# Deep learning for Stock Market Prediction

arXiv: https://arxiv.org/abs/2004.01497 · HTML: https://mlsanity.com/paper/2004.01497/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 10 of 10)

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- [Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization](https://mlsanity.com/paper/2203.05673/) (2022)
- [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)
- [Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals](https://mlsanity.com/paper/2410.07220/) (2024)
- [A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces](https://mlsanity.com/paper/2508.00472/) (2025)
- [A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios](https://mlsanity.com/paper/2609.13125/) (2026)

## Abstract (arXiv)

Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.