NLP Papers

Repository of NLP papers useful for applying NLP techniques to financial markets.

NLP Financial Applications

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

• 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

Repos

• WayneDW/Sentiment-Analysis-in-Event-Driven-Stock-Price-Movement-Prediction
• v0d1ch/financial-news-scraper
• petrovsimeon/Financial-News-scraper

News

• AI Decodes Trading Signales Hidden in Jargon

Contribution

Contributions more than welcome :-)