Multi-factor stock price prediction based on GAN-TrellisNet.

Applying deep learning, especially time series neural networks, to predict stock price, has become one of the important applications in quantitative finance. Recently, some GAN-based stock prediction models are proposed, where LSTM or GRU is used as the generator. However, these generators lack the function of feature extraction, and the prediction accuracies are slightly low. Meanwhile, these models choose some simple volume-price factors (such as OCHLV and OCHLVC) as inputs, without considering the impact of other factors on stock prices. In order to solve these problems, a stock prediction method based on multiple factors and GAN-TrellisNet is proposed. Instead of "OCHLV" or "OCHLVC," a multi-factor strategy with "alpha158+OCHLVC" is introduced to enrich the stock data of inputs. The proposed generative adversarial network (GAN) is a combination of two neural networks which are TrellisNet as generative model and convolutional neural network (CNN) as discriminative model for adversarial training to forecast the stock market. TrellisNet, which integrates the feature extraction capabilities of CNN and the temporal processing capabilities of recurrent neural network (RNN), will generate new predicted results based on historical data, and then CNN will distinguish between predicted results and real stock prices. In order to demonstrate the performance of our method, we selected the decade data of different stocks from four markets (A-shares, U.S. stocks, U.K. stocks and Hong Kong stocks) as dataset and conducted two groups of comparative experiments. Compared with the state-of-the-art methods based on GAN, our method has better performance in terms of MSE, MAE, RMSE and MAPE. In addition, the multi-factor strategy with "alpha158+OCHLVC" is more effective than the original strategy with OCHLVC factors. [ABSTRACT FROM AUTHOR]