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Labor Capital, Income Level and Expected Stock Returns

One of the risks investors should consider is how their human (or labor) capital correlates with equity risks. Given the risk of correlation, it is logical to conclude that human capital could have explanatory power when it comes to stock returns.

Sean Campbell, Stefanos Delikouras, Danling Jiang and George Korniotis contribute to the literature on this subject through their study, “The Human Capital That Matters: Expected Returns and High-Income Households,” which appears in the September 2016 issue of The Review of Financial Studies.

The authors proposed a new human capital asset pricing model (HCAPM) that separates the income risk of households at the top and bottom of the income distribution. They decided to decompose aggregate income into high and low income for two reasons.

First, human capital is not tradable and financial markets are incomplete. Second, aggregate wealth is primarily held by households concentrated at the top of the income distribution—households in the top 10% of the income distribution hold about 72% of risky assets.


The authors’ data set covered the period 1933 through 2011. Following is a summary of their findings:

  • The income growth of top income earners behaves differently from aggregate income growth. For example, during the recessions of 1981, 1991, 2001 and 2009, the top 1% of incomes dropped by 8%, 10%, 9% and 10%, whereas the aggregate income fell only by approximately 2%, 3%, 2% and 3%, respectively. This result suggests that high-income risk is significantly different from aggregate-income risk, and may be a better proxy for the income risk that matters for asset prices.
  • The high-income factor has a considerably higher volatility, 6.7% per year, than the low-income factor, which has a standard deviation of 3.4% per year.
  • The high-income factor is statistically significant, while the low-income factor is not. At successively higher-income households, the high-income factor becomes more significant. Specifically, the t-statistic (a measure of statistical significance) on the high-income factor rises from 1.9 for the 10% cutoff to 2.5 for the top 1% of incomes, suggesting that the most important high-income factor is the one related to the top 1% of the income distribution.
  • The HCAPM is able to price the cross section of expected returns as accurately as the Fama-French three-factor and four-factor models.
  • As a test of its robustness, the HCAPM remained significant in other asset pricing models that included the newer investment and profitability factors.
  • Stock-level regressions show that the high-income factor earns positive and statistically significant risk premiums of about 5-7% per year. Importantly, these premiums remain significant when controlled for size and book-to-market ratio.
  • The high-income factor is related to the value premium. This finding suggests that the value premium might be compensation for the inability of high-income households to perfectly hedge their income risk—providing support for a risk-based explanation for the value premium.

Hedging High-Income Risk

Using firm-level wage and profitability data from Compustat, the authors found that their high-income factor co-varies more with the per-employee wage growth of value firms than with the wage growth of growth firms, rendering value stocks a poor hedge for high-income risk.

The authors determined their findings “suggest that high-income investors may be disproportionately employed by value firms. Given that the average U.S. household cares about income hedging, it is possible that high-income investors may avoid investing in value firms.”

Thus, the authors concluded that their “findings suggest that the income risk of high-income investors is a unique source of macroeconomic risk that is not captured by the alternative factors.” Their conclusion is consistent with the conjecture made by Eugene Fama and Kenneth French in their paper “Multifactor Explanations of Asset Pricing Anomalies,” which appeared in the March 1996 issue of The Journal of Finance.

Fama and French attributed the value premium to investors whose human capital was correlated with value returns. Those investors shun value stocks, generating a premium for the lucky investors whose outside income is not so exposed, and who then buy value stocks.

It’s interesting to note that Campbell, Delikouras, Jiang and Korniotis’ findings are consistent with the findings of the study “Do Dividend Clienteles Exist? Evidence on Dividend Preferences of Retail Investors,” which appears in the June 2006 issue of The Journal of Finance. The authors, John Graham and Alok Kumar, found that the portfolio exposure of retail investors to the HML (high minus low) factor varies with income level.

For example, the exposure of low-income investors to the HML factor is twice that of the exposure of high-income investors (0.25 versus 0.12). Thus, the tilt of high-income investors away from value stocks may contribute to the value premium.

While the authors suggested high-income investors may be disproportionately employed by value firms, another explanation might be that these high-income investors own companies whose earnings co-vary with the earnings of value firms.


In summary, Campbell, Delikouras, Jiang and Korniotis demonstrate that high-income risk is priced in the cross section of expected returns, both at the portfolio and at the individual stock levels. In contrast, they found that the low-income factor is irrelevant for asset pricing.

It’s worth noting that the high-income risk they employed in the study also been used to try to explain what is called the “equity risk premium puzzle.” The equity risk premium has been so great that it implies an implausibly high level of investor risk aversion that is fundamentally incompatible with other branches of economics, particularly macroeconomics and financial economics.

One explanation for the puzzle is that a large percentage, if not the vast majority, of equities are owned by high net worth individuals. As net worth increases, the marginal utility of wealth decreases. While more wealth is always better than less, when individuals attain a level of wealth at which there is no longer a need to assume risk, only a very large risk premium might induce them to take it.

This commentary originally appeared December 2 on ETF.com

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The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.

© 2016, The BAM ALLIANCE

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Chief Research Officer

Larry Swedroe is Chief Research Officer for the BAM ALLIANCE.

Previously, Larry was vice chairman of Prudential Home Mortgage. Larry holds an MBA in finance and investment from NYU, and a bachelor’s degree in finance from Baruch College.

To help inform investors about the evidence-based investing approach, he was among the first authors to publish a book that explained evidence-based investing in layman’s terms — The Only Guide to a Winning Investment Strategy You’ll Ever Need. He has authored 15 more books:

What Wall Street Doesn’t Want You to Know (2001)
Rational Investing in Irrational Times (2002)
The Successful Investor Today (2003)
Wise Investing Made Simple (2007)
Wise Investing Made Simpler (2010)
The Quest for Alpha (2011)
Think, Act and Invest Like Warren Buffett (2012)
The Incredible Shrinking Alpha (2015)
Your Complete Guide to Factor-Based Investing (2016)
Reducing the Risk of Black Swans (2018)
Your Complete Guide to a Successful & Secure Retirement (2019)

He also co-authored four books: The Only Guide to a Winning Bond Strategy You’ll Ever Need (2006), The Only Guide to Alternative Investments You’ll Ever Need (2008), The Only Guide You’ll Ever Need for the Right Financial Plan (2010) and Investment Mistakes Even Smart Investors Make and How to Avoid Them (2012). Larry also writes blogs for MutualFunds.com and Index Investor Corner on ETF.com.

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