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Predicting Financial Market Bubbles and Crises in Real-time
Professor Robin Greenwood notes that faculty members across Harvard have long been exploring the behavioral perspective on financial market bubbles and financial crises. Five years ago, a group formed to examine key issues in greater depth, which led to the creation of the Behavioral and Financial Stability Project. Researchers have conducted groundbreaking research on bank capital and liquidity management, the nature of bank runs in the modern financial system, and the unprecedented growth of the financial sector prior to the crisis. They have also been among the first to develop comprehensive historical data on the incidence and length of financial and banking crises.
KEY THEMES
Greenwood conducted research into financial bubbles, called “Bubbles for Fama,” with colleagues Andrei Shleifer and Yang You. They sought to evaluate the claim by Nobel Laureate Eugene Fama that stock markets do not exhibit price bubbles.
Greenwood and his colleagues used stock return data gathered from a variety of US industries and a gamut of international stock market sectors. They analyzed 40 episodes since 1928 in which stock prices of a US industry have increased over 100 percent in raw and net of market returns over the past two years to determine the likelihood of a crash (defined as a 40 percent drawdown) after a large price run-up. They also collected all of the characteristics of price run-ups that may lead to a crash, such as volatility of returns, turnover, age of the firm, and the percentage of firms that issued stock during a run-up, among other factors.
Greenwood found that, while Fama was correct in asserting that sharp price increases do not predict lower returns going forward, these increases do predict substantial heightened probability of a crash. Simple attributes related to the price run up can help predict both the crash probability and future returns.
FUTURE SCOPE
Greenwood is also conducting research that looks at predicting financial crises, which harnesses data compiled in the past decade by a variety of researchers. It reflects information from 42 countries spanning the years 1947 to 2017. What he found was a “killer predictor”—the coincidence of price and quantity growth in which either housing prices are high and there is great housing credit growth, or corporate equity prices are really high, or corporate credit spreads are down.
Ultimately, Greenwood explains, the variables that predict crises have lots of lead time, suggesting that there is enough time for policymakers to respond.
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