# Classification-based Financial Markets Prediction using Deep Neural Networks

arXiv: https://arxiv.org/abs/1603.08604 · HTML: https://mlsanity.com/paper/1603.08604/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2016.

## Built on by (most cited first, 25 of 25)

- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://mlsanity.com/paper/2305.08740/) (2023)
- [Reachability Is NP-Complete Even for the Simplest Neural Networks](https://mlsanity.com/paper/2108.13179/) (2021)
- [LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU](https://mlsanity.com/paper/2409.08282/) (2024)
- [HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2212.00325/) (2022)
- [Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs](https://mlsanity.com/paper/2206.14284/) (2022)
- [Financial Markets Prediction with Deep Learning](https://mlsanity.com/paper/2104.05413/) (2021)
- [Reachability In Simple Neural Networks](https://mlsanity.com/paper/2203.07941/) (2022)
- [Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers](https://mlsanity.com/paper/2110.07029/) (2021)
- [Chain-structured neural architecture search for financial time series forecasting](https://mlsanity.com/paper/2403.14695/) (2024)
- [Learning Fairer Representations with FairVIC](https://mlsanity.com/paper/2404.18134/) (2024)
- [Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market](https://mlsanity.com/paper/2406.10695/) (2024)
- [A new Video Synopsis Based Approach Using Stereo Camera](https://mlsanity.com/paper/2106.12362/) (2021)
- [Deep Partial Least Squares for Empirical Asset Pricing](https://mlsanity.com/paper/2206.10014/) (2022)
- [Adversarial training with informed data selection](https://mlsanity.com/paper/2301.04472/) (2023)
- [Higher-order Graph Attention Network for Stock Selection with Joint Analysis](https://mlsanity.com/paper/2306.15526/) (2023)
- [Dynamic financial processes identification using sparse regressive reservoir computers](https://mlsanity.com/paper/2310.12144/) (2023)
- [Checking extracted rules in Neural Networks](https://mlsanity.com/paper/2509.16547/) (2025)
- [Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications](https://mlsanity.com/paper/2103.12419/) (2021)
- [Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements](https://mlsanity.com/paper/2202.03158/) (2022)
- [Corporate Credit Rating: A Survey](https://mlsanity.com/paper/2309.14349/) (2023)
- [Robustness Verifcation in Neural Networks](https://mlsanity.com/paper/2403.13441/) (2024)
- [On Multivariate Financial Time Series Classification](https://mlsanity.com/paper/2504.17664/) (2025)
- [Long-only cryptocurrency portfolio management by ranking the assets: a neural network approach](https://mlsanity.com/paper/2512.08124/) (2025)
- [S&P 500 Stock's Movement Prediction using CNN](https://mlsanity.com/paper/2512.21804/) (2025)
- [Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction](https://mlsanity.com/paper/2603.19288/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. In particular we describe the configuration and training approach and then demonstrate their application to backtesting a simple trading strategy over 43 different Commodity and FX future mid-prices at 5-minute intervals. All results in this paper are generated using a C++ implementation on the Intel Xeon Phi co-processor which is 11.4x faster than the serial version and a Python strategy backtesting environment both of which are available as open source code written by the authors.