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  • PRACTITIONER’S BRIEF:  Turn Seemingly Irrelevant Beta Into A Potentially Powerful Predictive Tool Using The Implied Cost Of Capital

    15 Jun 2017

    Still widely considered as bedrock financial theory yet often criticized, CAPM is a simple, formal methodology to price securities. Its fundamental idea of systematic risk is reflected in all modern asset-pricing theory. Because math-based models are built on sets of assumptions (e.g., “markets are frictionless and efficient”) that may not always reflect reality, all models are, to some degree, suspect. Such models are still relevant, however, in trying to gauge the differences in risk premiums of individual stocks. CAPM dictates that beta is the solely relevant measurement of a given stock’s risk—its co-movement with the market—relative to the movements of the market as a whole.
    The foundation for asset-pricing theories such as CAPM is the Efficient Market (EM) hypothesis, which, over the years, has taken on water like the hull of a leaky boat. Skeptics, have poked holes in the EM hypothesis and cast CAPM in a contradictory light. Numerous studies have shown that beta, which came from CAPM, loses relevance when closely scrutinized. Shi and Xu are careful to set the stage by conceding from the outset that beta in and of itself exhibits weak power in explaining return differences of individual stocks. However, they also seek the source of this weakness: Could there be a glitch with standard “cross-sectional regression tests” that shows, when comparing one stock to another stock, statistical beta estimates to be overly noisy and/or limited measures of expected return? Might these backtests have a fundamental design flaw? And if so, is there a better proxy for expected return?
    With a long-horizon investing approach in mind, Shi and Xu assert that most beta-bashing research shares a common denominator: the use of short-term testing methods to demonstrate the inconsistency of beta patterns. A common example, they find, is inputting the next month’s realized return as a proxy for the (retroactively assigned) expected return. Shi and Xu believe that CAPM may be better suited to capturing the risk-return relation in the long-run. To refashion CAPM to achieve this goal, they create a new way to model longer-horizon expected returns in their tests by having the future Implied Cost of Capital (ICC) play the part of expected return. “ICC is in the same spirit as internal rate of return,” they write. “It is perceived average return over time even when actual future expected returns might be time varying.” To test their theory, the authors examine all U.S. public companies on major exchanges (stripping out financial companies due to their overabundance of leverage, which skews the model) and pulling together, as a proxy for future cash flows, a mountain of analyst earnings forecast data (annualized). They supplement those data with long-term growth-rate assumptions for certain industries, forming a 710,840-variable dataset. The key output is the estimated ICC, which is calculated by asking what expected returns have investors used to discount future cash flows (approximated in part by the analysts’ earnings forecasts) to observe the current equity prices. Although the use of actual future returns might seem like a reasonable means of representing expected returns, an approach in which ICC is used as a proxy for expected return is, the authors insist, a better estimate when running models that aim to drill down into the explanatory power of beta.
    Despite abundant current evidence that seems to reject the explanatory power of the market beta in expected returns, Shi and Xu assert that when tests are run with ICC as a proxy for expected return, the weaknesses seen by others are not necessarily present. They find that the future implied cost of capital is “both positively and significantly related to the conventional beta estimate,” implying that beta could still explain future cross-sectional expected return differences over a longer horizon. In other words, the conventional estimate of the market beta risk might be a good measure of the long-term market risk. As an example of how long-term expected return measures can be useful, Shi and Xu show a connection between individual stocks with high levels of uncertainty (i.e., an additional dimension of risk surrounding them as measured by analyst forecast dispersion) and long term expected returns as approximated by future ICC. Stocks with large dispersion—when analysts’ forecasts do not agree—tend to have high long-term expected return, suggesting that investors are compensated for taking on the uncertainty risk. Thus, not only is beta—or rather, long-term beta—of use when trying to forecast expected return, but it may bring with it unexpected positive results.
    CAPM may not be perfect, but it is intuitive, easy to apply, and powerful in practice. In fact, Shi and Xu’s research justifies the continued use by industry members of models such as Fama and French’s three-factor model, which includes the market factor as its most important factor. In addition, their results carry important implications across finance. For private equity firms and investment bankers assessing the value of a young company, what matters most? The answer (or at least what should be the answer) is the company’s growth potential and long-term expected return. The market risk beta value attached to the company is the risk associated with the company for many years to come. For portfolio managers struggling to rationalize, perhaps even to themselves, that a long-term conviction is worth holding, running numbers through a long-term prism can bring peace of mind and justify additional allocations. Shrewd investors will, of course, not just pay attention to a manager’s absolute performance, but also to their own level of beta risk. In addition, they will invest in strategies carrying levels of beta risk with which they can feel most comfortable.


    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.