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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. Still, the strategy of riding bubbles is distinct from momentum, and it, therefore, offers good diversification benefits.
Fundamental reason
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.
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Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Notes to Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Regions
Simple trading strategy
The investment universe consists of equity industry funds (or ETFs), which are proxy for equity industry indexes. An investor uses ten years of past data to calculate the industry’s alpha based on the 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 (academic source paper uses a 97,5% significance threshold, but it is possible to use other values). The investor is long in each industry experiencing a bubble by applying 1/N rule (investment is divided equally between industries in a bubble). If no bubble is detected, then he/she makes no investment. Data examination, alpha calculation, and portfolio rebalancing is done monthly.
Hedge for stocks during bear markets
No – The selected strategy is long-only. As such has a strong exposition to equity market risk, therefore it can't be used as a hedge/diversification during the time of market crisis.
Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)
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Source paper
Guenster, Jacobsen, Kole: Riding Bubbles
Abstract: 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%.
Other papers
Milunovich, Shi, Tan: Bubble Detection and Sector Trading in Real Time
Abstract: 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.
Arnott, Robert D. and Clements, Mark and Kalesnik, Vitali and Linnainmaa, Juhani T.: Factor Momentum
Abstract: Past industry returns predict future industry returns, and this predictability is at its strongest at the one-month horizon. We show that the cross section of factor returns shares this property and that industry momentum stems from factor momentum. Factor momentum transmits into the cross section of industry returns through variation in industries’ factor loadings. We show that momentum in "systematic industries," mimicking portfolios built from factors, subsumes industry momentum as does momentum in industry-neutral factors. Industry momentum is therefore a byproduct of factor momentum, not vice versa. Momentum concentrates in its entirety in the first few highest-eigenvalue factors.
Zarattini, Carlo and Antonacci, Gary: A Century of Profitable Industry Trends
Abstract: This paper evaluates the profitability of an industry-based long-only trend-following portfolio. Utilizing 48 industry portfolios from 1926 to 2024, our analysis explores the model's profitability over a century, highlighting its adaptability and effectiveness across diverse market epochs. We assess the overall profitability of the model and examine the distribution of long-term returns and associated risks. Our analysis includes the impact of individual industry contributions on overall portfolio performance, focusing on the frequency and average profitability of trades at both the portfolio and industry levels. The Timing Industry strategy achieves an average annual return of 18.2% with an annual volatility of 12.6%, resulting in a Sharpe Ratio of 1.39, compared to the US equity market's 9.7% return, 17.1% volatility, and 0.63 Sharpe Ratio. The model's outperformance is underscored by an annualized alpha of 10.9%, with the timing strategy reducing drawdown by almost 60% compared to a passive long exposure. Further investigations reveal the active strategy's ability to fully participate during market upswings while significantly limiting exposure during downturns. In the final section, we introduce 31 sector ETFs provided by State Street Global Advisors and backtest the same trading methodology over the last 20 years. The ETFs successfully replicate the model's exposure and returns. We also assess the impact of commissions and slippage, demonstrating that the active timing strategy remains largely profitable even with high trading costs.This paper evaluates the profitability of an industry-based long-only trend-following portfolio. Utilizing 48 industry portfolios from 1926 to 2024, our analysis explores the model's profitability over a century, highlighting its adaptability and effectiveness across diverse market epochs. We assess the overall profitability of the model and examine the distribution of long-term returns and associated risks. Our analysis includes the impact of individual industry contributions on overall portfolio performance, focusing on the frequency and average profitability of trades at both the portfolio and industry levels. The Timing Industry strategy achieves an average annual return of 18.2% with an annual volatility of 12.6%, resulting in a Sharpe Ratio of 1.39, compared to the US equity market's 9.7% return, 17.1% volatility, and 0.63 Sharpe Ratio. The model's outperformance is underscored by an annualized alpha of 10.9%, with the timing strategy reducing drawdown by almost 60% compared to a passive long exposure. Further investigations reveal the active strategy's ability to fully participate during market upswings while significantly limiting exposure during downturns. In the final section, we introduce 31 sector ETFs provided by State Street Global Advisors and backtest the same trading methodology over the last 20 years. The ETFs successfully replicate the model's exposure and returns. We also assess the impact of commissions and slippage, demonstrating that the active timing strategy remains largely profitable even with high trading costs.