A Sentiment Analysis Approach to the Prediction of Market Volatility
Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modelling our classifier achieved a directional prediction accuracy for volatility of 63%.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementPortfolio OptimizationSentiment AnalysisSimilar Papers 제목 키워드 기반
Degree of Irrationality: Sentiment and Implied Volatility Surface
In this study, we constructed daily high-frequency sentiment data and used the VAR method to attempt to predict the next day's implied volatility surface. We utilized 630,000 text data entries from the East Money Stock F…
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast…
Information RetrievalRetrievalSentiment AnalysisWord EmbeddingsGRUvader: Sentiment-Informed Stock Market Prediction
Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market predicti…
PredictionSentiment AnalysisStock Market PredictionStock Price PredictionBERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights
This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to …
Financial AnalysisSentiment AnalysisStock PredictionStock Price Prediction+1Volatility forecasting using Deep Learning and sentiment analysis
Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning …
Deep LearningSentiment Analysis