Evolving computationally efficient prediction model for Stock Volatility using CGPANN

Abstract

Financial market volatility has become one of the most difficult applications for stock price forecasting in ongoing situations. The current statistical models for stock price forecasting are too rigid and inefficient to appropriately deal with the uncertainty and volatility inherent in stock data. CGPANN-CGP based ANNs and LSTM are the most common methods used these days to predict such dynamics in time series data. In comparison to other methodologies, studies have demonstrated that the application of Cartesian genetic programming evolved Artificial Neural Networks (CGPANNs) to time series forecasting problems produces better results, and LSTM can be competitive at times. CGPANN provides the ability to train both structure, topology, and weights of network to achieve the global optimum solution. The prediction model is trained on the behavior of stock exchange patterns and is based on trends in historical daily stock prices. The proposed CGPANN and LSTM models produced competitive results of 98.86% and 98.52% respectively. However, CGPANN architecture is capable computationally efficient than LSTM and its ability of quick predictions makes it ideal for real-time applications.