Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors

Nevendra Kumar Upadhyay; Vineet Singh; Shikha Singh; Pooja Khanna

doi:10.54060/jmss.v3i1.42

Authors

Nevendra Kr Upadhyay Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India
Vineet Singh Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India
Dr. Shikha Singh Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India
Dr. Pooja Khanna Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India

DOI:

https://doi.org/10.54060/jmss.v3i1.42

Keywords:

stocks, Prediction, RNN, LSTM, retail investor, deep learning algorithms, Stock Price Prediction, stock markets

Abstract

The stock markets are important components of the global financial system and have a considerable impact on an economy's growth and stability. This research article uses algorithms, notably deep learning, to increase the prediction of stock values. The efficacy and precision of long short-term memory (LSTM) and recurrent neural networks (RNN) algorithms to estimate stock prices are compared in this study. The paper investigates the potential of deep learning algorithms in creating a more predictable and trustworthy environment for the stock market. The study utilizes historical market data obtained from the Alpha Vault API and evaluates the performance of the RNN and LSTM models in forecasting stock prices. The results indicate that LSTM exhibits superior precision and is better suited for stock price prediction, while RNN faces certain challenges. Overall, this research contributes to the understanding of the application of deep learning algorithms in stock market analysis, to make informed investment decisions, thereby reducing risks and maximizing returns.

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