Research on Stock Index Futures Trading Strategy and Risk Management Based on Genetic Algorithm
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DOI: 10.25236/ecomhs.2019.208
Corresponding Author
Wang Yufeng
Abstract
Neural network integration technology can effectively improve the learning ability and generalization ability of neural networks, and has become a research hotspot in the field of machine learning and neural computing. This paper makes use of different neural network algorithms to generate neural network ensemble individuals. Using the criterion of minimizing the sum of squared errors, we use the genetic algorithm to dynamically solve the non-negative weight coefficients of the integrated individuals to perform optimal combinational integration modeling research. And to establish a stock market prediction model. Through the Shanghai Stock Exchange Index opening price, closing price for example analysis.
Keywords
Genetic algorithm, Stock index futures trading strategy, Risk management