Stock Price Change Rate Prediction by Utilizing Social Network Activities
Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predic...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/861641 |
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| author | Shangkun Deng Takashi Mitsubuchi Akito Sakurai |
| author_facet | Shangkun Deng Takashi Mitsubuchi Akito Sakurai |
| author_sort | Shangkun Deng |
| collection | DOAJ |
| description | Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. |
| format | Article |
| id | doaj-art-e24d83cdafb04737a42e591effc81fc7 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e24d83cdafb04737a42e591effc81fc72025-02-03T05:50:15ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/861641861641Stock Price Change Rate Prediction by Utilizing Social Network ActivitiesShangkun Deng0Takashi Mitsubuchi1Akito Sakurai2Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanGraduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanGraduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JapanPredicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.http://dx.doi.org/10.1155/2014/861641 |
| spellingShingle | Shangkun Deng Takashi Mitsubuchi Akito Sakurai Stock Price Change Rate Prediction by Utilizing Social Network Activities The Scientific World Journal |
| title | Stock Price Change Rate Prediction by Utilizing Social Network Activities |
| title_full | Stock Price Change Rate Prediction by Utilizing Social Network Activities |
| title_fullStr | Stock Price Change Rate Prediction by Utilizing Social Network Activities |
| title_full_unstemmed | Stock Price Change Rate Prediction by Utilizing Social Network Activities |
| title_short | Stock Price Change Rate Prediction by Utilizing Social Network Activities |
| title_sort | stock price change rate prediction by utilizing social network activities |
| url | http://dx.doi.org/10.1155/2014/861641 |
| work_keys_str_mv | AT shangkundeng stockpricechangeratepredictionbyutilizingsocialnetworkactivities AT takashimitsubuchi stockpricechangeratepredictionbyutilizingsocialnetworkactivities AT akitosakurai stockpricechangeratepredictionbyutilizingsocialnetworkactivities |