<p>This Master’s Degree is a futurology that aims to analyse how the Chinese stock market might develop for a period of ten years, i.e. between the years 2005-2015. Since the future never with certainty can be predicted, scenarios will be presented displaying other possible outcomes. Naturally these scenarios are built upon given assumptions which otherwise could be as many as one’s imagination allows. The thought is to present the results as an index so the reader easily can see the possible development and scenarios.</p><p>The methodology used to collect necessary data is through the classical Delphi method, by which one interviews the selected “experts” that have the knowledge needed of the Chinese stock market. Moreover, the authors have collected further information through literature, the Internet, articles, reports and other written sources needed to continue further investigation. Further, the forecast was measured by two steps. The first step was to calculate the value at the start point. The second step was to create tow types of scenarios, added as a frame of the forecast outcomes. To transform the analysis and the scenarios in to a numerical index, a technical measurement of Quasi Monte Carlo Simulation was applied.</p><p>The theories applied when creating the index is foremost the Arbitrage Pricing Theory, which makes it possibly to measure several factors at the same time, including macro economical effects on the stock market.</p><p>According to the result, four factors were identified as the driving forces when finding a balanced economy, which affect the stock exchange: the investment structure; equal standard of living; the state of the financial sector and increased transparency. The result also indicates that the Chinese stock market will not stay in parity with the earlier development. A healthier and more efficient market will occur, due to structural reforms and the expected improvements within the financial sector including the stock exchange.</p><p>It is with great anticipation that the authors await a bright and successful future for the Chinese stock market. A new direction has been settled, although there are many difficult challenges.</p>

This Master’s Degree is a futurology that aims to analyse how the Chinese stock market might develop for a period of ten years, i.e. between the years 2005-2015. Since the future never with certainty can be predicted, scenarios will be presented displaying other possible outcomes. Naturally these scenarios are built upon given assumptions which otherwise could be as many as one’s imagination allows. The thought is to present the results as an index so the reader easily can see the possible development and scenarios. The methodology used to collect necessary data is through the classical Delphi method, by which one interviews the selected “experts” that have the knowledge needed of the Chinese stock market. Moreover, the authors have collected further information through literature, the Internet, articles, reports and other written sources needed to continue further investigation. Further, the forecast was measured by two steps. The first step was to calculate the value at the start point. The second step was to create tow types of scenarios, added as a frame of the forecast outcomes. To transform the analysis and the scenarios in to a numerical index, a technical measurement of Quasi Monte Carlo Simulation was applied. The theories applied when creating the index is foremost the Arbitrage Pricing Theory, which makes it possibly to measure several factors at the same time, including macro economical effects on the stock market. According to the result, four factors were identified as the driving forces when finding a balanced economy, which affect the stock exchange: the investment structure; equal standard of living; the state of the financial sector and increased transparency. The result also indicates that the Chinese stock market will not stay in parity with the earlier development. A healthier and more efficient market will occur, due to structural reforms and the expected improvements within the financial sector including the stock exchange. It is with great anticipation that the authors await a bright and successful future for the Chinese stock market. A new direction has been settled, although there are many difficult challenges.

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.

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.