Stock price forecasting using PSO hypertuned neural nets and ensembling

Abstract

The stock market is a platform that allows individuals and organizations to buy stocks of publicly listed companies. It is imperative for investors and traders to utilize the platform to buy and sell stocks efficiently, but they must also determine when to do it in order to maximize profits. As trading involves holding stocks for shorter periods, projecting the future direction of a stock’s price becomes essential. In recent years, deep neural network-based trading strategies have been researched and implemented to identify when a stock’s price will increase or decrease. The main issues in implementing such solutions are that they need to deal with the noisy nature of the stock market and the problem of overfitting. The objective of the paper is to put forth an approach that utilizes deep learning techniques to predict price movements in the Nifty 50 index. The paper will explore the use of Recurrent Neural Networks (RNNs) for the given task. The paper will also look into applying metaheuristic algorithms to further improve the results of the prediction models. In this approach, RNNs, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), are utilized to predict the movement of the index. The models are trained on a unique and efficient feature set that takes into consideration the stock price of large market capitalization companies present in the National Stock Exchange (NSE). Our findings show that ensembled architectures produce better results than individual models. An LSTM and GRU ensembled architecture produced an accuracy of 56.66% and a precision of 0.4734. Particle swarm optimization (PSO) was put forth as a method to hypertune the models to improve their performance. The LSTM, hypertuned with PSO, produced an accuracy of 57.64% and a precision of 0.2882. To further enhance the model’s stock price prediction performance, the LSTM and GRU ensembled architecture was ensembled with the PSO hypertuned LSTM architecture to produce a model that gives the highest accuracy of 57.72%. The proposed ensemble approach outperforms the other cutting edge techniques used to forecast how the stock price of the NSE will move. Additionally, the ensemble method increased precision from 0.2882 to 0.5485, demonstrating that ensembling and the PSO algorithm combine to produce models with superior performance. Based on the results, combining PSO hyper parameter optimized models with ensembling provides a good approach towards price movement predictions and also shows the potential of using this approach in other Artificial Intelligence (AI) fields to improve the performance of deep learning models.