THE HISTORY OF FINANCE: AN EYEWITNESS ACCOUNT

In this account of the evolution of finance theory, the “father of modern finance” uses the series of Nobel Prizes awarded finance scholars in the 1990s as the organizing principle for a discus‐sion of the major developments of the past 50 years. Starting with Harry Markowitz's 1952 Journal of Finance paper on “Portfolio Selection,” which provided the mean‐variance frame‐work that underlies modern portfolio theory (and for which Markowitz re‐ceived the Nobel Prize in 1990), the paper moves on to consider the Capi‐tal Asset Pricing Model, efficient mar‐ket theory, and the M & M irrelevance propositions. In describing these ad‐vances, Miller's major emphasis falls on the “tension” between the two main streams in finance scholarship: (1) the Business School (or “micro normative”) approach, which focuses on investors ‘attempts to maximize returns and cor‐porate managers’ efforts to maximize shareholder value, while taking the prices of securities in the market as given; and (2) the Economics Depart‐ment (or “macro normative”) approach, which assumes a “world of micro optimizers” and deduces from that assumption how the market prices actually evolve. The tension between the two ap‐proaches is resolved, and the two streams converge, in the final episode of Miller's history–the breakthrough in option pricing accomplished by Fischer Black, Myron Scholes, and Rob‐ert Merton in the early 1970s (for which Merton and Scholes were awarded the Nobel Prize in 1998, “with the late Fischer Black everywhere ac‐knowledged as the third pivotal fig‐ure”). As Miller says, the Black‐Scholes option pricing model and its many successors “mean that, for the first time in its close to 50‐year history, the field of finance can be built, or…rebuilt, on the basis of ‘observable’ magnitudes.” That option values can be calculated (almost entirely) with observable vari‐ables has made possible the spectacu‐lar growth in financial engineering, a highly lucrative activity where the prac‐tice of finance has come closest to attaining the precision of a hard sci‐ence. Option pricing has also helped give rise to a relatively new field called “real options” that promises to revolu‐tionize corporate strategy and capital budgeting. But if the practical applications of option pricing are impressive, the op‐portunities for further extensions of the theory by the “macro normative” wing of the profession are “vast,” in‐cluding the prospect of viewing all securities as options. Thus, it comes as no surprise that when Miller asks in closing, “What would I specialize in if I were starting over and entering the field today?,” the answer is: “At the risk of sounding like the character in ‘The Graduate,’ I reduce my advice to a single word: options.”     

Does Money Explain Asset Returns? Theory and Empirical Analysis

A cash-in-advance model of a monetary economy is used to derive a money-based CAPM (M-CAPM), which allows us to implement tests of asset pricing restrictions without consumption data. A test as in Fama and MacBeth of the model suggests that the money betas have some explanatory power for the cross-sectional variation of expected returns; however, the model is rejected using conditional information. Consistent with our predictions, estimates of the curvature parameter are lower than those of the consumption CAPM (C-CAPM) and pricing errors of the M-CAPM tend to be smaller than those of the C-CAPM.     

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors‐in‐variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and the Fama and French (1993) three‐factor model. We find that time‐variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three‐factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm‐specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three‐factor model, the unconditional CAPM, and the unconditional three‐factor model.     

It takes a model to beat a model: Volatility bounds

Models like the CAPM and Fama–French three-factor models are commonly used as benchmarks for calculating cost of capital and evaluating portfolio performance, despite the empirical evidence to reject them. For many practical purposes, “it takes a model to beat a model.” In this paper we derive restrictions on models that could “beat” a bench-mark model but might still be misspecified. In these “takes-a-model-to-beat-a-model” (TMBM) bounds, model A beats model B if model A's quadratic form of pricing errors is smaller. The bounds generalize the Hansen–Jagannathan bound and distance measure. We use the TMBM bounds to evaluate various linear factor models and consumption-based models. The failure of the power utility model is much less extreme when it is compared with the CAPM and Fama–French model. For reasonable utility curvature, the Ferson–Constantinides model and Epstein–Zin model perform best among the consumption-based models, beating the model of Campbell and Cochrane, in which model the value of the persistence parameter that matches the time-series properties of aggregate stock market returns seems too low for cross-sectional asset pricing.     

Do relative leverage and relative distress really explain size and book-to-market anomalies?

In a capital asset pricing model (CAPM) framework, Ferguson and Shockley [2003. Equilibrium “anomalies”. Journal of Finance 58, 2549–2580] propose two factors constructed on relative leverage and relative distress, and show that the two factors subsume Fama and French's [1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56] factors constructed on size and book-to-market (BM) in explaining the cross-sectional average returns of the 25 size-BM portfolios. Based on tests on individual securities, we find that all factors fail to fully explain the common asset-pricing anomalies. In the spirit of Merton's [1973. An intertemporal capital asset pricing model. Econometrica 41, 867–887] intertemporal CAPM, we propose an augmented five-factor model, which incorporates Ferguson and Shockley's [2003. Equilibrium “anomalies”. Journal of Finance 58, 2549–2580] factors into the Fama–French three-factor model. The empirical results show that a simple conditional version of the augmented model is able to explain most well-known asset-pricing anomalies.     

Implementing Fischer Black's Simple Discounting Rule

Corporate managers typically estimate the value of capital projects by discounting the project's expected future net cash flows at the cost of capital. The capital asset pricing model (CAPM) is generally used to estimate that cost. But, as anyone who has worked on the finance or business development staff of a public company can attest, there are major challenges in applying the CAPM, including largely unresolved questions about what constitutes the “market portfolio,” how to estimate market risk premiums, and how to estimate the betas of projects. In a short article published in Financial Management in 1988, Fischer Black proposed a valuation “discounting rule” that avoids all these problems—one that involves discounting a relatively certain (as opposed to an expected or average) level of operating cash flows at the risk-free rate. But Black's article does not address the question of how to calculate these “certainty equivalent” or “conditional” cash flows. In this article, the authors propose a way of implementing Black's rule that involves estimating the “conditional” cash flows in a three-step procedure:

• • Find a benchmark security that correlates with the project's cash flows;
• • Estimate the percentiles of the distribution in which the benchmark return equals the risk-free rate over different investment horizons;
• • Use information from corporate managers to assess the cash flows that define the same percentiles in the cash flow distributions.

As the authors point out, the virtue of Black's rule is that it shifts the focus of the analyst away from the assessment of discount factors and puts it squarely on the more challenging, and arguably more relevant, problem of estimating the project's cash flows.     

A Tribute to Joel Stern

The co‐founder of corporate finance consulting firm Stern Stewart and Co. pays tribute to Joel Stern, the well‐known popularizer of “modern corporate finance” and consultant to hundreds of companies worldwide who died on May 21, 2019. During a 45‐year career that spanned his graduation from the University of Chicago's School of Business in 1964, a 14‐year stint at the Chase Manhattan Bank, and the formation of Stern Stewart (and its successor, Stern Value Management), Stern traveled the world over, always eager to address and make converts among legions of corporate executives, board members, and MBA students. One key to his success was a passionate reverence for the academic scholars who developed modern finance. Joel's translation of the Miller‐Modigliani valuation model into a practical framework for evaluating corporate performance gained a following among a generation or two of corporate leaders, leading ultimately to the development of EVA, or Economic Value Added, a practical framework for value‐based financial management.     

Downside Risk in Practice

Although investors associate risk with negative outcomes and downside fluctuations, modern portfolio theory does not. For investors, volatility per se is not necessarily bad; volatility below a benchmark is. A stock that magnifies the market's fluctuations is not necessarily bad; one that magnifies the market's downside swings is. Even Harry Mar‐kowitz, the father of modern portfolio theory, viewed downside risk as a better way to assess risk than the “mean‐variance” framework that he ultimately proposed and that has since become the standard. This article highlights the shortcomings of traditional measures of risk (the standard deviation and beta), introduces the concept of downside risk, and discusses two measures of it—the “semideviation” and “downside beta.” It also discusses the use of such measures in asset pricing models to estimate required returns on equity. Data from a few well‐known companies are used to illustrate that the cost of equity based on downside risk can be substantially different from that based on the CAPM. The article concludes with a brief discussion of risk‐adjusted returns and a comparison of the traditional method of calculating such returns with both the Sharpe ratio and its counterpart in a downside risk framework, the Sortino ratio. The appendix demonstrates how to calculate these risk measures in Excel.     

Equilibrium "Anomalies"

Many empirical “anomalies” are actually consistent with the single beta capital asset pricing model if the empiricist utilizes an equity‐only proxy for the true market portfolio. Equity betas estimated against this particular inefficient proxy will be understated, with the error increasing with the firm's leverage. Thus, firm‐specific variables that correlate with leverage (such as book‐to‐market and size) will appear to explain returns after controlling for proxy beta simply because they capture the missing beta risk. Loadings on portfolios formed on relative leverage and relative distress completely subsume the powers of the Fama and French (1993) returns to small minus big market capitalization (SMB) portfolios and returns to high minus low book‐to‐market (HML) portfolios factors in explaining cross‐sectional returns.     

Do Large Blockholders Reduce Risk?

The conventional assumption in the asset pricing literature is that the identity of a company's owners is largely irrelevant, but studies of companies with “blockholders”—shareholders with large positions in a particular company—provide grounds for questioning this assumption. Unlike the well‐diversified investors of modern portfolio theory, blockholders have strong incentives to monitor corporate performance and, when necessary, to exert control over ineffective managements and boards. The findings of many studies support the idea that blockholders have a positive effect on rates of return. The authors of this article report the findings of their recent investigation of whether blockholders might also have a positive effect on shareholder value by reducing the risk of the companies in which their holdings are concentrated. After distinguishing between companies with individual as opposed to corporate blockholders, and those with one share, one vote as opposed to those with dual‐class shares, the authors find that ownership of large positions by individuals—but not corporations—was associated with lower systematic risk (when using both Fama‐French multiple factor and CAPM models). At the same time, they find that the firm‐specific risk of such companies was higher, but “biased” toward positive outcomes—that is, smaller downsides with larger upsides. What's more, this upward shift in performance and risk‐profile was achieved at least partly through increases in productivity as reflected in higher profit margins, profitability, profit per employee, and operating leverage, and lower costs of goods sold, SGA, and cash holdings. By contrast, in the case of blockholders in companies with dual‐class share structures, all of these positive associations with blockholders were either significantly weaker, or reversed. That is, whereas the presence of individual blockholders appears to increase productivity and value under a one share, one vote governance regime, blockholders in companies with dual‐class structures were associated with higher systematic risk and reduced productivity and value.     

MERTON MILLER'S CONTRIBUTIONS TO MODERN FINANCE

Winner of the 1990 Nobel Prize in Economics, and widely regarded as the “father of modern finance,” the University of Chicago's Merton Miller died last June at age 77. This article attempts to sum up Miller's career in terms of a single governing principle: the role of arbitrage in ensuring the “efficiency” of financial markets and, more generally, the effectiveness of such markets in promoting economic growth and creating social wealth. Starting with the formulation of Proposition I (also known as the capital structure irrelevance proposition) with Franco Modigliani in 1958, Miller's research over the next 40 years is seen as applying—with remarkable clarity and consistency—the principle of arbitrage to the study of many aspects of financial markets. Miller's main accomplishment, according to the author, is to have made arbitrage arguments the cornerstone of modern finance. The arbitrage proof of Proposition I introduced a new standard in finance—namely, that any finding in financial research deserving serious consideration must have the critical property that it cannot represent opportunities for riskless profit by investors. And the article goes on to show that arbitrage is a constant theme in Miller's writings, from his work in corporate finance to his later studies of financial innovation, derivatives markets, and financial crashes and crises. Having started and presided over the transformation of financial studies from a “glorified apprenticeship system” into a scientific discipline, Miller devoted much of the last 15 years of his life to a different, though clearly related undertaking: the defense of financial markets against the attacks of politicians and regulators, as well as businessmen intent on stifling competition (including hostile takeovers). Whether it was the alleged role of the stock index futures markets in the 1987 market crash, the claims of “overleveraging” in the LBOs of the '80s, or the derivatives fiascos in the mid‐'90s, Miller was there to provide careful economic analysis of the problems. In the early '90s, he explained why the “myopia” of the U.S. stock market was likely to cause far fewer problems than the “hyperopia” induced by regulatory distortions of the Japanese market. And in one of his last speeches, Miller showed that the primary cause of the recent Asian crisis was not “too much reliance on financial markets,” as claimed by politicians and the popular press, but “too little”—in particular, the heavy dependence on bank financing (particularly state‐owned banks) and the failure to develop alternative sources of capital that continue to depress the Japanese economy.     

Extending the capital asset pricing model: the reward beta approach

This paper offers an alternative method for estimating expected returns. The proposed reward beta approach performs well empirically and is based on asset pricing theory. The empirical section compares this approach with the capital asset pricing model (CAPM) and the Fama–French three‐factor model. In out‐of‐sample testing, both the CAPM and the three‐factor model are rejected. In contrast, the reward beta approach easily passes the same test. In robustness checks, the reward beta approach consistently outperforms both the CAPM and the three‐factor model.     

Asset Pricing with Conditioning Information: A New Test

This paper presents a new test of conditional versions of the Sharpe-Lintner CAPM, the Jagannathan and Wang (1996) extension of the CAPM, and the Fama and French (1993) three-factor model. The test is based on a general nonparametric methodology that avoids functional form misspecification of betas, risk premia, and the stochastic discount factor. Our results provide a novel view of empirical performance of these models. In particular, we find that a nonparametric version of the Fama and French model performs well, even when challenged by momentum portfolios.     

A semiparametric conditional capital asset pricing model

This paper proposes using a functional coefficient regression technique to estimate time-varying betas and alpha in the conditional capital asset pricing model (CAPM). Functional coefficient representation relaxes the strict assumptions regarding the structure of betas and alpha by combining the predictors into an index. Appropriate index variables are selected by applying the smoothly clipped absolute deviation penalty. In such a way, estimation and variable selection can be done simultaneously. Based on the empirical studies, the proposed model performs better than the alternatives in explaining asset returns and we find no strong evidence to reject the conditional CAPM.     

The performance of cross-sectional regression tests of the CAPM with non-zero pricing errors

We show that in the presence of non-zero pricing errors, the Fama–MacBeth (FM) cross-sectional regression test is very likely to either reject the Capital Asset Pricing Model (CAPM) when it (almost) holds or accept the model when it grossly fails. We investigate the case when pricing errors are correlated with betas and demonstrate that the test performance depends crucially on the correlation, cross-sectional distribution of betas, and several other parameter values. Even when the CAPM holds exactly (pricing errors are zero) the FM test is equally likely to either reject or accept the model when typical sample sizes are used.     

The Beta Dilemma in Emerging Markets

In principle, emerging markets analysts employ the same analytical framework when estimating the value of businesses as their counterparts in developed economies: they forecast future cash flows and discount those to the present with appropriate costs of capital that are estimated using the Capital Asset Pricing Model (CAPM) framework. But in practice, emerging market analysts have a more complicated job because the task of estimating costs of equity in emerging markets is more difficult. Whereas developed economies have an abundance of historical data on overall stock market movements, industry share price behavior, and many individual share price histories, emerging market economies often do not. There may be no comparable local firms that are publicly traded—or if there are, their CAPM betas may be unreliable. And if analysts instead use the beta of a U.S. competitor as a surrogate for the emerging market beta, they face the question of whether domestic betas are equivalent across borders. As a consequence, appraisers of emerging market companies confront a “beta dilemma.” Part of this is a data problem stemming from shorter share price histories in emerging markets and the absence of publicly traded companies in some industries. In such cases, analysts may be inclined to use industry betas calculated with U.S. share prices as a substitute. But this creates an equivalence problem—the possibility, as confirmed by the author's research, that domestic U.S. and emerging market betas are not statistically equivalent for most industries. The author proposes a solution to this problem that involves grouping emerging markets into a single, distinctive asset class that allows for reliable calculations of industry betas. He also suggests ways of testing emerging market industry betas to determine whether they are statistically comparable.     

Alternative measures of the Federal Reserve Banks’ cost of equity capital

The Monetary Control Act of 1980 requires the Federal Reserve System to provide payment services to depository institutions through the 12 Federal Reserve Banks at prices that fully reflect the costs a private-sector provider would incur, including a cost of equity capital (COE). Although Fama and French [Fama, E.F., French, K.R., 1997. Industry costs of equity. Journal of Financial Economics 43, 153–193] conclude that COE estimates are “woefully” and “unavoidably” imprecise, the Reserve Banks require such an estimate every year. We examine several COE estimates based on the CAPM model and compare them using econometric and materiality criteria. Our results suggest that the benchmark CAPM model applied to a large peer group of competing firms provides a COE estimate that is not clearly improved upon by using a narrow peer group, introducing additional factors into the model, or taking account of additional firm-level data, such as leverage and line-of-business concentration. Thus, a standard implementation of the benchmark CAPM model provides a reasonable COE estimate, which is needed to impute costs and set prices for the Reserve Banks’ payments business.     

Risk Interpretation of the CAPM's Beta: Evidence from a New Research Method

This study tests the validity of using the CAPM beta as a risk control in cross‐sectional accounting and finance research. We recognize that high‐risk stocks should experience either very good or very bad returns more frequently compared to low‐risk stocks, that is, high‐risk stocks should cluster in the tails of the cross‐sectional return distribution. Building on this intuition, we test the risk interpretation of the CAPM's beta by examining if high‐beta stocks are more likely than low‐beta stocks to experience either very high or very low returns. Our empirical results indicate that beta is a strong predictor of large positive and large negative returns, which confirms that beta is a valid empirical risk measure and that researchers should use beta as a risk control in empirical tests. Further, we show that because the relation between beta and returns is U‐shaped, that is, high betas predict both very high and very low returns, linear cross‐sectional regression models, for example, Fama–MacBeth regressions, will fail on average to reject the null hypothesis that beta does not capture risk. This result explains why previous studies find no significant cross‐sectional relation between beta and returns.     

Cross‐sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market

We study the performance of conditional asset pricing models and multifactor models in explaining the German cross‐section of stock returns. We focus on several variables, which (according to previous research) are associated with market expectations on future market excess returns or business cycle conditions. Our results suggest that the empirical performance of the Capital Asset Pricing Model (CAPM) can be improved when allowing for time‐varying parameters of the stochastic discount factor. A conditional CAPM using the term spread explains the returns on our size and book‐to‐market sorted portfolios about as well as the Fama‐French three‐factor model and performs best in terms of the Hansen‐Jagannathan distance. Structural break tests do not necessarily indicate parameter instability of conditional model specifications. Another major finding of the paper is that the Fama‐French model – despite its generally good cross‐sectional performance – is subject to model instability. Unconditional models, however, do a better job than conditional ones at capturing time‐series predictability of the test portfolio returns.     

Monotonicity in asset returns: New tests with applications to the term structure,the CAPM,and portfolio sorts

Many theories in finance imply monotonic patterns in expected returns and other financial variables. The liquidity preference hypothesis predicts higher expected returns for bonds with longer times to maturity; the Capital Asset Pricing Model (CAPM) implies higher expected returns for stocks with higher betas; and standard asset pricing models imply that the pricing kernel is declining in market returns. The full set of implications of monotonicity is generally not exploited in empirical work, however. This paper proposes new and simple ways to test for monotonicity in financial variables and compares the proposed tests with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.