# Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

arXiv: https://arxiv.org/abs/2009.10819 · HTML: https://mlsanity.com/paper/2009.10819/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models](https://mlsanity.com/paper/2103.15096/) (2021)
- [Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH](https://mlsanity.com/paper/2105.13898/) (2021)
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- [Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model](https://mlsanity.com/paper/2104.06259/) (2021)
- [Stock Portfolio Optimization Using a Deep Learning LSTM Model](https://mlsanity.com/paper/2111.04709/) (2021)
- [Design and Analysis of Robust Deep Learning Models for Stock Price Prediction](https://mlsanity.com/paper/2106.09664/) (2021)
- [Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models](https://mlsanity.com/paper/2111.01137/) (2021)
- [Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks](https://mlsanity.com/paper/2202.02728/) (2022)
- [Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model](https://mlsanity.com/paper/2203.01326/) (2022)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market](https://mlsanity.com/paper/2107.11371/) (2021)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
- [Review of deep learning models for crypto price prediction: implementation and evaluation](https://mlsanity.com/paper/2405.11431/) (2024)
- [Adaptive Weighted Genetic Algorithm-Optimized SVR for Robust Long-Term Forecasting of Global Stock Indices for investment decisions](https://mlsanity.com/paper/2512.15113/) (2025)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)](https://mlsanity.com/paper/2101.09356/) (2021)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)](https://mlsanity.com/paper/2104.14406/) (2021)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)
- [Enhancing Multistep Prediction of Multivariate Market Indices Using Weighted Optical Reservoir Computing](https://mlsanity.com/paper/2408.00652/) (2024)
- [Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals](https://mlsanity.com/paper/2410.07220/) (2024)
- [Understanding the Impact of News Articles on the Movement of Market Index: A Case on Nifty 50](https://mlsanity.com/paper/2412.06794/) (2024)
- [Boosting the Accuracy of Stock Market Prediction via Multi-Layer Hybrid MTL Structure](https://mlsanity.com/paper/2501.09760/) (2025)
- [Predicting Stock Prices using Permutation Decision Trees and Strategic Trailing](https://mlsanity.com/paper/2504.12828/) (2025)
- [Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations](https://mlsanity.com/paper/2510.10695/) (2025)

## Abstract (arXiv)

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for the all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week open value of the NIFTY 50 time series is the most accurate model.