Stock index forecasting based on TBA fusion model - HAN Di; GUO Wei; LIAO Kai; SUN Chuanyi; WANG Bocheng; LIN Kunling (Shenzhen Daxue xuebao. Ligong ban)
Due to the volatility and complexity of stock market, stock index prediction has always been a challenge in the field of financial forecasting. Long short-term memory (LSTM) network model is commonly used in financial index forecasting, however, this model has some limitations in long time series which may lead to insufficient use of data information. By using bidirectional long short-term memory network model (BiLSTM), temporal convolutional network (TCN) and attention mechanism, a novel fusion model named TCN-BiLSTM-attention (hereinafter referred to as TBA model) for stock index forecasting is constructed to further improve the ability of recognition and extraction of long time series data features of the model. Taking the public stock index datasets within China for nearly 30 years as an example, the TBA model is compared and ablated with the current mainstream machine learning and neural network prediction algorithms in finance as well as the top ranked models in Kaggle. The experimental results show the TBA model has significantly lower average prediction error and more stable performance compared with the average error baseline of the control experimental group in the forecasting of SSE, CSI 300 and GEI multi-day indices, thus this model can be used in a variety of financial forecasting scenarios based on time series.