Paulo F. Maio

Interest Rate Risk and the Cross-Section of Stock Returns

We derive a macroeconomic asset pricing model in which the key factor is the opportunity cost of money. The model explains well the cross section of stock returns in addition to the excess market return. The interest rate factor is priced and seems to drive most of the explanatory power of the model. In this model, both value stocks and past long-term losers enjoy higher average (excess) returns because they have higher interest rate risk than growth/past winner stocks. The model significantly outperforms the nested models (capital asset pricing model (CAPM) and consumption CAPM (CCAPM)) and compares favorably with alternative macroeconomic models.

Intertemporal CAPM with Conditioning Variables

This paper derives and tests an intertemporal capital asset pricing model ICAPM based on a conditional version of the Campbell--Vuolteenaho two-beta ICAPM bad beta, good beta BBGB. The novel factor is a scaled cash-flow factor that results from the interaction between cash-flow news and a lagged state variable market dividend yield or consumer price index inflation. The cross-sectional tests over 10 portfolios sorted on size, 10 portfolios sorted on book-to-market, and 10 portfolios sorted on momentum show that the scaled ICAPM explains relatively well the dispersion in excess returns on the 30 portfolios. The results for an alternative set of equity portfolios 25 portfolios sorted on size and momentum show that the scaled ICAPM prices particularly well the momentum portfolios. Moreover, the scaled ICAPM compares favorably with alternative asset pricing models in pricing both sets of equity portfolios. The scaled factor is decisive to account for the dispersion in average excess returns between past winner and past loser stocks. More specifically, past winners are riskier than past losers in times of high price of risk. Therefore, a time-varying cash-flow beta/price of risk provides a rational explanation for momentum. This paper was accepted by Wei Xiong, finance.

Dividend Yields, Dividend Growth, and Return Predictability in the Cross-Section of Stocks

There is a generalized conviction that variation in dividend yields is exclusively related to expected returns and not to expected dividend growth, for example, Cochrane’s (2011) presidential address. We show that this pattern, although valid for the aggregate stock market, is not true for portfolios of small and value stocks, where dividend yields are related mainly to future dividend changes. Thus, the variance decomposition associated with the aggregate dividend yield has important heterogeneity in the cross section of equities. Our results are robust to different forecasting horizons, econometric methodology (long-horizon regressions or first-order vector autoregression), and alternative decomposition based on excess returns.

Short-Term Interest Rates and Stock Market Anomalies

We present a simple two-factor model that helps explaining several CAPM anomalies -- value premium, return reversal, equity duration, asset growth, and inventory growth. The model is consistent with Mertons ICAPM framework and the key risk factor is the innovation on a short-term interest rate -- the Fed funds rate or the T-bill rate. This model explains a large fraction of the dispersion in average returns of the joint market anomalies. Moreover, the model compares favorably with alternative multifactor models widely used in the literature. Hence, short-term interest rates seem to be relevant for explaining several dimensions of cross-sectional equity risk premia.

Return Dispersion and the Predictability of Stock Returns

In this paper, I examine whether stock return dispersion (RD) provides useful information about future stock returns. RD consistently forecasts a decline in the excess market return at multiple horizons, and compares favorably with alternative predictors used in the literature. The out-of-sample performance of RD tends to beat the alternative predictors, and is economically significant as indicated by the certainty equivalent gain associated with a trading investment strategy. RD has greater forecasting power for big and growth stocks compared to small and value stocks, respectively. I discuss a theoretical mechanism giving rise to the negative correlation between RD and the equity premium.

Intertemporal CAPM with Time-Varying Risk Aversion

This paper derives and tests an ICAPM based on a conditional version of the two-beta ICAPM (bad beta, good beta, BBGB) from Campbell and Vuolteenaho (2004). The novel factor is a scaled cash-flow factor that results from the interaction between cash-flow news and a lagged state variable (market dividend yield or CPI inflation). The cross-sectional tests over 10 portfolios sorted on size, 10 portfolios sorted on book-to-market, and 10 portfolios sorted on momentum show that the scaled ICAPM explains relatively well the dispersion in excess returns on the 30 portfolios. The results for an alternative set of equity portfolios (25 portfolios sorted on size and momentum) show that the scaled ICAPM prices particularly well the momentum portfolios. Moreover, the scaled ICAPM compares favorably with alternative asset pricing models in pricing both sets of equity portfolios -- the BBGB model, the unconditional CAPM, the conditional CAPM, the Fama and French (1993) three-factor model, and the Carhart (1997) four-factor model. The scaled factor is decisive to account for the dispersion in average excess returns between past winner and past loser stocks. More specifically, past winners are riskier than past losers in times of high price of risk. Therefore, a time-varying cash-flow beta/price of risk provides a rational explanation for momentum.

Cash-Flow or Return Predictability at Long Horizons? The Case of Earnings Yield

This paper focuses on the predictive ability of the aggregate earnings yield for market returns and earnings growth by imposing the restrictions associated with a present-value relation. By estimating a variance decomposition for the earnings yield based on weighted long-horizon regressions for the 1872–1925 and 1926–2010 periods, I find a reversal in return/earnings growth predictability: in the earlier period, the bulk of variation in the earnings yield is predictability of earnings growth, while in the modern sample the driving force is return predictability. When the variance decomposition is based on a first-order VAR the results in the modern sample are qualitatively different, i.e., the restrictions imposed by the first-order VAR are not validated by the data. Therefore, in the post-1926 period what drives aggregate financial ratios are expectations about future discount rates rather than future cash flows, irrespective of the financial ratio (dividend yield or earnings yield) being used.We examine the predictive ability of the aggregate earnings yield for market returns and earnings growth by estimating a variance decomposition. Based on weighted long-horizon regressions for the 1946-2015 period, we find that most of the variation in the earnings yield is due to return predictability, with earnings growth predictability assuming a secondary role. However, by estimating a variance decomposition based on a first-order vector autoregression (VAR), we find that the major driving force is earnings growth predictability. We show that the share of earnings predictability implied from the VAR is due to a misspecification driven by the unusual high volatility in aggregate U.S. earnings during the recent financial crisis (2007-2009). Our results suggest that the practice of analyzing long-run return and cash-flow predictability from a VAR can be misleading, and reinforce previous evidence that what drives variation in aggregate price ratios is return (instead of cash-flow) predictability.

A Simple Model that Helps Explaining the Accruals Anomaly

We propose a new multifactor model to price the cross-section of average excess returns associated with accruals portfolios, and hence explain the accruals anomaly. Our model represents an application of the Intertemporal CAPM from Merton (1973), where the key factors are the innovations on the term spread and value spread. The model clearly outperforms the simple CAPM, and shows large explanatory power for the cross-sectional risk premia associated with three different groups of accrual portfolios. Moreover, the model compares favorably with alternative multifactor models widely used in the literature. Our results remain robust by using equal-weighted accruals portfolios.

Factor Dispersion and Cross-sectional Risk Premia

I propose a new risk-based explanation of momentum by deriving a simple two-factor asset pricing model, which contains the market return and stock return dispersion as risk factors. This model offers a large fit for portfolios sorted on momentum and industry momentum, with cross-sectional R^2 estimates around 80%. Past losers are more positively correlated with return dispersion, which earns a negative price of risk, and hence the lower risk premia earned by those stocks. The model compares favorably with some of the most popular multifactor models in the literature, and also helps explaining the earnings momentum and profitability anomalies.

Multifactor Models and the APT: Evidence from a Broad Cross-Section of Stock Returns

We examine the consistency of several prominent multifactor models from the asset pricing literature with the Arbitrage Pricing Theory (APT) framework. We follow the APT-related literature and estimate the common factor structure from a cross-section containing 420 equity portfolios (associated with 42 major CAPM anomalies) by employing the asymptotic principal components method. Our benchmark model contains six statistical factors and clearly dominates (both in economic and statistical terms) most of the empirical multifactor models. These results represent a critical challenge to the current workhorse models in terms of explaining large-scale portfolio equity risk premia and achieving consistency with the APT.