In recent years, with the rapid development of the economy, more and more people have entered the stock market for investment. Due to the volatility characteristics of the stock market, stock price prediction is often...
详细信息
ISBN:
(纸本)9783031366246;9783031366253
In recent years, with the rapid development of the economy, more and more people have entered the stock market for investment. Due to the volatility characteristics of the stock market, stock price prediction is often a nonlinear time series prediction. And the fluctuation of stock prices will be affected by many factors, so it is difficult to predict through a simple model. For solving this problem, a hybrid adaptive particle swarm optimization and bp neural network algorithm (apso-bp) is proposed. The apso-bp algorithm effectively integrates the global search ability of the PSO algorithm and the local search ability of the bpalgorithm and further improves the prediction accuracy. Two sets of real stock data of China's stock market are applied to empirical analysis, and the results show that the algorithm is more effective than the standard bpalgorithm in solving this problem and can provide timely risk warning information for investors.
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