Economists have long been interested in being able to identify stock market bubbles in advance because not only are they associated with significant mispricing in financial markets (contrary to the efficient markets hypothesis), but mispricing leads to distortions in the distribution of capital (overinvestment). . Furthermore, bubbles are followed by crashes as the consequences of inefficient investment emerge.
We can define a stock bubble as a market that booms (rises more than 100% within two years) and then crashes (a pullback of at least 40% in two years). Stock market bubbles generally follow the same five stages, first identified by American economist Hyman Minsky:
- displacement: A major change or series of changes affects the way investors think about the markets.
- boom: Prices rise, attracting speculators who raise prices as word spreads.
- EUFOR: Investors are driven by excitement rather than rational justification for rising prices.
- Taking profit: Rising prices end up being too good to be true and the bubble is popped.
- Panic selling: Investors faced with declining margins and values seek to liquidate at any price.
Factors that can contribute to the formation of stock bubbles include:
- Low interest rates: When interest rates are low, investors seek higher returns, often leading to increased investment in stocks.
- Easy availability of credit: Abundant credit can fuel speculation as investors borrow to invest in stocks.
- Economic growth: Periods of strong economic expansion can create optimism and boost stock prices.
- Technological innovation: The emergence of new technologies can generate excitement and investment, sometimes leading to overvaluation.
- The psychology of the investor: Herd mentality, fear of missing out (FOMO) and overconfidence can all contribute to a bubble.
When a bubble bursts, the consequences can be severe:
- Economic decline: Sharp declines in stock prices can lead to reduced consumer spending and business investment, causing recessions with rising unemployment.
- Bankruptcy: Financial institutions that have invested heavily in the bubble may face bankruptcy.
- Loss of confidence: Public confidence in financial markets may be eroded.
Unfortunately, there is little evidence that financial economists have been able to identify bubbles in advance. Consider the following from 2013 NPR interview with Nobel Prize-winning economist Gene Fama.
Eugene F. Fame: The word “bubble” drives me crazy, frankly, because I don't think there's anything in the statistical evidence that says anyone can reliably predict when prices will go down…
NPR: What would prove to you that there were bubbles?
Eugene F. Fame: Empirical evidence.
NPR: Like?
Eugene F. Fame: Well, that you can show me that you can predict when these things come back in a reliable way.
Empirical evidence
To determine whether accounting information could ex-ante identify a stock market bubble Salman Arif and Edward Sul, study authors July 2024 “Does accounting information identify bubbles for Fama? Evidence from Accruals” examined industry-level investments in net operating asset accruals and stock returns for 49 countries worldwide. They measured investment using changes in net accruals of operating assets capturing net investment in both working capital accruals and long-term operating accruals.
They focused their analysis on the industry level, “consistent with historical evidence that bubbles are often an industry phenomenon.” Using a large sample of countries, they identified initial episodes in which value-weighted industry stock prices rose above 100% in terms of both raw and net market returns over the previous two years. Crashes were defined as withdrawals of at least 40% over the following two years. Since accounting data were only available starting in the early 1990s for non-US countries, they examined the periods between 1992 and 2020. This resulted in 18 US and 222 non-US rounds, for a total of 240 racing industries in 49 countries. Their tests focused on univariate return predictive regressions (a statistical model used to predict the future return of a financial asset based on information contained in a single past variable), sample return predictability, tests of multiple regression, the predictability of analysts' forecast errors, and the economic size associated with predictability. Here is a summary of their key findings:
Of the 240 total runs, they identified 114 crashes – 47.5% ended in crashes within the next two years. Of the 18 races in the US, 10, or approximately 56%, were subsequently dropped. China and Hong Kong experienced the most clashes at the international championship with eight each, closely followed by Brazil and India with seven each.
While the average two-year industry return in any given month was about 24.2% in the full panel, the average return was over 205% in the original sample. The initial sample exhibited higher average volatility, one-year changes in volatility and turnover, equity issuance, sales growth, CAPE ratio, price path convexity (acceleration), and NOA calculations. The increases were also associated with younger firms and lower book-to-market ratios.
Change in industry-level NOA calculations was a statistically significant predictor of crashes, with a coefficient of 0.687 and t statistic of 4.23. A one standard deviation increase in scores, all else equal, was associated with a 12.4% greater likelihood of a crash in the next two years. Accruals were significantly higher for price increases that subsequently collapsed relative to those that did not—a sharp increase in industry-wide stock prices did not unconditionally predict low future returns.
Industry-level NOA accruals were a strong negative predictor of industry stock returns. Growths in the lowest tertile of industry accruals experienced returns of 23.8% net of the risk-free rate averaged over the following two years, while returns in the highest tertile of industry-level accruals experienced returns of -8.1%. The difference of 31.9% was statistically significant. However, industry-level NOA accruals associated with price increases negatively predicted country-level aggregate returns, but industry-level accruals not associated with price increases generally did not predict country-level aggregate returns.
Accruals were positive from the r-squared sample when predicting each of the post-startup return measures.
Their findings led Arif and Sul to conclude: “Overall, these results suggest that computations identify bubbles in a statistically robust and economically meaningful way.” They added: “The predictive ability of accruals for industry shocks, returns and forecast errors is almost quintupled in the follow-up compared to the baseline. This indicates that our results are not the product of average calculations that generally predict future performance. Rather, our findings show that capital misallocation due to bubble-driven overinvestment has a significantly negative impact on future asset prices and corporate fundamentals.
Turning to the predictive bubble explanation of overinvestment, Arif and Sul noted: “Historical accounts of bubbles suggest that under the overinvestment explanation, managers are more likely to overinvest when sentiment is buoyant, earnings expectations are inflated, and financing it's easy to get. Consistent with this, we find a positive simultaneous correlation between accruals and two indicators of investor sentiment: Baker, Wurgler, and Yuan (2012) sentiment index at the country level as well as Dichev (2007) measure of investor capital market fund net flows calculated at the country-industry level.” They also found: “Higher accruals represent larger earnings shortfalls relative to EPS analyst expectations.”
Investor Relations
Arif and Sul's findings are consistent with an overinvestment channel—corporate investment increases when investor sentiment around new periods is greatest, yet such periods tend to be followed by price declines and disappointing corporate fundamentals. Thus, they have provided Fama with his research for empirical evidence – financial statement analysis can be used to detect and predict important sources of capital market inefficiency at the industry and market level with NOA accruals that identify bubbles and provide a signal principal of industry declines and aggregate-level returns. As the prices of many AI-related stocks soar, Arif and Sul's findings provide a warning. Arif and Sul also found that several other measures were significant predictors of a crash: “Volatility, Volatility1yrChange, IndustryAge, AgeTilt, PercentIssuers, BooktoMarket, Acceleration, and CAPE.” Forewarned is forearmed.
Larry Swedroe is the author or co-author of 18 books on investing, including his latest, Enrich your future: the keys to a successful investment