tracking in the Brazilian market and to compare it with the performance of an index tracking by the technique of selection by participation in benchmark index (optimized by cointegration). The LASSO technique, as proposed, is innovative as application to the Brazilian financial market. The comparative tests were carried out with the stocks of the Ibovespa index between 2010 and 2016. Regarding the limitations related to the analysis period, the results suggest, among others, that the LASSO method generates more volatile tracking than the traditional ad hoc proceding, and thus, generates a portfolio that is less adhered to the benchmark over time.

This paper investigates why patron states choose to adopt a passive strategy in relation to de facto states. Pål Kolstø from the University of Oslo has claimed that this strategy is based on the assumption that de facto states have nowhere else to turn. In this thesis, role theory is used to expand this claim. The paper argues that patron states can adopt a passive strategy in relation to their clients when they expect role coherence. This occurs when the perceptions of both actors of the patron’s role are in alignment. Role theory places emphasis on both leaders and followers in bilateral relations, which enables a deeper exploration of the perspectives of both actors. The selected case study focuses on the relationship between Russia and the de facto state Abkhazia, acknowledged by previous research as a client characterised by a high degree of defiance. Thematic analysis by Braun and Clarke was used to identify roles on which the study was based. The material encompasses 25 articles each from the Abkhaz newspaper Respublika Abkhaziya and Russian newspaper Izvestiya during the years 2019 and 2020.  The thesis concludes that role coherence was present in this relationship. Despite diverging attitudes and interests, the perception of Russia’s role was clearly reflected in Abkhaz media. The results indicate that role theory can be used to explore how patron states conduct foreign policy and enables a more comprehensive study of patron-client relationships.

<p>This study deals with one of the efficient market hypothesis’ anomaly. The research aims at proving the</p><p>existence of a size anomaly by answering the question: can you outperform the market by investing in</p><p>small and mid caps? It is in fact a questioning of the well-know efficient market hypothesis (EMH). We</p><p>investigate the size effect in the situation of a passive strategy with different indices (Russell Indices and</p><p>S&P Indices) from 1995 to 2005.</p><p>The introduction gives to the reader the background he needs to understand the methodology and the</p><p>approach of the issue by the authors. Key concepts are defined such as EMH, passive strategy.</p><p>The second part exposes the methodology the authors choose and the methodology of exploited indices.</p><p>The research consist on measuring the risk adjusting excess returns by comparing the market index</p><p>return (S&P 500 or Russell 3000) and the Small and Mid Caps indices (S&P Small Cap 600, S&P Mid</p><p>Cap 400, Russell Mid Cap and Russell 2000) over the period. Indeed the methodology of indices is</p><p>exposing in details to understand in which extent the study can be influence by the construction of</p><p>indices.</p><p>Then in part 3 the authors describe theories that are possible explanations for the size effect. Then it is</p><p>understandable that the size anomaly is the result of a set of factors that generate abnormal returns.</p><p>These theories help the authors to come up with a model that gives an overview of the research.</p><p>After having explained their research method and reveal their empirical findings. The authors</p><p>demonstrate that excess returns can be earned by investing in small and mid caps indices even after</p><p>controlling for risk. The risk adjusting excess returns their findings can potentially be explained by the</p><p>other factors depicted in the theoretical part. E/P ratios, Trading Costs, January effect, Overreaction are</p><p>possible reasons to explain the size anomaly. They also find an instability and/or reversal of the size</p><p>effect consistent with one of the theories. However the authors find data with non statistic significance,</p><p>so I accept the null hypothesis that the excess returns of small and mid caps indices are equal to zero.</p><p>The paper ends with a discussion about the limitations of the study and possible further researches. The</p><p>authors conclude that even if the existence of a size effect is obvious for some years and horizons of</p><p>investment, the passive strategy appears to be an unsuited method to take advantage of the small effect</p><p>since the results reject the null hypothesis. The authors clarify the fact that before investing in small and</p><p>mid caps, one has to be aware of all the factors that can influence his investment (beside risk) because</p><p>the size effect is a set of factors.</p><p>Key words: Efficient Market Hypothesis, Abnormal returns, Size effect (anomaly), Passive strategy,</p><p>Market Index, S&P indices, Russell indices</p>

This study deals with one of the efficient market hypothesis’ anomaly. The research aims at proving the existence of a size anomaly by answering the question: can you outperform the market by investing in small and mid caps? It is in fact a questioning of the well-know efficient market hypothesis (EMH). We investigate the size effect in the situation of a passive strategy with different indices (Russell Indices and S&amp;P Indices) from 1995 to 2005. The introduction gives to the reader the background he needs to understand the methodology and the approach of the issue by the authors. Key concepts are defined such as EMH, passive strategy. The second part exposes the methodology the authors choose and the methodology of exploited indices. The research consist on measuring the risk adjusting excess returns by comparing the market index return (S&amp;P 500 or Russell 3000) and the Small and Mid Caps indices (S&amp;P Small Cap 600, S&amp;P Mid Cap 400, Russell Mid Cap and Russell 2000) over the period. Indeed the methodology of indices is exposing in details to understand in which extent the study can be influence by the construction of indices. Then in part 3 the authors describe theories that are possible explanations for the size effect. Then it is understandable that the size anomaly is the result of a set of factors that generate abnormal returns. These theories help the authors to come up with a model that gives an overview of the research. After having explained their research method and reveal their empirical findings. The authors demonstrate that excess returns can be earned by investing in small and mid caps indices even after controlling for risk. The risk adjusting excess returns their findings can potentially be explained by the other factors depicted in the theoretical part. E/P ratios, Trading Costs, January effect, Overreaction are possible reasons to explain the size anomaly. They also find an instability and/or reversal of the size effect consistent with one of the theories. However the authors find data with non statistic significance, so I accept the null hypothesis that the excess returns of small and mid caps indices are equal to zero. The paper ends with a discussion about the limitations of the study and possible further researches. The authors conclude that even if the existence of a size effect is obvious for some years and horizons of investment, the passive strategy appears to be an unsuited method to take advantage of the small effect since the results reject the null hypothesis. The authors clarify the fact that before investing in small and mid caps, one has to be aware of all the factors that can influence his investment (beside risk) because the size effect is a set of factors. Key words: Efficient Market Hypothesis, Abnormal returns, Size effect (anomaly), Passive strategy, Market Index, S&amp;P indices, Russell indices