23 resultados para Chinese stock market

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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With the aim of analyzing the information search behavior of investors working in the stock market, this research sought to raise the aspects related to this behavior with focus on the cognitive and causal aspects which pervade the need for information of these investors. For that, the general pattern of informational behavior proposed by Wilson [10], and also the analysis of a report from an investor of the stock market area were used as basis for the analysis and reflection. The report of only one investor was used as basis for investigation, turning it impossible to extrapolate such result to a greater universe. The objective of this research was to investigate the need for information, the context and the intervenient variables which might interfere or not in the information search behavior of investors, in an attempt to get a deeper comprehension about the subject, as well as to propose the continuity of studies with basis on this study proposal.

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Power-law distributions have been observed in various economical and physical systems. Levy flights have infinite variance which discourage a physical approach. We introduce a class of stochastic processes, the gradually truncated Levy flight in which large steps of a Levy flight are gradually eliminated. It has finite variance and the system can be analyzed in a closed form. We applied the present method to explain the distribution of a particular economical index. The present method can be applied to describe time series in a variety of fields, i.e. turbulent flow, anomalous diffusion, polymers, etc. (C) 1999 Elsevier B.V. B.V. All rights reserved.

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Power-law distributions, i.e. Levy flights have been observed in various economical, biological, and physical systems in high-frequency regime. These distributions can be successfully explained via gradually truncated Levy flight (GTLF). In general, these systems converge to a Gaussian distribution in the low-frequency regime. In the present work, we develop a model for the physical basis for the cut-off length in GTLF and its variation with respect to the time interval between successive observations. We observe that GTLF automatically approach a Gaussian distribution in the low-frequency regime. We applied the present method to analyze time series in some physical and financial systems. The agreement between the experimental results and theoretical curves is excellent. The present method can be applied to analyze time series in a variety of fields, which in turn provide a basis for the development of further microscopic models for the system. © 2000 Elsevier Science B.V. All rights reserved.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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With the increase of stakeholders and consequently increase of amount of nancial transaction the study of news investment strategies in the stock market with data mining techniques has been the target of important researches. It allows that great historical data base to be processed and analysed looking for pattern that can be used to take a decision in investments. With the idea of getting pro t more than the real indexs' gain, we propose a strategy method of transactions using rules built by algorithm classi cation. For that, diary historical data of Ibovespa index and Petrobras stocks are organized and processed to nding the most important attribute that act decisively when taking a investment decision.To test the accuracy of proposed rules, a non real portfolio management is created, showing the decisions' performance over the real index and stocks' performance. Following the proposed rules, the results show that the strategy of investment give me back a high return that Stock market's return. The exclusive characteristics of algorithms maximize the gain inside the analysed time allowing to determine the techniques' return and the number of the days necessary to double the initial investment. The best classi er applied on the time series and its use on the propose investments strategy will demand 104 days to double the initial capital