# Applications of deep learning in stock market prediction: recent progress

arXiv: https://arxiv.org/abs/2003.01859 · HTML: https://mlsanity.com/paper/2003.01859/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 30 of 51)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models](https://mlsanity.com/paper/2405.14767/) (2024)
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- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022](https://mlsanity.com/paper/2305.04811/) (2023)
- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction](https://mlsanity.com/paper/2401.01846/) (2024)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Continuous-time Autoencoders for Regular and Irregular Time Series Imputation](https://mlsanity.com/paper/2312.16581/) (2023)
- [MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction](https://mlsanity.com/paper/2409.05698/) (2024)
- [Modeling News Interactions and Influence for Financial Market Prediction](https://mlsanity.com/paper/2410.10614/) (2024)
- [Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model](https://mlsanity.com/paper/2202.02723/) (2022)
- [E2EAI: End-to-End Deep Learning Framework for Active Investing](https://mlsanity.com/paper/2305.16364/) (2023)
- [Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting](https://mlsanity.com/paper/2608.01857/) (2026)
- [Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools](https://mlsanity.com/paper/2103.11824/) (2021)
- [Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction](https://mlsanity.com/paper/2107.14033/) (2021)
- [Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models](https://mlsanity.com/paper/2211.08281/) (2022)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy](https://mlsanity.com/paper/2110.12062/) (2021)
- [Factor Investing with a Deep Multi-Factor Model](https://mlsanity.com/paper/2210.12462/) (2022)
- [CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction](https://mlsanity.com/paper/2411.06391/) (2024)
- [QuantBench: Benchmarking AI Methods for Quantitative Investment](https://mlsanity.com/paper/2504.18600/) (2025)
- [DDPG based on multi-scale strokes for financial time series trading strategy](https://mlsanity.com/paper/2207.10071/) (2022)
- [Effective Adapter for Face Recognition in the Wild](https://mlsanity.com/paper/2312.01734/) (2023)
- [Adaptive Boosting with Fairness-aware Reweighting Technique for Fair Classification](https://mlsanity.com/paper/2401.03097/) (2024)
- [Predicting Stock Price Movement with LLM-Enhanced Tweet Emotion Analysis](https://mlsanity.com/paper/2510.03633/) (2025)
- [Adaptive Weighted Genetic Algorithm-Optimized SVR for Robust Long-Term Forecasting of Global Stock Indices for investment decisions](https://mlsanity.com/paper/2512.15113/) (2025)
- [VertiFuseX: Generalizable Financial Forecasting via Multi-Stream Temporal Fusion](https://mlsanity.com/paper/2609.12793/) (2026)
- [WaVeFuse: Regime-Adaptive Equity Index Forecasting via Channel-Wise Wavelet Denoising and Vertical Attention Fusion](https://mlsanity.com/paper/2609.14733/) (2026)

## Abstract (arXiv)

Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Base on the summary, we also highlight some future research directions in this topic.