# Financial Markets Prediction with Deep Learning

arXiv: https://arxiv.org/abs/2104.05413 · HTML: https://mlsanity.com/paper/2104.05413/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)

## Built on by (most cited first, 5 of 5)

- [CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets](https://mlsanity.com/paper/2104.04041/) (2021)
- [Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets](https://mlsanity.com/paper/2404.07225/) (2024)
- [Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements](https://mlsanity.com/paper/2202.03158/) (2022)
- [Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy](https://mlsanity.com/paper/2205.08382/) (2022)
- [Superfast Selection for Decision Tree Algorithms](https://mlsanity.com/paper/2405.20622/) (2024)

## Abstract (arXiv)

Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.