Study on the optimal investment strategy based on single target PSO
DOI:
https://doi.org/10.54691/bcpbm.v30i.2254Keywords:
Single-objective Programming; PSO; Quantitative Investment.Abstract
With the development of science, technology and popularity of electronic transactions, quantitative investment transactions have become the focal point in the field of investment. In order to maximize returns during the investment period, we will build a single-objective PSO-based decision model based on asset price trend forecast data to develop a daily investment strategy. At first, we use price volatility as a risk factor and determine the risk level in combination with the application principle of ROC. Then, combined with the prediction results, we distinguish the gold market trading days and non-trading days, establish a single-objective programming model with the maximum daily net income as the goal. Finally, we use PSO algorithm to optimize the solution process. And we can get the daily optimal investment strategy and the final asset is $ 226648.42.
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References
Fama E F. The Behavior of Stock Market Price[J]. The Journal of Business, 1965, 38(1): 34-105.
Aimin Zhou. The Model-est for the Prediction of Stock Markets, [J]. Application of Statistics and Management, 1999(01): 6-11.
Zongze Cai, Changhui Yao. Predictability and Variance Decomposition of Stock Re-turns[J]. Economic Science, 2009(02): 108-116.
Feng Xu. GARCH Model for Stock Price Prediction [J]. Statistics and Decision, 2006(18): 107-109.
Samuel A L. Some Studies in Machine Learning Using the Game of Checks[J]. IBM Journal of Research and Development, 1959, 3(3): 211-229.
White H. Economic prediction using neural networks: the case of IBM daily returns [C]. IEEE International Conference on Neural Networks, California, 451-458.
Kim KJ. Financial Time Series Forecasting Using Support Vector Machines[J]. Neuro-computing, 2003, 55(1/2): 307-319.
Xiao Zhang, Zengxin Wei. Application of Random Forest in Stock Trend Forecasting[J]. Management Informationization in China, 2018, 12(2): 120-123.
Yanping Xu. Urban Air Quality Prediction Model Based on Improved Random Forest Algorithm-ake Chongqing as an example[D]. Chongqing Technology and Business University, 2021.
Zhang X. Single and multi-optimization based on particle swarm optimization algo-rithm[D]. Liaoning:University of Science and Technology Liaoning, 2021.
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