# Design and Analysis of Robust Deep Learning Models for Stock Price Prediction

**Authors:** Jaydip Sen, Sidra Mehtab  
**arXiv:** [2106.09664](https://arxiv.org/abs/2106.09664)  
**Date:** 2021-06-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2106.09664v1/

## Key points

- Predicting stock prices and movements accurately is a challenging research problem in finance
- The efficient market hypothesis suggests that it is impossible to predict future stock prices in an efficient stock market as they are assumed to be purely stochastic
- Researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and appropriate variables selection in the models
- Authors Jaydip Sen and Sidra Mehtab propose a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India
- The authors use the Metastock tool to download historical stock prices over a period of two years (2013- 2014) at 5-minute intervals. While records for the first year are used to train the models, testing is carried out using remaining records.
- The chapter presents detailed design approaches of all models and their performance results, including comparisons based on execution time and accuracy of prediction.
- The authors analyze various deep learning-based models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Hybrid CNN-LSTM, Hybrid GRU-CNN-LSTM, and Attention-based LSTM.
- They demonstrate that these models can provide accurate predictions by selecting appropriate hyperparameters.
- This chapter provides insights into building robust predictive models for accurate prediction of stock prices using deep learning techniques.
- It highlights how sophisticated algorithms coupled with appropriate variable selection can help overcome challenges associated with predicting future stock prices accurately.
- This research has significant implications for investors who rely on accurate predictions to make informed decisions about buying or selling stocks in financial markets worldwide.

## Summary

In the world of finance, predicting stock prices and movements accurately is a challenging research problem. The efficient market hypothesis suggests that it is impossible to predict future stock prices in an efficient stock market as they are assumed to be purely stochastic. However, researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and appropriate variables selection in the models. In this chapter titled "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," authors Jaydip Sen and Sidra Mehtab propose a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The authors use the Metastock tool to download historical stock prices over a period of two years (2013- 2014) at 5-minute intervals. While records for the first year are used to train the models, testing is carried out using remaining records. The chapter presents detailed design approaches of all models and their performance results, including comparisons based on execution time and accuracy of prediction. The authors analyze various deep learning-based models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Hybrid CNN-LSTM, Hybrid GRU-CNN-LSTM, and Attention-based LSTM. They demonstrate that these models can provide accurate predictions by selecting appropriate hyperparameters. This chapter provides insights into building robust predictive models for accurate prediction of stock prices using deep learning techniques. It highlights how sophisticated algorithms coupled with appropriate variable selection can help overcome challenges associated with predicting future stock prices accurately. This research has significant implications for investors who rely on accurate predictions to make informed decisions about buying or selling stocks in financial markets worldwide.