A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimization.

Due to the complexity and volatility of stock market trading, there are still some issues in the existing prediction methods, including the processing of data noise, inexplicable selection of model parameters, and the lag in predicting price fluctuations. Aiming to better predict daily stock prices and fluctuations caused by high noise non-stationary data in actual trading, we propose a hybrid deep learning framework based on Singular Spectrum Analysis (SSA), multiple feature selection, and Long Short Term Memory (LSTM) network optimized by Particle Swarm Optimization (PSO). Based on Pearson correlation coefficients, we select features highly correlated with the closing price as inputs, and further achieve the noise reduction and optimization of those input sources by applying SSA to decompose the stock price time series into independent component signals. Using the stock price data from China and USA, we compared the prediction performance of our method with several well-known methods, and found that it achieved higher price prediction accuracy during periods of high stock volatility. For example, the R-squared and prediction accuracy of Shanghai Composite Index achieved 0.9998 and 99.01%, while the prediction metrics of S &P 500 reached 0.9883 and 94.26%, respectively. Besides, considering transaction costs, our method also achieved the highest profit in trading tests, even compared to long-term holding strategy. [ABSTRACT FROM AUTHOR]