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논문검색

A Novel Model of Stock Data Mining with M/G/1 Queue for Evaluation of Stock Crash

초록

영어

Data mining is the process of searching the information from a large amount of data. In order to evaluate the stock crash this paper proposes general decrementing service M/G/1 queue system with multiple adaptive vacations to find information related to stock crash in data about Shanghai Composite Index. We use the probability generating function (P.G.F.) of stationary queue length and LST of waiting time, and their stochastic decomposition to calculate Existing money flow. Existing Money flow calculation model is improved based on the stationary queue length and LST of waiting time. We program to achieve the stock of existing money flow algorithm, and get the number of existing money flow. The improved algorithm can early warn the stock market crash. The empirical result shows that: There will be a rise in price before the Stock Market Crash, and the stock of existing money inflow begin to decrease. The stock market crash fell for at least six months. The stock market crash fell by at least fifty-five percent. Most of the stock market crash fell by over seventy-percent. The stock market crash down time is inversely proportional to the magnitude of the decline. If the down time is short, the magnitude of the decline is large. If the down time is long, the magnitude of the decline is small. The stock market crash is great harm to investors.

목차

Abstract
 1. Introduction
 2. Existing Money Flow Model Description
 3. Existing Money Flow Algorithm with L and W Queue
 4. Data Mining and Analysis of Money Flow of Shanghai Composite Index
 5. Conclusion
 Acknowledgements
 References

저자정보

  • Qingzhen Xu School of Computer Science, South China Normal University, Guangzhou 510631, China, China Investment Securities, Shenzhen 518048, China
  • Feifei Zhang School of Computer Science, South China Normal University, Guangzhou 510631, China

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