随机贴现因子的要素结构与资产定价异常

在随机贴现因子框架内,资产定价的动态行为、投资者的学习过程及其微观心理行为不仅增强了对定价理论与其实证金融中出现的“定价异常”现象的经济解释能力,还推动了资产定价理论的发展。     

F-F三因子资产定价模型的扩展及其实证研究

针对中国证券市场的特征,本文引入会计指标市盈率、技术指标换手率,对标准F-F三因子模型进行了不同层面的改进。实证结果表明,标准F-F三因子定价模型基本能解释中证100样本股相应组合的周回报率,但效果不是很好;换手率高低对组合收益率的解释能力并不优于标准模型,但换手率与市盈率同时进入模型,却能有效提高周收益率的解释,是一个基本适合中国股市的资产定价模型。     

A股市场的盈利效应及其定价能力 ——基于Fama-French五因子模型的实证研究

以1997年1月至2020年6月我国上市公司的交易数据和财务数据为样本,基于Fama-French五因子模型分析框架,检验我国股票市场盈利溢价的存在性、产生机制及其定价能力.研究发现,盈利稳健的企业相对于盈利疲弱企业存在明显溢价,且这一盈利溢价不能被市场因子、规模因子、价值因子和投资因子很好解释.机制分析发现,盈利溢价效应在投资摩擦低的公司中更显著,盈利溢价的产生机制符合投资Q理论.包括市场因子、规模因子和盈利因子的三因子模型在我国股票市场具有最好的解释能力,说明A股市场具备对盈利能力做出合理价值判断的能力.     

盈利因子与投资因子具有定价能力吗?——来自中国股市的实证

Fama-French五因子模型首次系统性地将反映公司基本面的两大核心要素——盈利和投资提炼为定价因子,并在美国市场得到验证。那么在中国股市中盈利因素和投资因素是否可以更好地反映股票价格?基于此,本文研究选取1999年至2017年沪深A股月度收益数据进行实证分析。研究表明,我国股市存在显著的盈利效应,与美国股市不同,在我国股市中投资无益于解释股票收益,刻画企业成长的价值因子则至关重要。该研究进一步充实了资本资产定价领域的相关文献,为中国股票定价提供实证证据。     

Fama五因子模型在中国创业板市场适用性的实证研究

本文选取我国创业板上市公司数据,采用Fama五因子模型进行实证研究。结果表明,规模效应、账面市值比效应、盈利效应和投资效应显著;并且,该模型优于三因子模型,但解释能力仍然相对有限。原因在于,我国创业板公司信息披露不完善、投资主体不成熟和监管不到位,从而导致定价效率低下。对此,本文提出了相应的对策建议。     

FF三因子模型在上海A股市场实证分析

FF三因子模型是资本资产定价的重要模型,自提出以来受到了学界多方面的支持与挑战。本文针对该模型在上海A股市场的适用性进行了实证分析。结果表明：市场超额收益率因子高度显著,市值（Size）因子次之,最后是账面市值比（BE/ME）因子,FF三因子模型在上海A股市场基本是适用的。     

FF三因子模型在上海A股市场实证分析

FF三因子模型是资本资产定价的重要模型,自提出以来受到了学界多方面的支持与挑战。本文针对该模型在上海A股市场的适用性进行了实证分析。结果表明:市场超额收益率因子高度显著,市值(Size)因子次之,最后是账面市值比(BE/ME)因子,FF三因子模型在上海A股市场基本是适用的。     

上市公司资产增速对股票回报率的影响 ——基于A股的因子检验

针对实证研究中的投资因子之谜,本文探究上市公司的资产增速对收益率的影响,以2003—2018年A股3631只为样本,构建资产增速因子,检验其对超额收益的解释能力.通过对资产增速进行单变量排序,构建了5个资产组合;结合规模或账面市值比进行双重排序,分别构建了15个资产组合.研究发现:组合持有收益率和资产增速间有较为明显的负相关性,构造的风险中性投资组合有比较稳定的正收益;和FF三因子模型相比,资产增速因子并没有提供显著的超额收益,预示着不同因子间潜在的相互替代性;Fama-MacBeth回归分析结果中资产增速的系数始终为负,但并不显著.     

资产定价理论文献综述

本文介绍了不同时期资产定价模型的主要形式和理论思想,从最初的资本资产定价模型到后来三因子模型,假设条件不断的放松而得到更符合实际的资产定价模型。     

基于ARCH族模型的上海股票市场波动性特征研究

一、引言资本资产定价理论已成为现代金融理论中颇为活跃的一个分支,对资产选择和资产定价的需要催生了对金融市场波动性的研究。很多年来,经济学家一直在努力构建合适的数学模型以捕获金融市场的波动性特征,这其中最成功的当属ARCH族模型。ARCH模型最早是由Engle于1982年提出,     

Asset liquidity,business risk,and beta

Extant literature posits that because of leverage, equity beta estimates from a single factor capital asset pricing model based on an equity-only market index are biased. We show analytically that this leverage bias is intimately related to the firm's asset structure per se, the firm's asset liquidity (i.e., cash holdings) and business risk. This is mainly because riskless cash holdings and risky real assets jointly determine the relevant risk for asset pricing. We empirically confirm that asset liquidity and business risk can marginally explain the leverage bias in the cross-section of stocks returns.     

劳动杠杆的定价效力:来自中国A股市场的证据

本文选取中国沪深A股2002年至2018年的季度数据,从公司层面考察了劳动杠杆(由劳动力成本粘性特征导致的企业利润变化率大于产出变化率的经济现象,可理解为不考虑固定成本时经营杠杆的特殊表现形式)对股票截面收益率的定价效力。结果发现:(1)劳动杠杆对截面收益率具有显著负向影响,具体表现为高劳动杠杆公司的收益率低于低劳动杠杆公司的收益率;(2)该影响在控制了公司特征后依然显著存在;(3)该影响在不同经济周期下表现不同:在经济下行期,负向定价效力更为明显。进一步地,本文通过生产率冲击和工资冲击这两个风险来源探究了劳动杠杆的作用机制。结果表明,劳动杠杆一方面通过生产率冲击产生显著正向影响,另一方面通过工资冲击产生显著负向影响。但后者的影响程度显著强于前者,两者的相对重要性取决于上市公司的技术水平。相关结果能够为公司应对劳动力成本上升、理解劳动杠杆的定价机制及相关投资策略的制定和风险管理等提供经验支持。     

Equilibrium asset pricing: with non-Gaussian factors and exponential utilities

We analyse the equilibrium asset pricing implications for an economy with single period return exposures to explicit non-Gaussian systematic factors, that may be both skewed and long-tailed, and Gaussian idiosyncratic components. Investors maximize expected exponential utility and equilibrium factor prices are shown to reflect exponentially tilted prices for non-Gaussian factor risk exposures. It is shown that these prices may be directly estimated from the univariate probability law of the factor exposure, given an estimate of average risk aversion in the economy. In addition, a residual form of the capital asset pricing model continues to hold and prices the idiosyncratic or Gaussian risks. The theory is illustrated on data for the US economy using independent components analysis to identify the factors and the variance gamma model to describe the probability law of the non-Gaussian factors. It is shown that the residual CAPM accounts for no more than 1% of the pricing of risky assets, while the exponentially tilted systematic factor risk exposures account for the bulk of risky asset pricing.     

Leverage constraints and asset prices: Insights from mutual fund risk taking

Prior theory suggests that time variation in the degree to which leverage constraints bind affects the pricing kernel. We propose a measure for this leverage constraint tightness by inverting the argument that constrained investors tilt their portfolios to riskier assets. We show that the average market beta of actively managed mutual funds—intermediaries facing leverage restrictions—captures their desire for leverage and thus the tightness of constraints. Consistent with theory, it strongly predicts returns of the betting-against-beta portfolio, and is a priced risk factor in the cross-section of mutual funds and stocks. Funds with low exposure to the factor outperform high-exposure funds by 5% annually, and for stocks this difference reaches 7%. Our results show that the tightness of leverage constraints has important implications for asset prices.     

Equilibrium "Anomalies"

Many empirical “anomalies” are actually consistent with the single beta capital asset pricing model if the empiricist utilizes an equity‐only proxy for the true market portfolio. Equity betas estimated against this particular inefficient proxy will be understated, with the error increasing with the firm's leverage. Thus, firm‐specific variables that correlate with leverage (such as book‐to‐market and size) will appear to explain returns after controlling for proxy beta simply because they capture the missing beta risk. Loadings on portfolios formed on relative leverage and relative distress completely subsume the powers of the Fama and French (1993) returns to small minus big market capitalization (SMB) portfolios and returns to high minus low book‐to‐market (HML) portfolios factors in explaining cross‐sectional returns.     

THE ARBITRAGE PRICING THEORY AND COST-OF-CAPITAL ESTIMATION: THE CASE OF ELECTRIC UTILITIES

Capital asset pricing model (CAPM) and alternative arbitrage pricing theory (APT) methodologies are used to estimate the cost of capital for a sample of electric utilities. The statistical factors APT method is found to produce significantly different estimates depending on the number of factors specified and the set of firms factor analyzed. The use of macroeconomic factors is explored, and it is shown that this methodology has advantages over the statistical factors APT and the market model.     

Liquidity and asset pricing: Evidence from the Hong Kong stock market

This study investigates the role of liquidity in pricing stock returns in the Hong Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced.     

中国股票市场风险溢价研究

本文通过综合资产定价理论和实证文献研究结论,对1997年到2009年中国股市A股股票的风险溢价的截面差异作了详尽的实证研究。我们构造25个投资组合作为检验资产,进行Fama-MacBeth两步回归法,建立了基于市场风险溢价,账面市值比,盈利股价比,现金流股价比,投资资本比,工业增加值变化率以及回购利率和期限利差的八因素模型。我们的主要发现有以下三点:一是相对于Fama-French三因素模型,我们模型的实证解释力有显著提高;二是与过去的文献不同,我们发现回购利率和期限利差等债市指标对股市风险溢价的截面数据有显著解释能力;三是与基于投资的资产定价理论一致,我们发现投资比率和现金流股价比能显著反映我国股市的风险溢价。     

Margin Requirements and the Security Market Line

Between 1934 and 1974, the Federal Reserve changed the initial margin requirement for the U.S. stock market 22 times. I use this variation to show that investors' leverage constraints affect the pricing of risk. Consistent with earlier theoretical predictions, I find that tighter leverage constraints result in a flatter relation between betas and expected returns. My results provide strong empirical support for the idea that the constraints investors face may help explain the empirical failure of the capital asset pricing model.