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A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation

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Abstract

Predicting stock composite index is useful, which can raise the interest of both the investors and the corresponding researchers. This paper presented a new combination prediction model based on the technique of artificial intelligence and the principle of combination forecast. The principle of combination forecast, as a valid foundation of the new model, was strictly proved and carefully illustrated in this paper. Given the predicting rules, the new combination model was established by synthesizing three commonly used prediction models based on the principle of combination forecast. The comprehensive usage of qualitative forecast and quantitative forecast is also a feature of the new model. To valid the new model, comparison analysis and multi-agent simulation were both applied. Besides, the application of multi-agent simulation made the new model able to guide the investors’ operations in a real stock market. According to the theoretical proof, the comparison analysis and the simulation experiment, the new combination prediction model tends to be a powerful and applicable tool in making the investment decisions.

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Author information

Authors and Affiliations

  1. School of Management, Harbin Institute of Technology, 150001, Harbin, P.R. China

    Yongli Li, Chong Wu & Peng Luo

  2. Beijing Institute of Information and Control, 100037, Beijing, P.R. China

    Jiaming Liu

Authors
  1. Yongli Li

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  2. Chong Wu

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  3. Jiaming Liu

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  4. Peng Luo

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Corresponding author

Correspondence toChong Wu.

Additional information

This paper was funded by the National Natural Science Foundation of China (71271070) and China Scholarship Council (201306120159).

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This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Li, Y., Wu, C., Liu, J.et al. A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation.Int J Comput Intell Syst7, 853–864 (2014). https://doi.org/10.1080/18756891.2013.876722

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