Combining Interpretable Embedded Multicriteria Feature Cross-Selection Engineering and Machine Learning to Mimic the Brain for Stock Trading Signal Prediction

Stock trading signal prediction is very important for investors’ trading decisions. However, since the stock market is a complex and nonlinear system, stock trading is frequent and complex. Human beings cannot integrate all the relevant information in time and make the right decisions by their brains alone. Machine learning can mimic the brain, learn from experience, and discover the connection between different things, thus realizing correct prediction and decision-making. Therefore, this study innovatively proposes a fusion of interpretable embedded multicriteria feature cross-selection engineering to capture effective features. Meanwhile, an optimized neural network prediction model is proposed where the Bayesian (BO) algorithm assumes the task of searching for hyperparameter combinations. The methods are as follow: (1) Daily stock prices are categorized into four types of key points for stock trading signals based on the time series extreme point algorithm. (2) A more comprehensive range of impact factors is constructed. Starting from the stock’s historical trading data, based on the stock’s trend, volatility, and turnover flow, five categories of technical indicators are constructed: Overlap Study, Momentum Indicator, Momentum Indicator, Volatility Indicator, and Price Conversion. (3) To construct a feature cross-selection method with multiple feature screening criteria to find the optimal feature influencing factors from different evaluation dimensions. (4) The hyper-parameters of the Artificial Neural Network (ANN) are optimized using Bayesian optimization algorithm. The optimized ANN is then used to model the data and obtain predictions. Twenty stocks were randomly selected from Shanghai Stock Exchange and Shenzhen Stock Exchange as experimental data to verify the validity of the model. The accuracy of the model proposed in this paper is 54.83%, 55.46%, and 54.70% for stocks with upward, steady, and downward trends respectively. The accuracy is on average 7.93%, 8.09%, and 8.09% higher than the comparison model. The return on investment through the predicted results of the model is 21.87%, 7.76%, and −3.51% respectively, which is better than the other comparative models. It can be seen from the experiments that the feature cross-selection method with multi-feature screening criterion can help the model to better find the optimal feature influencing factor, which helps to improve the accuracy of prediction. The Bayesian optimization algorithm contributes to the performance improvement of the ANN. After modeling the features using the Bayesian optimized ANN, the stock trading signal prediction model proposed in this paper is significantly better than other prediction models.