Repository of NLP papers useful for applying NLP techniques to financial markets.
Direct applications of NLP research to financial markets.
- Analyzing Stock Market Movements Using Twitter Sentiment Analysis
- Deep Learning for Financial Sentiment Analysis on Finance News Providers
- Deep Learning for Stock Prediction Using Numerical and Textual Information
- Giving Content to Investor Sentiment: The Role of Media in the Stock Market
- The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models
- Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
- More Than Words: Quantifying Language to Measure Firms’ Fundamentals
- Predicting Stock Market Movement with Deep RNNs
- Predicting Stock Movement through Executive Tweets
- Sentiment Analysis in Financial News
- Sentiment Predictability for Stocks
- Textual Analysis of Stock Market Prediction Using Breaking Financial News: The AZFinText System
- Twitter mood predicts the stock market
- Natural Language Processing - Part 1: Primer
- An Analysis of Verbs in Financial News Articles and their Impact on Stock Prices
- Trading Strategies to Exploit Blog and News Sentiment
- From Word to Time Series Embedding
- The Effects of Conference Call Tones on Market Perceptions of Value Uncertainty
- The Capital Market Consequences of Language Barriers in the Conference Calls of Non-U.S. Firms
- Words versus Deeds: Evidence from Post-Call Manager Trades
- Linguistic Complexity in Firm Disclosures: Obfuscation or Information?
- When Managers Change Their Tone, Analysts and Investors Change Their Tune
- Buy-Side Analysts and Earnings Conference Calls
- Are Founder CEOs more Overconfident than Professional CEOs? Evidence from S&P 1500 Companies
- Speaking of the Short-Term: Disclosure Horizon and Managerial Myopia
- Finding Value in Earnings Transcripts Data with AlphaSense
- Using Unstructured and Qualitative Disclosures to Explain Accruals
- Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media
- Differences in Conference Call Tones: Managers Versus Analysts
- The Blame Game
- Can Investors Detect Managers’ Lack of Spontaneity? Adherence to Pre-determined Scripts during Earnings Conference Calls
- Predicting Returns with Text Data
- Domain Adaptation using Stock Market Prices to Refine Sentiment Dictionaries
- Climate change concerns and the performance of green versus brown stocks
- SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
- FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
- IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis
- IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text
- ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
- RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
- COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines
- UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation
- TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
- DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles
- SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
- funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter
- NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
- HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks
- INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines
- HHU at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods
- IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
- SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model
- Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
- Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines
- WayneDW/Sentiment-Analysis-in-Event-Driven-Stock-Price-Movement-Prediction
- v0d1ch/financial-news-scraper
- petrovsimeon/Financial-News-scraper
Contributions more than welcome :-)