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Shanghai Composite Index forecasting methods and their empirical research

Author: KangBo
Tutor: TangXiaoWo
School: University of Electronic Science and Technology
Course: Quantitative Economics
Keywords: Stock price prediction Empirical research Time Series
CLC: F832.51
Type: Master's thesis
Year: 2005
Downloads: 461
Quote: 2
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Abstract


China's stock market is gradually standardize management and scale are constantly expanding , the concern for the market index has increasingly shown its importance , at the same time gradually increased institutional investors , as well as the introduction of index - related trading products , makes both from the broad-market index stock index , or today's investment trading function , or whether it is from a macro point of view , or from the microscopic point of view , the need for the stock price index detailed forecast analysis . Therefore , this article over Shanghai Composite Index has practical significance for the study of stock price index prediction research . On the other hand , fundamental analysis and numerical analysis of stock market prediction methods have their own advantages and disadvantages , both organic combination able to provide investors with a more comprehensive predictive analysis methodology . Therefore , the analysis of macroeconomic indicators of stock index function and micro - transactions for profit function two aspects , the Shanghai Composite Index for the study, a combination of the two aspects of the macro fundamental analysis and microscopic models predict stock price index forecast to macro the basic analysis concluded bedding microscopic model predicts the final solution , a comprehensive analysis of the forecast price movements of the Shanghai Composite Index . The empirical results show the feasibility of this study . Used in this article some of the new method to analyze the problem , such as fundamental analysis which the orthogonal polynomial distributed lag model for stock index and macroeconomic factors between short-term and long-term multiplier effects analysis ; using neural network analysis of macroeconomic factors nonlinear correlation between the strength of the stock index ; use of the mean indicators and stability indicators investigated the pros and cons of the forecasting methods ; nonparametric model based on orthogonal sequence estimation prediction; used in combination forecasting double hidden layer neurons network combination of various forecasting methods . The empirical results show that these attempts to obtain a certain effect .

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CLC: > Economic > Fiscal, monetary > Finance, banking > China's financial,banking > Financial market
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