Volume 7, Issue 5, October 2014, Pages 853 - 864
A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation
Yongli Li, Chong Wu, Jiaming Liu, Peng Luo
Received 2 April 2013, Accepted 6 December 2013, Available Online 1 October 2014.
- https://doi.org/10.1080/18756891.2013.876722How to use a DOI?
- Combined forecast, Artificial intelligence, Decision analysis, Multi-Agent simulation, Stock composite index, Real world application
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Yongli Li AU - Chong Wu AU - Jiaming Liu AU - Peng Luo PY - 2014 DA - 2014/10/01 TI - A Combination Prediction Model of Stock Composite Index Based on Artificial Intelligent Methods and Multi-Agent Simulation JO - International Journal of Computational Intelligence Systems SP - 853 EP - 864 VL - 7 IS - 5 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.876722 DO - https://doi.org/10.1080/18756891.2013.876722 ID - Li2014 ER -