# Stock Prices Prediction using Deep Learning Models

arXiv: https://arxiv.org/abs/1909.12227 · HTML: https://mlsanity.com/paper/1909.12227/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images](https://mlsanity.com/paper/2410.19291/) (2024)
- [A Comparative Evaluation of Predominant Deep Learning Quantified Stock Trading Strategies](https://mlsanity.com/paper/2103.15304/) (2021)
- [Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals](https://mlsanity.com/paper/2410.07220/) (2024)

## Abstract (arXiv)

Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In this study, we focus on predicting stock prices by deep learning model. This is a challenge task, because there is much noise and uncertainty in information that is related to stock prices. So this work uses sparse autoencoders with one-dimension (1-D) residual convolutional networks which is a deep learning model, to de-noise the data. Long-short term memory (LSTM) is then used to predict the stock price. The prices, indices and macroeconomic variables in past are the features used to predict the next day's price. Experiment results show that 1-D residual convolutional networks can de-noise data and extract deep features better than a model that combines wavelet transforms (WT) and stacked autoencoders (SAEs). In addition, we compare the performances of model with two different forecast targets of stock price: absolute stock price and price rate of change. The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.