Deep learning models for stock prediction on diverse datasets

Rachna Sable; Shivani Goel; Pradeep Chatterjee; Mani Jindal

doi:10.46947/joaasr632024949

Authors

Rachna Sable Department of Artificial Intelligence and Data Science, GHRCEM, Pune University, Maharashtra, India.
Shivani Goel Department of Computer Science and Engineering, Bennett University, Greater Noida, U.P. – 201310, India
Pradeep Chatterjee Department of Head Digital Transformation & Customer Experience, GDC, Tata Motors, Pune, India.
Mani Jindal School of Business and Management, Christ University, Delhi NCR, Ghaziabad, India

DOI:

https://doi.org/10.46947/joaasr632024949

Keywords:

Stock Prediction, LSTM, CNN, RMSE, Deep Learning

Abstract

Market forecasting has attracted the interest of investors all over the world. The investors are looking for an accurate and reliable forecasting model that can fully embrace the extremely volatile and nonlinear market behavior. It is now possible to design effective stock price prediction algorithms due to the abundance of data, the quick advancement of AI and machine learning techniques, and the machine's increased computational capability. Deep learning algorithms are particularly successful in modelling market volatility. To forecast the closing prices of three stocks: Apple (AAPL), Google (GOOG), and Amazon (AMZN), Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) are implemented and compared. The stock data was obtained from yahoo finance for one year, three years and five years. The Root Mean Square Error (RMSE) metric and loss are employed for evaluating the model’s performance.

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