Building News Measures from Textual Data and an Application to Volatility Forecasting

Caporin, Massimiliano; Poli, Francesco

doi:10.3390/econometrics5030035

Cited by 33 publications

(20 citation statements)

References 105 publications

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“…These investor sentiment data may reveal the effects of market shocks on the stock market. Preis 2010, Bordino 2012Vlastakis 2012, Dimpfl and Jank 2016, and Caporin, 2017 examined the relationship between the number of daily queries and trading volume and volatility for a given stock. The results suggest that there is a circular relationship between the search volume of Google Trends and the stock volume, returns, liquidity and volatility.…”

Section: Introductionmentioning

confidence: 99%

The impact of Baidu Index sentiment on the volatility of China's stock markets

Fang

Gözgör

Lau

et al. 2020

Finance Research Letters

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Nowadays, search engine use increasingly reflects investor sentiment, which affects the return on the stock market. In this article, we examine the relationship between Baidu Index sentiment and China's stock market returns. In two different GARCH models, the benchmark model and a Baidu Index extended model, the one-step forward method is used to predict the return of stock market. The study finds that Baidu Index − search volume is a valid indicator for forecasting volatility in China's stock market. The Baidu Index extended model performs better than the benchmark model, both in periods of high volatility and periods of low volatility. These results are quite robust in Shanghai Stock Exchange, Shenzhen Stock Exchange, CSI 300 Index, and CSI 100 Index. This study shows that the investor sentiment reflected in the Baidu Index can be used as a good early warning indicator of China's stock market.

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Section: Introductionmentioning

confidence: 99%

The impact of Baidu Index sentiment on the volatility of China's stock markets

Fang

Gözgör

Lau

et al. 2020

Finance Research Letters

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“…Similarly, Caporin et al [4] find that news-related variables can improve volatility prediction. Certain news topics such earning announcements and upgrades/downgrades are more relevant than other news variables in predicting market volatility.…”

Section: Sentiment Anal and Volatilitymentioning

confidence: 90%

A Sentiment Analysis Approach to the Prediction of Market Volatility

Deveikyte¹,

Geman²,

Piccari³

et al. 2020

Preprint

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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%.

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“…A noteworthy amount of finance research has pointed out the impact of sentiment expressed through various corpora on stock returns and trading volume, including Heston and Sinha (2017), Jegadeesh and Wu (2013), Tetlock, Saar-Tsechansky, and Macskassy (2008), and Antweiler and Frank (2004). Caporin and Poli (2017) create lexicon-based news measures to improve daily realized volatility forecasts. Manela and Moreira (2017) explicitly construct a news-based measure closely related to the CBOE Volatility Index (VIX) and a good proxy for uncertainty.…”

Section: Application To Predicting the Cboe Volatility Indexmentioning

confidence: 99%

The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment

Ardia¹,

Bluteau²,

Borms³

et al. 2021

J. Stat. Soft.

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We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.

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Building News Measures from Textual Data and an Application to Volatility Forecasting

Cited by 33 publications

References 105 publications

The impact of Baidu Index sentiment on the volatility of China's stock markets

The impact of Baidu Index sentiment on the volatility of China's stock markets

A Sentiment Analysis Approach to the Prediction of Market Volatility

The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment

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