Predicting the further trends of Chinese stock index based on the one-dimensional convolutional neural network

In the sphere of financial investment, predicting future trends of stock market indexes using historical transaction data is a critical topic. As the complexity and extreme volatility of the stock market, precisely predicting the trajectory of the indexes is challenging. Aiming at the volatility of short-term index prediction tasks, a long-term prediction method termed One-Covn is proposed. Specifically, this method takes the mean scale of rise and fall in further few days instead of the following one day as the prediction label. First, a data normalization method is proposed, in which the historical transaction data are transformed to the scale of rise and fall. Then, a one-day step sliding window is applied to split the sequence data to prediction samples and the corresponding labels are obtained at the same time. Finally, the one-dimensional convolutional network is utilized to extract the sample deep features and also map the feature to the prediction label. To evaluate the algorithm’s performance, 42 Chinese stock market indices were chosen as experimental data, the mean absolute error (MAE) and mean square error (MSE) were utilized as training loss functions. Classic approaches including ANN, LSTM, CNN LSTM were chosen as comparison benchmarks. The results show that the method can effectively reduce the average prediction error.