Stock Movement Prediction with Financial News using Contextualized Embedding from BERT

• URL: http://arxiv.org/abs/2107.08721v1
• Date: Mon, 19 Jul 2021 09:47:28 GMT
• Title: Stock Movement Prediction with Financial News using Contextualized Embedding from BERT
• Authors: Qinkai Chen
• Abstract summary: We introduce a new text mining method called Fine-Tuned Contextualized-Embedding Recurrent Neural Network (FT-CE-RNN) Our model uses contextualized vector representations of the headlines (contextualized embeddings) generated from Bidirectional Representations from Transformers (BERT) It shows significant improvement compared with other baseline models, in both accuracy and trading simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis [2.7921137693344384]
  We use deep learning networks, based on the history of stock prices and articles of financial, business, technical news that introduce market information to predict stock prices. We developed a pre-trained NLP model known as FinBERT, designed to discern the sentiments within financial texts. This model utilizes news categories related to the stock market structure hierarchy, namely market, industry, and stock related news categories, combined with the stock market's stock price situation in the previous week for prediction.
  arXiv  Detail & Related papers  (2024-07-23T03:26:07Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Towards systematic intraday news screening: a liquidity-focused approach [1.688090639493357]
  Given the huge amount of news articles published each day, most of which are neutral, we present a systematic news screening method to identify the true'' impactful ones. We show that the screened dataset leads to more effective feature capturing and thus superior performance on short-term asset return prediction.
  arXiv  Detail & Related papers  (2023-04-11T10:14:48Z)
• Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data [0.0]
  We propose a new stock movement prediction framework: Multi-Graph Recurrent Network for Stock Forecasting (MGRN) This architecture allows to combine the textual sentiment from financial news and multiple relational information extracted from other financial data. Through an accuracy test and a trading simulation on the stocks in the STOXX Europe 600 index, we demonstrate a better performance from our model than other benchmarks.
  arXiv  Detail & Related papers  (2021-07-22T21:57:18Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)
• Graph Convolutional Network for Swahili News Classification [78.6363825307044]
  This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification.
  arXiv  Detail & Related papers  (2021-03-16T21:03:47Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• A Novel Distributed Representation of News (DRNews) for Stock Market Predictions [4.963115946610032]
  A novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. DRNews creates news vectors that describe both the semantic information and potential linkages among news events. The attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news.
  arXiv  Detail & Related papers  (2020-05-24T10:01:27Z)
• News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition [36.98298182622104]
  We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously. We are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction.
  arXiv  Detail & Related papers  (2020-04-04T07:17:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.