GREENSBORO – Industrial electricity use is a unique, accurate and significant predictor of future stock market fluctuations, according to a researcher at the University of North Carolina at Greensboro (UNCG).
“Industrial electricity usage is measured very accurately and is difficult to manipulate. Year-over-year industrial electricity usage growth rate has a strong and significant predictive power for future stock market excess returns in horizons ranging from one month to up to one year,” according to the research paper authored by Dayong Huang, Ph.D, of the Bryan School of Business, UNCG, an expert in stock market indicators, and his colleagues, Zhia DA, of the Mendoza College of Business, University of Notre Dame, and Hayong Yun, of the Eli Broad College of Business, Michigan State University.
Unlike commercial and residential electricity usage, industrial electricity usage is less affected by weather conditions. Most modern industrial production activities involve the use of electricity, and because of technological limitations, electricity cannot easily be stored. As a result, industrial electricity usage can be used to track production and output in real time. And because electric utilities are highly regulated and subject to extensive disclosure requirements, electricity usage data are accurately measured and reported.
Huang said that this method of prediction outperforms the standard variables, including the output gap and the growth rate of the Gross Domestic Product. “Looking at industrial electrical usage tells a bigger picture of what we should be doing in the stock market. When there is high industrial electrical growth, the economy is booming. If the economy is up, the stock market is up – but evidence now shows that the market will revert back to its norm. It will correct itself.”
High industrial electricity usage today predicts low stock returns in the future, consistent with a countercyclical risk premium. Industrial electricity usage tracks the output of the most cyclical sectors,” the paper reports.
“This is just one variable for money managers to look at within a combination — but it’s a big one. It helps with market timing — when to allocate money in, and when to take it out. If the variable is too high, it’s time to consider taking money out. Usually, they are using historical data to make decisions. The present tells us about the past, so it’s critical to look at industrial electricity usage,” he added.
Huang’s paper has won the Sharpe Award for the best paper in the 2017 volume of the Journal of Financial and Quantitative Analysis.
