Forecast Dispersion and the Cross Section of Expected Returns

Recent work by Diether, Malloy, and Scherbina (2002) has established a negative relationship between stock returns and the dispersion of analysts' earnings forecasts. I offer a simple explanation for this phenomenon based on the interpretation of dispersion as a proxy for unpriced information risk arising when asset values are unobservable. The relationship then follows from a general options‐pricing result: For a levered firm, expected returns should always decrease with the level of idiosyncratic asset risk. This story is formalized with a straightforward model. Reasonable parameter values produce large effects, and the theory's main empirical prediction is supported in cross‐sectional tests.     

Stock Market Volatility and Learning

We show that consumption‐based asset pricing models with time‐separable preferences generate realistic amounts of stock price volatility if one allows for small deviations from rational expectations. Rational investors with subjective beliefs about price behavior optimally learn from past price observations. This imparts momentum and mean reversion into stock prices. The model quantitatively accounts for the volatility of returns, the volatility and persistence of the price‐dividend ratio, and the predictability of long‐horizon returns. It passes a formal statistical test for the overall fit of a set of moments provided one excludes the equity premium.     

Tests of the Relations Among Marketwide Factors,Firm-Specific Variables,and Stock Returns Using a Conditional Asset Pricing Model

In this article we generalize Harvey's (1989) empirical specification of conditional asset pricing models to allow for both time-varying covariances between stock returns and marketwide factors and time-varying reward-to-covariabilities. The model is then applied to examine the effects of firm size and book-to-market equity ratios. We find that the traditional asset pricing model with commonly used factors can only explain a small portion of the stock returns predicted by firm size and book-to-market equity ratios. The results indicate that allowing time-varying covariances and time-varying reward-to-covariabilities does little to salvage the traditional asset pricing models.     

Dividend Dynamics and the Term Structure of Dividend Strips

Many leading asset pricing models are specified so that the term structure of dividend volatility is either flat or upward sloping. These models predict that the term structures of expected returns and volatilities on dividend strips (i.e., claims to dividends paid over a prespecified interval) are also upward sloping. However, the empirical evidence suggests otherwise. This discrepancy can be reconciled if these models replace their proposed dividend dynamics with processes that generate stationary leverage ratios. Under such policies, shareholders are forced to divest (invest) when leverage is low (high), which shifts risk from long‐ to short‐horizon dividend strips.     

Valuation Risk and Asset Pricing

Standard representative‐agent models fail to account for the weak correlation between stock returns and measurable fundamentals, such as consumption and output growth. This failing, which underlies virtually all modern asset pricing puzzles, arises because these models load all uncertainty onto the supply side of the economy. We propose a simple theory of asset pricing in which demand shocks play a central role. These shocks give rise to valuation risk that allows the model to account for key asset pricing moments, such as the equity premium, the bond term premium, and the weak correlation between stock returns and fundamentals.     

Derivative Pricing with Liquidity Risk: Theory and Evidence from the Credit Default Swap Market

We derive an equilibrium asset pricing model incorporating liquidity risk, derivatives, and short‐selling due to hedging of nontraded risk. We show that illiquid assets can have lower expected returns if the short‐sellers have more wealth, lower risk aversion, or shorter horizon. The pricing of liquidity risk is different for derivatives than for positive‐net‐supply assets, and depends on investors' net nontraded risk exposure. We estimate this model for the credit default swap market. We find strong evidence for an expected liquidity premium earned by the credit protection seller. The effect of liquidity risk is significant but economically small.     

Financial Flexibility,Bank Capital Flows,and Asset Prices

In our parsimonious general‐equilibrium model of banking and asset pricing, intermediaries have the expertise to monitor and reallocate capital. We study financial development, intraeconomy capital flows, the size of the banking sector, the value of intermediation, expected market returns, and the risk of bank crashes. Asset pricing implications include: a market's dividend yield is related to its financial flexibility, and capital flows should be important in explaining expected returns and the risk of bank crashes. Our predictions are broadly consistent with the aggregate behavior of U.S. capital markets since 1950.     

Constructing Asset Pricing Models With Specific Factor Loadings

We demonstrate how one can build pricing formulae in which factors other than beta may be viewed as determinants of asset returns. This is important conceptually as it demonstrates how the additional factors can compensate for a market portfolio proxy that is mis‐specified, and also shows how such a pricing model can be specified ex ante. The procedure is implemented by first selecting an ‘orthogonal’ portfolio which falls on the mean‐variance efficient frontier computed from the empirical average returns, variances and covariances on the equity securities of a large sample of firms. One then determines the inefficient index portfolio which leads to a vector of betas that when multiplied by the average return on the orthogonal portfolio, and which when subtracted from the vector of average returns for the firms comprising the sample, yields an error vector that is equal to the vector of numerical values for the variables that are to form the basis of the asset pricing formula. There will then be a perfect linear relationship between the vector of average returns for the firms comprising the sample, the vector of betas based on the inefficient index portfolio and such other factors that are deemed to be important in the asset pricing process. We illustrate computational procedures using a numerical example based on the quality of information contained in published corporate financial statements.     

A best choice among asset pricing models? The Conditional Capital Asset Pricing Model in Australia

We use Australian data to test the Conditional Capital Asset Pricing Model (Jagannathan and Wang, 1996). Our results are generally supportive: the model performs well compared with a number of competing asset pricing models. In contrast to the study by Jagannathan and Wang, however, we find that the inclusion of the market for human capital does not save the concept of the time‐independent market beta (it remains insignificant). We find support for the role of a small‐minus‐big factor in pricing the cross‐section of returns and find grounds to disagree with Jagannathan and Wang's argument that this factor proxies for misspecified market risk.     

Exchange Rate Exposure at the Firm and Industry Level

Previous work on the exposure of firms to exchange rate risk has primarily focused on U.S. firms and, surprisingly, found stock returns were not significantly affected by exchange‐rate fluctuations. The equity market premium for exposure to currency risk was also found to be insignificant. In this paper we examine the relation between Japanese stock returns and unanticipated exchange‐rate changes for 1,079 firms traded on the Tokyo stock exchange over the 1975–1995 period. Second, we investigate whether exchange‐rate risk is priced in the Japanese equity market using both unconditional and conditional multifactor asset pricing testing procedures. We find a significant relation between contemporaneous stock returns and unanticipated yen fluctuations. The exposure effect on multinationals and high‐exporting firms, however, is found to be greater in comparison to low‐exporting and domestic firms. Lagged‐exchange rate changes on firm value are found to be statistically insignificant implying that investors are able to assess the impact of exchange‐rate changes on firm value with no significant delay. The industry level analysis corroborates the cross‐sectional findings for Japanese firms in that they are sensitive to contemporaneous unexpected exchange‐rate fluctuations. The co‐movement between stock returns and changes in the foreign value of the yen is found to be positively associated with the degree of the firm's foreign economic involvement and inversely related to its size and debt to asset ratio. Asset pricing tests show that currency risk is priced. We find corroborating evidence in support of the view that currency exposure is time varying. Our results indicate that the foreign exchange‐rate risk premium is a significant component of Japanese stock returns. The combined evidence from the currency exposure and asset pricing analyses, suggests that currency risk is priced and, therefoe, has implications for corporate and portfolio managers.     

IS THE BOOK‐TO‐MARKET RATIO A MEASURE OF RISK?

We develop a leverage‐based alternative to traditional asset pricing models to investigate whether the book‐to‐market ratio acts as a proxy for risk. We argue that the book‐to‐market ratio should act as a proxy because of the expected relations between (1) financial risk and measures of capital structure based on the market value of equity and (2) asset risk and measures of capital structure based on the book value of equity. We find no relation between average stock returns and the book‐to‐market ratio in all‐equity firms after controlling for firm size, and an inverse relation between average stock returns and the book‐to‐market ratio in firms with a negative book value of equity.     

Exchange Rates and the Conversion of Currency‐Specific Risk Premia

How do the risk factors that drive asset prices influence exchange rates? Are the parameters of asset price processes relevant for specifying exchange rate processes? Most international asset pricing models focus on the analysis of asset returns given exchange rate processes. Little work has been done on the analysis of exchange rates dependent on asset returns. This paper uses an international stochastic discount factor (SDF) framework to analyse the interplay between asset prices and exchange rates. So far, this approach has only been implemented in international term structure models. We find that exchange rates serve to convert currency‐specific discount factors and currency‐specific prices of risk – a result linked to the international arbitrage pricing theory (IAPT). Our empirical investigation of exchange rates and stock markets of four countries presents evidence for the conversion of currency‐specific risk premia by exchange rates.     

Short-selling bans and institutional investors' herding behaviour: Evidence from the global financial crisis

The literature on short-selling restrictions focusses mainly on a ban's impact on market efficiency, liquidity and overpricing. Surprisingly, little is known about the effects of short-sale constraints on herd behaviour. Since institutional investors have come to dominate mature stock markets and rely extensively on short sales, constraining these traders may influence the asset pricing process. We investigate six stock markets that faced bans during the recent global financial crisis. Our empirical evidence shows that short-selling restrictions exhibit either no influence on herding formation or induce adverse herding. This implies a higher dispersion of returns around the market compared to rational asset pricing, which can be interpreted as an increase in uncertainty among stock market investors.     

Time Variation in the Covariance between Stock Returns and Consumption Growth

The conditional covariance between aggregate stock returns and aggregate consumption growth varies substantially over time. When stock market wealth is high relative to consumption, both the conditional covariance and correlation are high. This pattern is consistent with the “composition effect,” where agents' consumption growth is more closely tied to stock returns when stock wealth is a larger share of total wealth. This variation can be used to test asset‐pricing models in which the price of consumption risk varies. After accounting for variations in this price, the relation between expected excess stock returns and the conditional covariance is negative.     

The Australian asset‐pricing debate

Utilising a comprehensive data set for Australian firms, we examine a range of competing asset‐pricing models, including the four‐ and five‐factor models where the equity‐risk premium is augmented by size, value, momentum and liquidity premia, and find that none of the models tested appears to adequately explain the cross section of Australian returns. A model accounting for Australia's integration with the US equity market appears to be the best of the competing models we study. Our argument that a model recognising Australia's integration with the USA is supported when we apply the portfolio and factor construction methodology suggested by Brailsford et al. (2012a,b).     

Does Greater Firm‐Specific Return Variation Mean More or Less Informed Stock Pricing?

Roll [1988] observes low R² statistics for common asset pricing models due to vigorous firm‐specific return variation not associated with public information. He concludes that this implies “either private information or else occasional frenzy unrelated to concrete information”[p. 56]. We show that firms and industries with lower market model R² statistics exhibit higher association between current returns and future earnings, indicating more information about future earnings in current stock returns. This supports Roll's first interpretation: higher firm‐specific return variation as a fraction of total variation signals more information‐laden stock prices and, therefore, more efficient stock markets.     

In America's thrall: the effects of the US market and US security characteristics on Australian stock returns

Can Australian equity returns be modelled by ‘home‐grown’ factors? We examine the indigenous capital asset pricing model, the indigenous Fama–French three‐factor model, and extensions to the latter, and find them all wanting. We find evidence of domestic market segmentation in Australia. For the smallest firms, all the models we study fail. For the largest Australian firms, we find that the US Fama–French three factors (downloaded from French's website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ ) provide a successful model of Australian returns. It is as if the largest firms in the Australian market are simply part of the larger US market.     

Investment and the Cross‐Section of Equity Returns

The data show that, upon being hit by adverse profitability shocks, large public firms have ample latitude to divest their least productive assets, reducing the risk faced by shareholders and the returns that they are likely to demand. In the one‐factor production‐based asset pricing model, when the frictions to capital adjustment are shaped to respect the evidence on investment, the model‐generated cross‐sectional dispersion of returns is only a small fraction of that documented in the data. Our conclusions hold even when operating or labor leverage is modeled in ways shown to be promising in the extant literature.     

Market Illiquidity and Conditional Equity Premium

We examine the time‐series relation between aggregate bid‐ask spreads and conditional equity premium. We document that average marketwide relative effective bid‐ask spreads forecast aggregate market returns only when controlling for average idiosyncratic variance. This control allows us to document the otherwise elusive relation between illiquidity and returns. The reason is that idiosyncratic variance correlates positively with spreads but has a negative effect on conditional equity premium, causing an omitted variable bias. Our results are robust to standard return predictors, alternative illiquidity measures, and out‐of‐sample tests. These findings are important because they provide strong support for the literature's conjecture that marketwide liquidity is an important asset pricing risk factor.