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Stock Market Forecast Based on the Complex Network

Author: HuaiBo
Tutor: FangWeiDong
School: South China University of Technology
Course: Probability Theory and Mathematical Statistics
Keywords: Complex network Shanghai Stock Index Betweenness Centrality Inverse Participation Ratio Transaction Volume
CLC: O211.62
Type: Master's thesis
Year: 2011
Downloads: 230
Quote: 0
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Abstract


Stock market prediction has always been a hot issue in stock market research, the traditional markov chain prediction model just will share price or volume forecast divided state respectively were linked to both the price and volume. Using the coarsening method to establish the stock price and volume of complex network model combined changes, and through the model to calculate the transition probability to utilize weighted markov chain model to forecast stock market trends. This paper mainly content three parts, specific as follows:The first part introduces the background needed to stock market prediction and theoretical knowledge, including the prediction of the stock market situation, the basic theory of complex networks, the stock market and the application of Markov chain in the stock market related knowledge.The second part focuses on indicators of the financial characteristics of complex networks of practical significance in the stock market, and the reference to investors. Also introduced the use of stock market volatility led the network connection matrix of the various steps of the transition probability of the methods and recognition using autocorrelation function yields the order of autocorrelation to complex network set up in the stock market using the weighted Markov chain model .The third part is an empirical analysis of China stock market, according to different period of China securities market economic performance situation, at the same time period were selected to division of different time intervals use coarsening of index closing price and volume method of converting the two-dimensional symbol opposite linkage weight, got more network sequence diagrams, through the appropriate adjacency matrix separately inspected each model point right distribution characteristics of, node frequency, node average period statistical characteristics such as, and the results are analyzed and compared, discovery into stock index futures market before and after the statistical characteristics introduced have bigger difference. With 8 nodes after the fluctuation network as an example introduces weighted markov chain prediction model, and the stock market is forecasted found better prediction effect.

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Theory of probability ( probability theory, probability theory ) > Random process > Markov process
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