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

arXiv: https://arxiv.org/abs/2010.13891 · HTML: https://mlsanity.com/paper/2010.13891/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models](https://mlsanity.com/paper/2103.15096/) (2021)
- [Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis](https://mlsanity.com/paper/2408.12115/) (2024)
- [Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model](https://mlsanity.com/paper/2104.06259/) (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)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks](https://mlsanity.com/paper/2210.00984/) (2022)
- [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)
- [Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting](https://mlsanity.com/paper/2304.09840/) (2023)
- [Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis](https://mlsanity.com/paper/2502.15813/) (2025)
- [Using Robust Regression to Find Font Usage Trends](https://mlsanity.com/paper/2106.15232/) (2021)
- [Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction](https://mlsanity.com/paper/2306.07296/) (2023)
- [Convolutional Neural Network(CNN/ConvNet) in Stock Price Movement Prediction](https://mlsanity.com/paper/2106.01920/) (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)
- [Portfolio Optimization: A Comparative Study](https://mlsanity.com/paper/2307.05048/) (2023)
- [Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks](https://mlsanity.com/paper/2309.13696/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)
- [Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture](https://mlsanity.com/paper/2501.10454/) (2025)
- [Investigating Market Strength Prediction with CNNs on Candlestick Chart Images](https://mlsanity.com/paper/2501.12239/) (2025)
- [Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion](https://mlsanity.com/paper/2603.19286/) (2026)

## Abstract (arXiv)

Designing robust and accurate predictive models for stock price prediction has been an active area of research for a long time. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. There exist propositions in the literature that have demonstrated that if properly designed and optimized, predictive models can very accurately and reliably predict future values of stock prices. This paper presents a suite of deep learning based models for stock price prediction. We use the historical records of the NIFTY 50 index listed in the National Stock Exchange of India, during the period from December 29, 2008 to July 31, 2020, for training and testing the models. Our proposition includes two regression models built on convolutional neural networks and three long and short term memory network based predictive models. To forecast the open values of the NIFTY 50 index records, we adopted a multi step prediction technique with walk forward validation. In this approach, the open values of the NIFTY 50 index are predicted on a time horizon of one week, and once a week is over, the actual index values are included in the training set before the model is trained again, and the forecasts for the next week are made. We present detailed results on the forecasting accuracies for all our proposed models. The results show that while all the models are very accurate in forecasting the NIFTY 50 open values, the univariate encoder decoder convolutional LSTM with the previous two weeks data as the input is the most accurate model. On the other hand, a univariate CNN model with previous one week data as the input is found to be the fastest model in terms of its execution speed.