深圳B股市场有效性分析

根据资本资产定价模型(CAPM),针对深圳B股市场2002年1月至2004年12月这一段时间的交易数据,利用横截面统计回归的方法对深圳B股的市场β值、流通市值、市盈率、账面/市场价值的比率四个因素与股票收益率之间的关系进行检验,结果是收益率与流通市值的对数、市场β值、市盈率、账面/市场价值比率的对数均呈负相关关系,这表明,深圳B股市场是非有效的.进而分析了深圳B股市场非有效的原因,并对强化深圳B股市场效率提出了一些建议.     

上海股票市场羊群行为实证研究

采用一种利用资产定价模型因子载荷截面离散度指标测度羊群行为的新方法来检验上海股票市场是否存在以市场指数为领头羊的羊群行为。这种方法不仅考虑到了基本面的变化,还自动考虑了时间序列波动的影响,并能直观地显现市场中羊群行为的动态特征。实证结果发现,上海股票市场存在以市场指数为领头羊的羊群行为,且羊群行为的增强和减弱与市场的阶段性涨跌相对应,市场上涨和下跌时羊群行为较严重,在市场相对平静时也存在一定程度的羊群行为,这和以往的实证结果认为羊群行为仅出现在市场剧烈波动时期不同。但是,市场羊群行为周期与大盘运行趋势周期并不完全相一致,即使在股市出现单边上涨或下跌过程中,市场羊群行为也可以完成增强与减弱的完整循环,这说明市场中投资者的风险意识在不断的变化。     

Rational functions: an alternative approach to asset pricing

This paper shows that asset prices are linear polynomials of various underlying explanatory factors and asset returns being ratios of these polynomials, are rational functions that do not add linearly when averaging. Hence, average returns should be modeled based on stock prices. However, continuous returns may be treated as approximately linear across time and modeled directly. Our new Rational Function (RF) models, empirically outperform the traditional asset pricing models like the Capital Asset Pricing Model (CAPM) and the Fama–French three and five-factor models for both average and continuous returns. Moreover, the RF theory also provides a model to estimate the asset volumes. The average change in asset volumes together with average returns provide the estimates for average change in market values of assets. Thus, the RF model approach can be used to select assets that provide either highest returns for profit maximization or highest change in market values for wealth maximization for given levels of risk.     

行为资产定价模型的适用性研究

作为行为金融学主干的行为资产定价理论近些年来发展迅速。行为资产定价模型能否指导现实中的投资行为,取决于对行为资产定价模型的实证检验结果。本文利用我国证券市场数据对行为资产定价模型的适用性进行了检验,发现行为资产定价模型适用于股价上涨时期,而非股价下跌时期。     

SAD效应：一个基于时变风险价格的条件CAPM的解释

很多实证研究都证明季节性情绪混乱(SAD)与股指收益有明显联系。本文研究了这种SAD效应能否用一个包含允许时间变化的风险价格的资产定价模型去捕捉它的存在。本文利用上证综指的日收盘数据得到的初始和超额收益率以及SAD和FALL变量,利用修正的GRACH-M模型进行了分析,认为条件CAPM可以捕捉SAD效应。这与由于日照时间变化而导致的投资者风险承受能力变化进而影响收益的认识相符合。     

对套利定价理论及应用的认识

本文从套利的一般概念入手，分析了套利的基本特点，叙述了套利定价理论(APT)的基本假设，APT的主要推导思路及主要结论，指出了应用APT进行套利交易的一般过程，并对套利定价理论和资本资产定价模型(CAPM)进行了比较，最后提出对几个相关问题的思考。     

The cost of capital in internationally integrated markets: The case of Nestlé

This paper argues that the cost of capital for firms in small countries should be estimated using the global CAPM rather than a local CAPM. Two related formulas showing the mistake made when using a local CAPM rather than a global CAPM are presented. the global CAPM is implemented for the case of Nestlé and the results are compared to the cost of capital estimate one obtains for Nestlé using a local CAPM when the global CAPM is appropriate.     

基于沪深股市的CAPM适用性检验

利用投资组合理论化解非系统风险的关键条件是系统风险能够充分地被β所反映。本文采用最近八年的沪深股票交易数据。对CAPM在我国股市的适用性进行检验，检验结果是β仍然不能涵盖所有无法分散的风险。     

Discounting Mean Reverting Cash Flows with the Capital Asset Pricing Model

Discounting cash flows requires an equilibrium model to determine the cost of capital. The CAPM of Sharpe and the intertemporal asset pricing model of Merton (1973) offer a theoretical justification for discounting at a constant risk adjusted rate. Two problems arise with this application. First, for mean reverting cash flows the risk adjustment is unknown, and second, if the present value is compounded forward then the distribution of future wealth is likely right skewed. I develop equilibrium discount rates for cash flows whose level or growth rate is mean reverting. Serial correlation also largely eliminates the skewness problem.     

The Determinants of REIT Institutional Ownership: Tests of the CAPM

This study examines the determinants of institutional investment demand for REIT common stock. We estimate the demand function for financial institutions using the mean return and CAPM risk measures (beta and standard error) for REIT stocks. The objective is to determine whether institutional investment decisions are influenced by CAPM model attributes. In addition, we examine the predicatability of REIT institutional ownership based on the factors in our model. We employ conventional OLS forecasting techniques, as well as two neural network models in order to deal with possible nonlinearities in the relationships.