Comparative Analysis of LSTM, GRU, and ARIMA Models for Stock Market Price Prediction


  • Rushil Yavasani Morris Hills High School
  • Frank Wang King's College London



Statistics, Data Science, Economics, Mathematics, Artificial Intelligence, Machine Learning


This study delves into the efficacy of various machine learning and statistical models that have captured the attention of financial analysts. Two of them, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are variations of Recurrent Neural Networks while the Autoregressive Integrated Moving Average (ARIMA) is a statistical model. These models will be used to forecast stock market data across different economic sectors. In the dynamic landscape of financial markets, accurate forecasting is crucial. This research paper contributes to quantitative finance by conducting a comprehensive comparative analysis of these models on historical stock market data from three sectors: extraction, manufacturing, and service (which are considered the primary, secondary, and tertiary sectors of the economy respectively). The models' performances are evaluated using mean squared error (MSE) on six selected stocks representing these sectors. Results reveal the power of recurrent neural networks in capturing intricate patterns. Moreover, the results will explore whether or not the efficacy of each model is impacted by the sector of the economy that it is forecasting data for.


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How to Cite

Yavasani, R., & Wang, H. (2023). Comparative Analysis of LSTM, GRU, and ARIMA Models for Stock Market Price Prediction. Journal of Student Research, 12(4).



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