China?s commercial bank stock price prediction using a novel K-means-LSTM hybrid approach

China's commercial Bank shares have become the backbone of the capital market. The prediction of a bank's stock price has been a hot topic in the investment field. However, the stock price is always unstable and nonlinear, challenging the traditional statistical models. Inspired by this problem, a novel hybrid deep learning approach is proposed to improve prediction performance. By modifying the distance measurement algorithm into DTW, an improved K-means clustering algorithm is proposed to cluster out banks with similar price trends. Then those clustered stocks are used to train a long and short-term memory (LSTM) neural network model for static and dynamic stock price prediction. Besides, by transforming the output of the LSTM network into multi-step output to predict multi-time intervals at one time, the performance of the long-term forecasts is improved. Through experiments, it is found that the hybrid model performs better than the single model in generalization ability and accuracy(i.e. R-SQUARE, MAE, MSE). Moreover, the multi-step output static prediction outperforms the dynamic rolling prediction for long-term prediction. In summary, this approach can predict stock prices more accurately and help investors and companies to make more profitable decisions.

Automated summary

This study proposes a novel hybrid deep learning approach that utilizes an improved clustering algorithm and LSTM neural network model to predict stock prices more accurately. Experimental results demonstrate that this method outperforms traditional statistical models in terms of prediction ability and accuracy.