Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the econom...
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Online Access: | http://dx.doi.org/10.1155/2014/124523 |
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doaj-087c76a4daab4922ac3b953286155cf82020-11-24T21:32:32ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/124523124523Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov ChainYonghui Dai0Dongmei Han1Weihui Dai2School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, ChinaSchool of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, ChinaSchool of Management, Fudan University, 220 Handan Road, Shanghai 200433, ChinaThe stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.http://dx.doi.org/10.1155/2014/124523 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yonghui Dai Dongmei Han Weihui Dai |
spellingShingle |
Yonghui Dai Dongmei Han Weihui Dai Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain The Scientific World Journal |
author_facet |
Yonghui Dai Dongmei Han Weihui Dai |
author_sort |
Yonghui Dai |
title |
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain |
title_short |
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain |
title_full |
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain |
title_fullStr |
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain |
title_full_unstemmed |
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain |
title_sort |
modeling and computing of stock index forecasting based on neural network and markov chain |
publisher |
Hindawi Limited |
series |
The Scientific World Journal |
issn |
2356-6140 1537-744X |
publishDate |
2014-01-01 |
description |
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. |
url |
http://dx.doi.org/10.1155/2014/124523 |
work_keys_str_mv |
AT yonghuidai modelingandcomputingofstockindexforecastingbasedonneuralnetworkandmarkovchain AT dongmeihan modelingandcomputingofstockindexforecastingbasedonneuralnetworkandmarkovchain AT weihuidai modelingandcomputingofstockindexforecastingbasedonneuralnetworkandmarkovchain |
_version_ |
1725957164497895424 |