Tracking market patterns with yesterday’s headlines

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New research uses an historic approach to predict times of stock market volatility and the resulting returns: back issues of The Wall Street Journal. Researchers relied on machine learning, big-data techniques to construct a text-based measure of market uncertainty, using the front pages of The Wall Street Journal going back to 1890.

Asaf Manela, assistant professor of finance at Olin, along with Alan Moreira, assistant professor of finance at the Yale School of Management, relied on machine learning, big-data techniques to construct a text-based measure of market uncertainty, using the front pages of The Wall Street Journal going back to 1890. Analyzing the words in those headlines and stories allowed measurement of a news implied volatility index (NVIX).

“If I let you read the front page of The Wall Street Journal on any given day, you can probably tell a lot about how much people are worried about the future,” Manela said. “We used this big body of text to construct a news-implied volatility index going back more than a century. By looking at that index, we can ask things like ‘What were people worried about at different points in time?’ ”

The research finds that war and government policy-related concerns are most important to stock market investors, and are responsible for the bulk of the variation in average returns.

Read the source article at ScienceDaily