NEWS AND STOCK MARKET VOLATILITY: A GLOBAL SYSTEMATIC LITERATURE REVIEW

Abstract

Research into the relationship between the release of news and its influence on volatility in the stock market has become increasingly prominent in the past few years. A systematic review of the literature since 2015 using the PRISMA guidelines has examined all research articles published within this time frame concerning how various types of news (macroeconomic announcements, firm specific announcements, geopolitical events, social media), impact the degree of volatility in stock prices across the world. Findings suggest that news plays a material role in influencing volatility; however, the level of impact or volatility generated by news varies in both extent and timing based on type of news and market conditions. Macro-economic news and earnings announcements are typical causes of increases in volatility over the short term. On the other hand, the speed of dissemination of news through social media and news sentiment can cause longer term changes in volatility as well as behavioral biases. Additionally, during global economic crises such as the COVID-19 Pandemic and Geopolitical Conflict, the uncertainty associated with news causes larger increases in volatility and inter-market spillovers than during non-crisis periods. The paper outlines some of the methodological advances made in this body of research. Many new methodologies have been employed recently. These include the use of text-based sentiment measures, high frequency data and machine learning models, in combination with traditional econometric methodologies (such as GARCH). In total, the results of the research suggest that the processing of information in financial markets does not occur in an efficient manner, and therefore, the reaction to negative and/or unforeseen news by financial markets may be disproportionate and create volatile price movements. The current research provides a global perspective on the relationships between news, volatility and the behavior of investors at the national and international levels, it identifies areas of agreement among researchers and regions (developed and emerging markets); and, finally, it proposes possible directions for future research (the integration of artificial intelligence (AI) powered news analytics into volatility forecasting models).