Stock Market Prediction Using a Hybrid Approach With Boruta and Liquid Neural Network

Globally, one of the most fundamental applications of time-series data is stock market forecasting, where each and every second is crucial and analysis is unpredictable, posing a significant challenge towards predicting its trend. Various research is underway for the analysis of the stock market using various time series algorithms such as long-short-term memory (LSTM), recurrent neural network (RNN). Here, in this article, we have proposed a novel approach of multiple layers including liquid time constant, the linear first order dynamics with nonlinear interlinked gates which improves performance of time series; after which we included liquid neural network and forecasted the results. Then, we enhanced the algorithm using Boruta feature selection, where we have selected only the required columns and ranked them as per requirement. Furthermore, for more accurate results, we have selected three different sets of historical data for five large cap sectors (Alphabet, Apple, Blackrock, JP Morgan, and IBM) after which we observed the R-square values along with all the performance evaluation and error evaluation results for all the individual dataset in two phases, i.e., before and after using the Boruta feature selection method for 5, 10, and 15 years data, we observed the best results for LSTM (0.9443) and RNN (0.9752). Liquid neural networks, being the most efficient and accurate, gave the best R-square value of 0.9835.