By a News Reporter-Staff News Editor at Life Science Weekly -- Data detailed on Life Science Research have been presented. According to news reporting originating in Yokohama, Japan, by NewsRx journalists, research stated, "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 news reporters obtained a quote from the research from Keio University, "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."
According to the news reporters, the research concluded: "Experimental results show that our proposed model performed better than other models including ones using state of the art techniques."
For more information on this research see: Stock price change rate prediction by utilizing social network activities. Thescientificworldjournal [electronic Resource], 2014;2014():861641 (see also Life Science Research).
Our news correspondents report that additional information may be obtained by contacting S. Deng, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan. Additional authors for this research include T. Mitsubuchi and A. Sakurai.
Keywords for this news article include: Asia, Japan, Yokohama, Life Science Research.
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