Generalized loss-based CNN-BiLSTM for stock market prediction

Stock market prediction (SMP) is challenging due to its uncertainty, nonlinearity, and volatility. Machine learning models such as recurrent neural networks (RNNs) have been widely used in SMP and have achieved high performance in terms of "minimum error". However, in the context of SMP, using "least cost" to measure performance makes more sense. False Positive Errors (FPE) can lead to significant trading losses, while False Negative Errors (FNE) can result in missed opportunities. Minimizing FPE is critical for investors. In practice, some errors may result in irreparable losses, so measuring costs based on data is important. In this research, we propose a new method called generalized loss CNN-BiLSTM (GL-CNN-BiLSTM), where the cost of each datum can be dynamically calculated based on the difficulty of the data. We verify the effectiveness of GL-CNN-BiLSTM on Shanghai, Hong Kong, and NASDAQ stock exchange data. Experimental results show that although there is no significant difference in the accuracy and winning rate between GL-CNN-BiLSTM and other methods, GL-CNN-BiLSTM achieves the highest rate of return on the test data.