Machine learning sentiment analysis,COVID-19 news and stock market reactions

The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.     

The power of text-based indicators in forecasting Italian economic activity

Can we use newspaper articles to forecast economic activity? Our answer is yes; and, to this end, we propose a high-frequency Text-based Economic Sentiment Index (TESI) and a Text-based Economic Policy Uncertainty (TEPU) for Italy. Novel survey evidence regarding Italian firms and households supports the rationale behind studying text data for the purposes of forecasting. Such indices are extracted from approximately 1.5 million articles from 4 popular newspapers, using a novel Italian economic dictionary with valence shifters. The TESI and TEPU can be updated daily for the whole economy and for specific sectors or economic topics. To test the predictive power of our indicators, we propose two forecasting exercises. Firstly, we use Bayesian Model Averaging (BMA) techniques to show that our monthly text-based indicators greatly reduce the uncertainty surrounding the short-term predictions of the main macroeconomic aggregates, especially during recessions. Secondly, we employ these indices in a weekly GDP tracker, achieving sizeable gains in forecasting accuracy, both in normal and turbulent times.     

Does air pollution influence music sentiment? Measuring music sentiment by machine learning

Air pollution has imposed significant negative effects on individuals’ well-being, including citizens’ sentiment levels. To test this claim, we investigate the impact of air pollution on Chinese urbanites’ music sentiments. The analysis is based on a unique dataset of high-frequency music consumption records from a music platform in China from October 13th, 2019 to January 7th, 2020. Using machine learning algorithms, songs on this platform are divided into cheerful songs, melancholy songs and other categories, by which a music sentiment index (MSI) is generated at city-daily level. By matching MSI and daily air quality, this study finds that the MSI declines during highly polluted days, indicating that: on highly polluted days, citizens tend to enjoy melancholy songs over cheerful ones. In addition, this effect becomes more remarkable when the Air Quality Index (AQI) score is above 200, a critical point for “heavily polluted” and “severely polluted” days.