Can reading the news make you richer?
Researchers have uncovered a novel way to forecast stock market volatility using daily business news. Business news can do more than report on financial markets; it can predict where they’re headed. That’s the finding from a new study by University of Auckland finance lecturer Dr Justin J. Case and Queensland University of Technology’s Professor Adam Clements, who show that utilising business news articles, specifically those published in The Wall Street Journal, can more accurately forecast stock market volatility than other commonly used methods. “Volatility is a common proxy for financial risk,” says Dr Case. “By accurately forecasting this risk, investors can take strategic steps to protect their investments before market shifts occur.” Using more than 1.1 million Wall Street Journal articles published between January 2000 and December 2022, the researchers analysed the language used in business reporting and linked it to fluctuations in the S&P 500 – the world’s most-watched equities index. Their study shows that news text offers a forward-looking, real-time lens on market conditions, delivering more accurate signals about risk than the retrospective data typically used in economic forecasting. The researchers applied a machine learning algorithm to news articles, sorting the text into topics and analysing these alongside high-frequency data on the S&P 500 index. “We’re looking at the world’s biggest equities market, and the biggest business newspaper in the US, and asking whether the news explains stock market volatility,” says Case. “We find that news coverage is strongly related to stock market volatility movements. And by analysing business news articles, we can identify both the topics and specific events influencing stock market volatility.” Additionally, the researchers found incorporating their news-based measures into benchmark volatility forecasting models reduced forecast errors by over 40 percent at the monthly horizon. They also found significant reductions in forecast errors at weekly […]