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Quantifying the novelty of news with an entropy measure helps to predict stock market trends and macroeconomic outcomes for the subsequent year.
by Eunice Kim | February 2024
Quantifying the novelty of news with a measure called entropy helps to predict stock market trends and macroeconomic outcomes for the subsequent year.
Going beyond prior studies that linked news sentiment with short-term asset price changes, researchers explored how variations in the distribution of news text can predict negative market returns. From a dataset comprising over 1.6 million Reuters news articles spanning 27 years, the study used advanced natural language processing (NLP) techniques to create a measure of entropy that captures the degree of unusualness or change in news language.
A key discovery was the predictive ability in forecasting stock market trends. A rise in entropy was linked to a 3% drop in S&P 500 index returns over the following 12 months, surpassing other economic predictors in forecasting accuracy. In addition, assets correlated with high entropy tended to have a negative risk premium, suggesting that investors accept lower returns on assets that offset risks linked to changes in news language.
A change in the distribution of news text was also found to be a powerful forecaster for year-ahead macroeconomic outcomes, such as unemployment rates, market volatility, industrial production, inflation, interest rates and corporate earnings. However, despite entropy’s significant forecasting ability, markets might not fully adjust to the information entropy provides due to informational constraints or slow-moving institutional capital.
The research reveals entropy’s unique informational content and its capability to capture aspects of market dynamics unexplained by existing models and traditional indicators. The findings offer a more nuanced understanding of how news affects asset prices and market dynamics. They also open new avenues for research, including exploring advancements in NLP to analyze news unusualness, as well as the potential for applying the concept of entropy to individual stock analysis.
Source: “New News is Bad News,” by Paul Glasserman, Harry Mamaysky and Jimmy Qin; SSRN, August 2023.
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