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The Application of Data Mining in Yield Volatility Forecast of Shanghai and Shenzhen 300 Index

Author: LiJiaYu
Tutor: HuangYangMing
School: Xiamen University
Course: Economic Information Management
Keywords: Data Mining Yield Forecast BP-AR-GARCH model
CLC: F832.51
Type: Master's thesis
Year: 2008
Downloads: 313
Quote: 0
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China 's stock market after a decade of rapid development , has attracted more and more investors to put money into the stock market among which you want to get a huge return. Meanwhile in recent years , data mining technology has also made great progress , it has huge amounts of data to explore the potential information capacity , is widely carried out using , in particular, used in the securities field . CSI 300 Index from its inception in 2005 , has become a be able to demonstrate good Shanghai and Shenzhen stock index . Selection criterion is a large scale, good mobility stock , covering more than 60% in Shanghai and Shenzhen market capitalization , is about to launch stock index futures of the subject matter . The yield on the index can be predicted from two angles : the sources of yield , the impact of the stock yields can be from the external environment , regulatory authorities , fundamentals of listed companies , the behavior of investors , technical analysis, related investment goods price point for analysis. From fluctuations in the time series of feature point of view, the time series of stock returns in addition to non-linear , non-stationary time series with common characteristics , but also has a fat tail , high noise, volatility clustering and other features. Therefore, the yield on the stock time series forecasting more difficult and challenging , and has a very wide range of application value and broad market prospects . For the above two perspectives , this paper try to use data mining methods for analysis and forecasting . First, from the above six sources of volatility to find some representative indicators , the use of data mining Logistic, decision trees and neural network method for stock index future trend analysis and forecasting . And find the source of some of the features of volatility and discovered neural network in predicting the effect is good. Then BP neural network adaptive , self-learning , nonlinear optimization and characterization of GARCH model time series autocorrelation , volatility clustering , fat tail of properties combined. Established the BP-AR-GARCH model in the forecast and achieved good results.

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