What is the best strategy when investor detects a bubble? Step outside, short it, or ride it? Academic research shows it is best to ride as excess returns which investor gains during bubble riding more than offsets risk of a subsequent crash (which always follows).
However, it is essential to be rational and to stop riding bubble when it starts to pop and do not fall in love with the particular industry. A bubble is identified as a structural break in the industry’s alpha return compared to the overall market return. A small part of the industry‘s return could be explained by a momentum factor, but the strategy of riding bubbles is distinct from momentum, and it, therefore, offers good diversification benefits.
A system’s validity seems strong as research shows that the industry bubbles are a different phenomenon than industry momentum. Since bubbles end with large negative abnormal returns, they cannot be explained by an underreaction to the good news.
Industry bubbles do not result from a misspecification of the asset pricing models used in research study: the bubbles cannot be explained by an omitted risk factor, an omitted structural break, or by a combination of factors, therefore, the trading strategy could be used as an independent add-on to the portfolio of strategies with potential for diversification.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, calculated as average strategy abnormal return (8% from table 4 – using CAPM model) plus average nominal equity performance (~10%), average raw return for industry in bubble is ~30% per annum
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
Notes to Number of Traded Instruments
average number of industries held in one moment
Moderately complex strategy
Notes to Maximum drawdown
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of equity industry funds (or ETFs) which are proxy for equity industry indexes. Investor uses 10 years of past data to calculate industry’s alpha based on CAPM model (from the regression model industry_return = alpha + beta*market return, it is possible to use alternative models like the Fama/French 3 factor model). A bubble in an industry is detected if the industry’s alpha is statistically significant (source academic paper uses 97,5% significance threshold, but it is possible to use other values). Investor is long in each industry experiencing a bubble by applying 1/N rule (investment is divided equally between industries in bubble). If no bubble is detected then he/she makes no investment. Data examination, alpha calculation and portfolio rebalancing is done on monthly basis.
Guenster, Jacobsen, Kole: Riding Bubbles
We empirically analyze rational investors’ optimal response to asset price bubbles. We define bubbles as a sudden acceleration of price growth beyond the growth in fundamental value given by an asset pricing model. Our new bubble detection method requires only a limited time-series of historical returns. We apply our method to US industries and find strong statistical and economic support for the riding bubbles hypothesis: when an investor detects a bubble, her optimal portfolio weight increases significantly. A dynamic riding bubble strategy that uses only real-time information earns abnormal annual returns of 3% to 8%.
Milunovich, Shi, Tan: Bubble Detection and Sector Trading in Real Time
We conduct a pseudo real-time analysis of the existence and severity of speculative bubbles in eleven US sectors over the period 1973-2015. Based on the real-time bubble signals, a trading strategy is constructed which switches funds between the market index and those industry sectors that exhibit bubble dynamics. Our strategy generates highest after-transaction-cost return and Sharpe ratio, and first-order stochastically dominates three other investments (including two alternative active strategies as well as the buy-and-hold investment in the market index). Subsample analysis and specification checks confirm the robustness of the reported findings.