Multifactor Explanations of Asset Pricing Anomalies

Previous work shows that average returns on common stocks are related to firm characteristics like size, earnings/price, cash flow/price, book-to-market equity, past sales growth, long-term past return, and short-term past return. Because these patterns in average returns apparently are not explained by the CAPM, they are called anomalies. We find that, except for the continuation of short-term returns, the anomalies largely disappear in a three-factor model. Our results are consistent with rational ICAPM or APT asset pricing, but we also consider irrational pricing and data problems as possible explanations.     

Multifactor Asset Pricing Models

Since the early 1960s, the mean-variance Capital Asset Pricing Model (CAPM) has been a dominant paradigm in modern finance. Recently, the accumulation of anomalous evidence, and a realisation that empirical tests of the model are tautologically related to the efficiency of the market index, have pushed that paradigm to a point of crisis. This paper reviews alternative asset pricing models which coexisted with the CAPM and may provide plausible substitutes. The major distinguishing feature of these models is that they predict multiple risk factors and, with the exception of the Arbitrage Pricing Theory (APT), are extensions of the CAPM.     

Comparing Asset Pricing Models

A Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.     

On Comparing Asset Pricing Models

Revisiting the framework of (Barillas, Francisco, and Jay Shanken, 2018, Comparing asset pricing models, The Journal of Finance 73, 715–754). BS henceforth, we show that the Bayesian marginal likelihood-based model comparison method in that paper is unsound : the priors on the nuisance parameters across models must satisfy a change of variable property for densities that is violated by the Jeffreys priors used in the BS method. Extensive simulation exercises confirm that the BS method performs unsatisfactorily. We derive a new class of improper priors on the nuisance parameters, starting from a single improper prior, which leads to valid marginal likelihoods and model comparisons. The performance of our marginal likelihoods is significantly better, allowing for reliable Bayesian work on which factors are risk factors in asset pricing models.     

Portfolio Selection and Asset Pricing Models

Finance theory can be used to form informative prior beliefs in financial decision making. This paper approaches portfolio selection in a Bayesian framework that incorporates a prior degree of belief in an asset pricing model. Sample evidence on home bias and value and size effects is evaluated from an asset-allocation perspective. U.S. investors' belief in the domestic CAPM must be very strong to justify the home bias observed in their equity holdings. The same strong prior belief results in large and stable optimal positions in the Fama–French book-to-market portfolio in combination with the market since the 1940s.     

Stock Return Predictability and Asset Pricing Models

This article develops an asset allocation framework that incorporatesprior beliefs about the extent of stock return predictabilityexplained by asset pricing models. We find that when prior beliefsallow even minor deviations from pricing model implications,the resulting asset allocations depart considerably from andsubstantially outperform allocations dictated by either theunderlying models or the sample evidence on return predictability.Under a wide range of beliefs about model pricing abilities,asset allocations based on conditional models outperform theirunconditional counterparts that exclude return predictability.     

Diagnosing Asset Pricing Models Using the Distribution of Asset Returns

This paper develops a set of diagnostic tests which can shed light on why a particular model is failing and indicate what steps might be taken to make the model consistent with asset returns. Theoretical bounds on the moments of a stochastic discount factor are derived as a function of the moments of observed asset returns. Particular attention is paid to restrictions on moments other than the variance. These bounds can also be used to measure the information about the distribution of the discount factor contained in the moments of various asset returns. As an application of this methodology, bounds on the discount factor are estimated using size-based portfolios, and the results are used to analyze the small firm effect. Empirical results indicate, for the period 1926–1975, that moments of the returns of small firms contain information about the discount factor that is not contained in the moments of the returns of large firms and/or a proxy of the aggregate wealth portfolio. However, this difference disappears when more recent data is included.     

Estimation and Evaluation of Conditional Asset Pricing Models

We find that several recently proposed consumption‐based models of stock returns, when evaluated using an optimal set of managed portfolios and the associated model‐implied conditional moment restrictions, fail to capture key features of risk premiums in equity markets. To arrive at these conclusions, we construct an optimal Generalized Method of Moments (GMM) estimator for models in which the stochastic discount factor (SDF) is a conditionally affine function of a set of priced risk factors, and we show that there is an optimal choice of managed portfolios to use in testing a null model against a proposed alternative generalized SDF.     

Using Expectations to Test Asset Pricing Models

Asset pricing models generate predictions relating assets' expected rates of return and their risk attributes. Most tests of these models have employed realized rates of return as a proxy for expected return. We use analysts' expected rates of return to examine the relation between these expectations and firm attributes. By assuming that analysts' expectations are unbiased estimates of market-wide expected rates of return, we can circumvent the use of realized rates of return and provide evidence on the predictions emanating from traditional asset pricing models. We find a positive, robust relation between expected return and market beta and a negative relation between expected return and firm size, consistent with the notion that these are risk factors. We do not find that high book-to-market firms are expected to earn higher returns than low book-to-market firms, inconsistent with the notion that book-to-market is a risk factor.     

资产证券化演进与金融市场效率

资产证券化的不断演进带来了金融市场效率的革命，使其成为最具创新性和最具活力的金融创新业务。资产证券化的引入将对优化我国金融体系结构和加强金融风险控制具有深远的影响。资产证券化演进需要宽松的制度支持，制度创新是我国推行资产证券化的首要任务。     

金融资产定价与普通商品定价的比较研究

站在时间的维度上,我们可以更加清楚地了解一般商品价格理论和金融资产价格理论的区别和内在联系。一般商品的价格理论是构建在个体和厂商之上的一般均衡分析,从时间的维度看,它是一种面向历史的分析范式。金融资产的定价则是建立在无套利均衡思想上的资本化定价,从时间的维度看,它是一种面向未来的分析范式。     

Portfolio Construction for Tests of Asset Pricing Models

Portfolios are commonly used in finance literature to study asset‐pricing models. In business practice portfolios are used to detect abnormal performance in certain asset groups or to construct reference assets. However, analyses on practical issues related to portfolio construction are surprisingly few. This paper presents and discusses issues related to portfolio return calculation from theoretical and practical perspectives. Special attention is given both to smaller and emerging stock markets. These stock markets often share common features like low liquidity, multiple stock series, and changes in foreign ownership restrictions that greatly affect portfolio construction.     

Return Distributions and Improved Tests of Asset Pricing Models

We compare and contrast some existing ordinary least squares(OLS)- and generalized method of moments (GMM)-based tests ofasset pricing models with a new more general test. This newtest is valid under the assumption that returns are ellipticallydistributed, a necessary and sufficient assumption of the linearcapital asset pricing model (CAPM). This new test fails to rejectthe CAPM on a dataset of stocks sorted by market valuations,whereas similar tests constructed from OLS and GMM estimationmethods reject the linear CAPM. We also find that outliers reducethe OLS-estimated mispricing of the linear CAPM on monthly returnssorted by previous performance, that is, momentum. Monte Carloevidence supports superior size and power properties of thenew test relative to OLS- and GMM-based tests.     

Can Market Incompleteness Resolve Asset Pricing Puzzles?

Abstract:   This paper shows that the presence of persistent uninsurable risk concentrated in economic depressions has the potential to resolve two well‐known asset pricing puzzles. It is also shown that the presence of such risk in more normal economic expansions and recessions is likely to be much less relevant in determining equilibrium asset prices.     

基于货币和财富的资本资产定价模型

基于货币和财富的资本资产定价模型指出，任何风险资产的期望超额收益率是由该风险资产的收益率与消费、货币和财富的增长率的协方差决定的。     

Explaining the Poor Performance of Consumption-based Asset Pricing Models

We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain why the Capital Asset Pricing Model (CAPM) and its extensions are betterapproximate asset pricing models than is the standard onsumption-based model. The model economy produces time-varying expected eturns, tracked by the dividend–price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture. Therefore, though the consumption-based model and CAPM are both perfect conditional asset pricing models, the portfolio-based models are better approximate unconditional asset pricing models.     

Implementing Option Pricing Models When Asset Returns Are Predictable

The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely-sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black-Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous-time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options.     

Two-Pass Tests of Asset Pricing Models with Useless Factors

In this paper we investigate the properties of the standard two-pass methodology of testing beta pricing models with misspecified factors. In a setting where a factor is useless, defined as being independent of all the asset returns, we provide theoretical results and simulation evidence that the second-pass cross-sectional regression tends to find the beta risk of the useless factor priced more often than it should. More surprisingly, this misspecification bias exacerbates when the number of time series observations increases. Possible ways of detecting useless factors are also examined.     

Numerical Approach to Asset Pricing Models with Stochastic Differential Utility

In this paper employing two heuristic numerical schemes, we study the asset pricing models with stochastic differential utility (SDU), which is formulated by either of backward stochastic differential equations (BSDEs) or forward-backward stochastic differential equations (FBSDEs).The first scheme is based upon a traditional lattice algorithm of option pricing theories, involving the discretization scheme of coupled FBSDEs, which is combined with a technique of solving numerically a certain type of nonlinear equations with respect to the backward state variables. The second one is based upon the four step scheme of Ma et al. (1994) which solves quasi-linear partial differential equations associated with the FBSDEs. We demonstrate that our practical implementation algorithms can successfully solve the asset pricing models with generalized SDU and the large investor problem with market impact which are typical examples such that the usual four step scheme is difficult to implement. As other numerical applications we study the optimal consumption and investment policies of a representative agent with SDU, and the recoverability of preferences and beliefs from observed consumption data.