# Stock price forecast with deep learning

arXiv: https://arxiv.org/abs/2103.14081 · HTML: https://mlsanity.com/paper/2103.14081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 1 of 1)

- [Forecasting significant stock price changes using neural networks](https://mlsanity.com/paper/1912.08791/) (2019)

## Abstract (arXiv)

In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively.