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In recent years, association networks and their applications have received increasing interest. The relationships in a network should ideally be ascertained without any preconceptions about the existence of a connection a priori. This would allow interpretations to be based on the underlying structure rather than on assumptions. Furthermore, a method that discounts outside influence on the relationships...
We propose a method for collective sentiment analysis for stock market prediction and analyse its ability to predict the change of a stock price for the next day. The proposed method is a two-stage process, based on the latest natural language processing and machine learning algorithms. Our evaluation shows best performance with the SVM approach in sentiment detection, with accuracy rates of 71.84/74...
This paper proposes an integrated nonlinearityexploration approach to discover the nonlinearitycharacteristic in clean energy stock market, integrating a set ofdata characteristics analysis technologies. In the proposedapproach, the stock data are first analysed in terms of Hurstexponent, a basic nonlinear exponent, from an overallperspective. Second, a series of nonlinear testing methods areemployed...
In stock networks analysis, the influential stocks is usually identified by using minimal spanning tree (MST) to filter the important information followed by the centrality measures analysis. In this paper, we introduce an analysis to identify the stocks that might have different behaviour compared to the others. Like the centrality measures analysis, this analysis is also conducted based on MST....
This paper presents various trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow...
The empirical results show that the dynamic conditional correlation (DCC) and the bivariate AGARCH (1, 2) model appropriates in evaluating the relationship of the Italy and the Germany's stock markets. The empirical result also indicates that the Italy and the Germany's stock markets is a positive relation. The average estimation value of correlation coefficient equals to 0.878, which implies that...
The bivariate normal mixture GARCH model is introduced in this paper, and applied to research the dynamic volatility features and the time-varying correlation structure of Shanghai Composite Index and Shenzhen Component Index in Chinese stock markets. Empirical results demonstrate that the bivariate normal mixture GARCH model outperforms other competing GARCH models, in terms of explaining the properties...
The empirical results show that the dynamic conditional correlation (DCC) and the bivariate AGARCH (1, 2) model is appropriate in evaluating the relationship of the Hong Kong and the Japan's stock markets. The empirical result also indicates that the Hong Kong and the Japan's stock markets is a positive relation. The average estimation value of correlation coefficient equals to 0.477, which implies...
This paper selected a sample of A-shares in Shanghai and Shenzhen Stock Exchanges whose ROE fall in the range of 0%-1% (namely listed companies with meager profit) during the three years of 2004-2006. Based on the multiple linear regression method, and studying the profit structure of listed companies with meager profit, this paper confirmed that listed companies with meager profit do have preference...
In this paper, a new evolutionary method named genetic relation algorithm (GRA) has been proposed and applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up a fixed number of the most efficient portfolio, the proposed...
This paper tests traditional capital structure models against the alternative of a pecking order model of corporate finance in Chinese stock market. We show that, the basic pecking order model, which predicts external debt financing driven by the internal financial deficit, has much greater explanatory power over the capital structure of Chinese listed companies than a static trade-off model. However,...
The survey of the relevant literature showed that there have been many studies for portfolio optimization problem and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite high. But almost none of these studies deals with genetic relation algorithm (GRA). This study presents an approach to large-scale portfolio optimization problem using GRA...
In this paper we investigate the linear and non-linear causality relationship between mainland China stock market and Hong Kong H-share market over January 1994-December 2007 period, which is divided into four periods marked by Asian financial crisis in 1997 and the opening of Chinapsilas B-shares to domestic investors in 2001, as well as by split-share structure reform in 2005. Nonlinear Granger...
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