# Stock Movement Prediction with Financial News using Contextualized Embedding from BERT

arXiv: https://arxiv.org/abs/2107.08721 · HTML: https://mlsanity.com/paper/2107.08721/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models](https://mlsanity.com/paper/1702.01978/) (2017)
- [From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations](https://mlsanity.com/paper/2001.09466/) (2020)

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

- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [Industry Risk Assessment via Hierarchical Financial Data Using Stock Market Sentiment Indicators](https://mlsanity.com/paper/2303.02707/) (2023)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction](https://mlsanity.com/paper/2510.15691/) (2025)
- [StonkBERT: Can Language Models Predict Medium-Run Stock Price Movements?](https://mlsanity.com/paper/2202.02268/) (2022)
- [DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents](https://mlsanity.com/paper/2111.00490/) (2021)
- [AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment](https://mlsanity.com/paper/2206.11072/) (2022)
- [Towards mapping the contemporary art world with ArtLM: an art-specific NLP model](https://mlsanity.com/paper/2212.07127/) (2022)
- [Towards systematic intraday news screening: a liquidity-focused approach](https://mlsanity.com/paper/2304.05115/) (2023)
- [On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?](https://mlsanity.com/paper/2312.14978/) (2023)
- [News Sentiment Embeddings for Stock Price Forecasting](https://mlsanity.com/paper/2507.01970/) (2025)
- [Adaptive Financial Sentiment Analysis for NIFTY 50 via Instruction-Tuned LLMs , RAG and Reinforcement Learning Approaches](https://mlsanity.com/paper/2512.20082/) (2025)

## Abstract (arXiv)

News events can greatly influence equity markets. In this paper, we are interested in predicting the short-term movement of stock prices after financial news events using only the headlines of the news. To achieve this goal, we introduce a new text mining method called Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN). Compared with previous approaches which use static vector representations of the news (static embedding), our model uses contextualized vector representations of the headlines (contextualized embeddings) generated from Bidirectional Encoder Representations from Transformers (BERT). Our model obtains the state-of-the-art result on this stock movement prediction task. It shows significant improvement compared with other baseline models, in both accuracy and trading simulations. Through various trading simulations based on millions of headlines from Bloomberg News, we demonstrate the ability of this model in real scenarios.