  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. This study explored
gated recurrent neural network (GRNN) algorithms such as LSTM (long short-term
memory), GRU (gated recurrent unit), and hybrid models like GRU-LSTM, LSTM-GRU,
with Tree-structured Parzen Estimator (TPE) Bayesian optimization for
hyperparameter optimization (TPE-GRNN). 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 R2, MAPE, and RMSE. 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.
