# Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model

arXiv: https://arxiv.org/abs/2206.06606 · HTML: https://mlsanity.com/paper/2206.06606/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis](https://mlsanity.com/paper/2005.05635/) (2020)
- [Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading](https://mlsanity.com/paper/2105.12825/) (2021)
- [Self-Supervised Multimodal Opinion Summarization](https://mlsanity.com/paper/2105.13135/) (2021)

## Built on by (most cited first, 12 of 12)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [FinGPT: Democratizing Internet-scale Data for Financial Large Language Models](https://mlsanity.com/paper/2307.10485/) (2023)
- [TradExpert: Revolutionizing Trading with Mixture of Expert LLMs](https://mlsanity.com/paper/2411.00782/) (2024)
- [FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model](https://mlsanity.com/paper/2403.02647/) (2024)
- [FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction](https://mlsanity.com/paper/2502.05218/) (2025)
- [No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks](https://mlsanity.com/paper/2403.06249/) (2024)
- [Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models](https://mlsanity.com/paper/2411.06272/) (2024)
- [Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism](https://mlsanity.com/paper/2406.06594/) (2024)
- [Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities](https://mlsanity.com/paper/2504.07274/) (2025)
- [Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction](https://mlsanity.com/paper/2409.17419/) (2024)
- [Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data](https://mlsanity.com/paper/2412.10540/) (2024)
- [Analyzing public sentiment to gauge key stock events and determine volatility in conjunction with time and options premiums](https://mlsanity.com/paper/2502.05403/) (2025)

## Abstract (arXiv)

Natural Language Processing(NLP) demonstrates a great potential to support financial decision-making by analyzing the text from social media or news outlets. In this work, we build a platform to study the NLP-aided stock auto-trading algorithms systematically. In contrast to the previous work, our platform is characterized by three features: (1) We provide financial news for each specific stock. (2) We provide various stock factors for each stock. (3) We evaluate performance from more financial-relevant metrics. Such a design allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a more realistic setting. In addition to designing an evaluation platform and dataset collection, we also made a technical contribution by proposing a system to automatically learn a good feature representation from various input information. The key to our algorithm is a method called semantic role labeling Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a compact representation of each news paragraph. Based on SRLP, we further incorporate other stock factors to make the final prediction. In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system. Through our experimental study, we show that the proposed method achieves better performance and outperforms all the baselines' annualized rate of return as well as the maximum drawdown of the CSI300 index and XIN9 index on real trading. Our Astock dataset and code are available at https://github.com/JinanZou/Astock.