Chaotic Recurrent Neural Networks for Financial Forecast

Abstract

In the past few decades, with the development of artificial intelligence and computer hardware, machine learning has been widely used in various applications including industrial, healthcare, education, finance, etc. Predicting financial time series sequences with effective AI tools for more accurate results has always been one of the hottest topics in finance and AI community. In this paper, the author introduces a new type of recurrent neural network algorithm, called Chaotic Recurrent Neural Network (CRNN), which is based on Dr. Raymond’s original research on Lee-Oscillator and Recurrent Neural Network (RNN) for worldwide financial prediction. We replaced the traditional activation function with a Lee Oscillator Neural Network, which not only can solve the vanishing gradient problem of traditional recurring neural networks during algorithm training, but can also provide an excellent memory correlation mechanism during long-term time series processing. The Experimental results reveal that CRNN outperforms than some popular neural network which widely applied to predict financial data, such as FFBPN, RNN, LSTM, in terms of forecast accuracy in certain cases. The experimental environment is based on Pytorch and Python 3.8, using 10 years (2010-2020) major financial index data, including DJI, HSI, IXIC, SPX, SSE, SZSE, APPL, to forecast 31th day closing price with previous 30 days closing price. Besides financial forecasting, our CRNN algorithm also has many potential applications, such as Natural Language Processing, weather forecasting, etc.