Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy

ABSTRACT The recent advancement of deep learning architectures, neural networks, and the combination of abundant financial data and powerful computers are transforming finance, leading us to develop an advanced method for predicting future stock prices. However, the accessibility of investment and trading at everyone's fingertips made the stock markets increasingly intricate and prone to volatility. The increased complexity and volatility of the stock market have driven demand for more models, which would effectively capture high volatility and non-linear behavior of the different stock prices. The aim is to improve the prediction accuracy of the next day's closing price of the NIFTY 50 index, a prominent Indian stock market index, using TPE-GRNN. A combination of eight influential factors is carefully chosen from fundamental stock data, technical indicators, crude oil price, and macroeconomic data to train the models for capturing the changes in the price of the index with the factors of the broader economy. Single-layer and multi-layer TPE-GRNN models have been developed. The models' performance is evaluated using standard matrices like R The analysis of models' performance reveals the impact of feature selection and hyperparameter optimization (HPO) in enhancing stock index price prediction accuracy. The results show that the MAPE of our proposed TPE-LSTM method is the lowest (best) with respect to all the previous models for stock index price prediction. Introduction The stock markets are complex systems where traders and investors can participate in publicly traded companies for buying and selling shares, hoping to profit from price fluctuations. Access to trading and investment at the fingertips of every interested person is increasingly making the stock market further intricate, noisy, and unpredictable. All the stock market participants are asking for higher profits and lower risk. Stock price or stock index price prediction is undeniably the upper choice for investors and financial analysts due to its extensive implementation area and significant effect on profit and loss.