公允价值的会计计量问题是会计理论与实务的重要研究课题，当缺乏活跃的市价作参考时，由于缺乏适当的分析工具，如何对公允价值的会计计量方法进行评价尤为困难。基于公允价值的机会成本观的视角，设计评价模型，以《关联方之间出售资产等有关会计处理问题暂行规定》（财会[2001] 64号）中规定的会计规范为例，分析评价公允价值的会计计量方法，并用以评价废止《暂行规定》的政策意义。

传统的股利折现模型反映的是持续经营下企业的市场价值，导致企业市场价值定价上的谬误，因为在某些情形下，企业实行有限期经营反而可实现企业市场价值的最大化．通过改进截尾方法与Farrell的持续期限理论，提出修正的股利折现模型，以消除这种谬误．证明了修正模型具有很好的数学分析性质，并用以简要证明折现率与企业最大化其市场价值经营方式之间的内在联系．利用这种内在联系，研究折现率的经济含义和传统的股利折现模型成立的条件．

传统的股利折现模型反映的是持续经营时的企业市场价值，导致企业市场价值定价上的谬误，因为在某些情形下，企业实行有限期经营反而可实现企业市场价值的最大化。通过改进截尾方法与Farrell的持续期限理论，提出修正的股利折现模型，以消除这种谬误。为便于修正的股利折现模型的实际应用，从逼近论的视角，提出可通过选择适当的经营期限，以有限期经营合理近似企业真实经营方式。这样的近似处理，对企业市场价值的影响并不大，将更方便于修正模型的实际应用。

传统的股利折现模型反映的是持续经营时的企业市场价值，导致企业市场价值定价上的谬误，因为在某些情形下，企业实行有限期经营反而可实现企业市场价值的最大化。通过改进截尾方法与Farrell的持续期限理论，提出修正的股利折现模型，以消除这种谬误。修正后的模型，首先需确定企业最大化其市场价值的经营方式（持续经营或是有限期经营）。这样的修正，似乎不利于直接的应用。为此，本文给出判别准则，包括一般准则和股利增长情形下的特殊准则，以确定企业最大化其市场价值所需的经营年限。一旦确定了经营年限，依模型企业市场价值的定价问题就可以确定下来。

传统的股利折现模型(CDDM)反映的是持续经营时的企业市场价值，导致企业市场价值定价上的谬误，因为在某些情形下，企业实行有限期经营反而可实现企业市场价值的最大化．通过改进截尾方法与Farrell的持续期限理论，提出修正的股利折现模型(MDDM)，以消除这种谬误．利用MDDM的数学性质，研究了折现率与企业最大化其市场价值的经营期限之间的内在联系．这种内在联系表明，不同于CDDM，无论其所使用的折现率为多大，总假定企业持续经营；MDDM反映出，折现率的选择事实上确定了企业最大化其市场价值的经营期限，该经营期限存在多样性的特征．

传统的股利折现模型反映的是持续经营时企业的市场价值，导致企业市场价值定价上的谬误，因为在某些情形下，企业实行有限期经营反而能实现其市场价值的最大化。通过改进截尾方法与Farrell的持续期限理论，提出修正的股利折现模型，以消除这种谬误。修正后的股利折现模型更完善，可以反映企业企业最大化其市场价值的经营方式（持续经营或是有限期经营），能够支持企业的市场价值与企业的资本结构、企业的股利政策无关性假设。然而，传统的股利折现模型并不支持此类假设。

Stock valuation is fundamentally important to finance. The current absolute and relative valuations do not wok in some common circumstances. This paper finds a new valuation method with the criterion of required payback period. The new method is a brand new way in valuation paralleling to the discounted cash flow method. This paper further derives the models of theoretical P/E, P/B and P/S based on the new method. These new valuation models are theoretical sound and flexible for valuing various stocks and market bubbles. They can also bridge the gap between the relative and absolute valuations.

In this paper, we extend the long-run risks model of Bansal and Yaron (BY, 2004) to allow both a long- and a short-run volatility component in consumption growth, long-run risks, and dividend growth. Our two volatility model better captures macroeconomic volatility than a single volatility model, and can reconcile simultaneously the large negative market variance risk premium, di?ering predictability in excess returns, consumption, dividends, and stock market volatility, all of which are di±cult to explain previously by the BY model.

This study examines the effect of corporate asset growth on stock returns using data on nine equity markets in Asia. For the period from 1981 to 2007, we find a pervasive negative relation between asset growth and subsequent stock returns. We further examine the determinants of this asset growth effect across markets. The negative relation between asset growth and stock returns is weaker in markets where firms’ assets growth rates are more homogeneous, and in markets where firms rely more on internal financing and bank financing for growth. On the other hand, corporate governance, investor protection, and legal origin do not influence the magnitude of the asset growth effect in the Asian markets.

We analyze return predictability for the Chinese stock market, including the aggregate market portfolio and the components of the aggregate market, such as portfolios sorted on industry, size, book-to-market and ownership concentration. Considering a variety of economic variables as predictors, both in-sample and out-of-sample tests highlight significant predictability in the aggregate market portfolio of the Chinese stock market and substantial differences in return predictability across components. Among industry portfolios, Finance and insurance, Real estate, and Service exhibit the most predictability, while portfolios of small-cap and low ownership concentration firms also display considerable predictability. Two key findings provide economic explanations for component predictability: (i) based on a novel out-of-sample decomposition, time-varying macroeconomic risk premiums captured by the conditional CAPM model largely account for component predictability; (ii) industry concentration and market capitalization significantly explain differences in return predictability across industries, consistent with the information-flow frictions emphasized by Hong, Torous, and Valkanov (2007).