知情交易与中国股市博彩溢价

本文发现中国股市存在博彩（投机）溢价,且无法为Fama—French三因子模型解释。尽管在组合构造期内,博彩型股票存在显著溢价,但组合的超额收益会迅速消失,并未给投资者（或投机者）带来持续的财富效应。基于知情交易概率测度,我们进一步发现中国股市的知情交易者驱动（或引发）了博彩型股票溢价;在组合构造后的月份,并没有明显的知情交易者存在,这意味着知情交易者在基于私人信息获利之后,那些随后进入市场的投资者（动量交易者）无法获得超额收益。     

中国股市价值反转投资策略有效性实证研究

本文以中国深沪A股股票市场为考察对象 ,分析了价值反转投资策略的有效性。作者通过实证分析发现 :在中国深沪A股股票市场上 ,以帐面价值与市场价值比 (B M)、B M GS等指标构造的价值反转投资策略可以产生显著的超额收益率 ,并且其显著程度因持有期不同而不同。接着 ,作者利用CAPM模型、Fama French三因素模型并引入了协偏度 (coskewness)和协峰度 (cokurtosis) ,构造出多风险因子模型来解释价值反转投资策略超额收益率。我们发现 :在经过传统风险因素调整后 ,价值反转投资策略效果依然明显 ;CAPM模型无法解释价值反转投资策略超额收益率 ;Fama French三因素模型对价值反转投资策略超额收益率的解释能力最为显著 ,但对于有些价值投资策略 ,在Fama French三因素基础上加上协偏度和协峰度因子后 ,模型的解释能力有所提高     

中国股票型基金季度内不可观测行为研究

现有信息披露机制决定了季度内基金投资行为不可观测,这导致了基金及投资经理的委托—代理行为,强化了基金短期炒作和投资风格轮动动机。本文借鉴Kacperczyk、Sialm和Zheng（2006）定义的＂收益差值＂指标,用以衡量基金的活跃交易收益及隐含成本,采用拓展的Fama—French三因子模型对30只股票型基金面板数据进行实证分析,结果显示＂收益差值＂指标对基金超额收益贡献明显,进一步采用市场周期虚拟变量说明基金交易活跃程度呈现周期性趋同变化,这种周期性变化加剧市场波动,其内在根源在于现代金融理论中资产定价理论假设与投资者行为的背离。     

我国封闭式基金的三因素定价模型实证研究

本文首先介绍了Fama和French的三因素模型构建方法,而后分别运用CAPM模型和三因素模型对我国封闭式基金截面平均收益进行解释,以分析和比较CAPM模型和三因素模型在国内封闭式基金定价问题上的适用性。本文的研究结果表明三因素模型明显优于CAPM模型。     

证券投资基金投资风格对心理账户的门限效应

针对国内证券投资基金的风格漂移现象,本文引入Hansen(1999)面板门限计量经济模型,对Fama—French三因子模型进行估计,以揭示基金投资风格漂移与投资者心理账户之间的非线性关系。发现基金投资决策并非依赖于自身的当前收益,而是依赖于持仓调整后市场平均收益,这是基金投资风格周期性漂移和趋同的主要原因。基金风格在市场收益为-0.073和0.182处发生普遍调整在高于0.182或低于-0.073时,基金都更偏好大盘股和高账面市值比股票。这一结果与前景理论中的心理账户效应相符,而并非完全是羊群效应导致。     

风险或偏误？企业投资视角下股票价值效应的成因研究

本文以企业投资与股票收益模式之间的关系为切入点,就股票市场上价值效应成因的投资角度解释进行了分析,并运用GRS时间序列回归检验和广义矩横截面估计的方法对该理论解释进行了实证检验。研究发现,以投资资本比为指标所构建的投资因子．包含着与Fama—French三因素模型中账面市值比因子相近的信息,并且能够与账面市值比因子一样好地解释价值效应。该发现为撤资代价及反周期风险价格是导致价值效应的动因这一理论提供了经验证据,也为经济决策者更好地理解我国企业投资、经济周期和金融市场波动性之间的系统相关性提供了依据。     

基金的市场时机把握能力研究

本文对我国证券投资基金的市场时机把握能力进行了实证研究。在研究设计上 ,本文以中信指数作为市场基准指数 ,使用 3种基于CAPM基础的模型和 3种基于Fama French三因素模型基础的改进模型 ,以相互印证结果的可靠性。同时 ,本文也采用了非参数检验方法 ,对基金年报的有关内容和投资组合公告中的持仓信息进行分析 ,以使结论更具有可信性。研究结果表明 ,整体而言 ,我国基金缺乏市场时机把握能力 ,但具有一定的证券选择能力 ,不过其对基金收益的贡献并不显著。     

资本资产定价模型应用研究——对上海机电股份贝塔系数的测算

经典的资本资产定价模型中所指的风险,按照是否可以利用多元化投资加以驱除区分为系统风险和非系统风险,而系统风险的大小主要靠β值进行测量。文章以上海机电股份为研究对象,运用图形分析和最小二乘法探究上海机电的预期收益率与风险之间的关系,并预测其β系数。实证结果说明:上海机电股份的风险与其收益之间显示出简单的线性关系,并且为正的线性相关关系;进一步通过预测其β系数发现该股票的风险比整体市场投资组合的风险要高,相应的其收益也大于整体市场投资组合的平均收益率。     

企业ESG表现对股票投资收益的影响研究——基于中国A股上市企业的经验证据

近年来,在政府政策与我国市场的共同作用下,综合考虑环境保护（E）、社会责任（S）和企业治理（G）的ESG投资理念受到投资者的广泛推崇。文章基于ESG投资理念,采用2016—2022年沪深A股数据和华证投资ESG评级数据,根据ESG评级将股票样本分为领先型、平均型和落后型三个组合。构建CAPM模型、Fama-French三因子模型与包含ESG因子在内的四因子模型,研究依据企业ESG表现对股票投资收益的影响。结果表明：投资者投资ESG表现良好的企业可以获得超额收益。     

流动性溢价理论研究进展

流动性溢价理论是在对资本资产定价模型(cAPM)的挑战和质疑的背景下形成的。继Sharpe(1964)，Lintner(1965)，Mossin(1966)推导出CAPM模型，Black(1972)又相继提出了零8的CAPM模型。尽管早期的一些实证研究支持了CAPM，但近年来已有许多实证研究对CAPM的有效性提出质疑。Fama和French(1992)应用横截面数据对CAPM的有效性进行研究，结果表明CAPM对预期收益不具有横截面解释能力，而公司规模(SIZE)、面值与市值比(BM比率)是影响预期收益的重要因素，     

Testing Fama–French’s new five-factor asset pricing model: evidence from robust instruments

Fama and French (FF, 2015) propose a five-factor asset pricing model that captures size, value, profitability and investment patterns. The primary purpose here is to further investigate this new model using an improved GMM-based robust instrumental variables technique. A further purpose is to explore the relationship among the FF factors and the Pástor–Stambaugh (PS, 2003) liquidity factor. We conclude that except for the market factor, all of the factors including liquidity are not significant at even the 5% level using our GMM approach for almost all of the FF 12 sectors.     

A panel data robust instrumental variable approach: a test of the new Fama-French five-factor model

Fama and French (FF, 2015) propose a new five-factor asset pricing model that adds profitability and investment patterns to the market, size and value variables used in FF (1992). Our purpose is to investigate this new model using an improved generalized method of moments (GMM)-based robust instrumental variables technique in a fixed-effects panel data framework. To test for measurement errors, we use a modified Hausman artificial regression. We also examine an augmented FF six-factor model that includes the Pástor–Stambaugh (PS, 2003) liquidity factor. Using the FF dataset, our GMM-based panel data approach leads us to conclude that the only consistently significant factor is the market factor.     

Interest rate,size and book-to-market effects in Australian financial firms

The Fama–French three-factor model (1993) has been extensively used to study the pricing of nonfinancial stocks. This study provides the first examination of the pricing of Australian financial stocks using the Fama–French framework. The four-factor model (market, size, book-to-market and momentum) augmented with the level, slope and curvature of the interest rate term structure is used to examine the pricing of Australian financial stocks. The interest rate factors have not been previously considered for pricing Australian stocks within the Fama–French framework. Consistent with US evidence, we use a system-based estimation to show that the size and book-to-market factors are not priced in the cross section of the equity returns of Australian financial stocks. Momentum and term spread are priced in the equity returns of both financial and nonfinancial stocks. These findings are robust to the inclusion of control variables such as default spread, the inflation rate and a dummy variable for the global financial crisis.     

Drivers of expected returns in Istanbul stock exchange: Fama–French factors and coskewness

We investigate the impact of coskewness on the variation of portfolio excess returns in Istanbul Stock Exchange (ISE) over the period July 1999 to December 2005. We form portfolios according to size, industry, size and book-to-market ratio, momentum and coskewness and compare alternative asset pricing models. The traditional capital asset pricing model (CAPM) and the three-factor model of Fama and French are tested in the multivariate testing procedure of Gibbons–Ross–Shanken (1989). Coskewness is introduced as a fourth factor and its incremental effect over CAPM and Fama–French factors is examined both in multivariate tests and in cross-sectional regressions. The findings reveal that coskewness is able to explain the size premium in ISE. Hence, the basic two-moment CAPM without the coskewness factor would underestimate the expected return of size portfolios. Multivariate test results indicate that coskewness reduces the pricing bias, albeit insignificantly. Cross-sectional analysis uncovers that coskewness has a significant additional explanatory power over CAPM, especially for size and industry portfolios. However, coskewness does not have a significant incremental explanatory power over Fama–French factors in ISE.     

Which is the best: A comparison of asset pricing factor models in Chinese mutual fund industry

Factor models are commonly used in estimating risk-adjusted fund performance. We compare the commonly used factor models in empirical asset pricing studies and find that Fama and French (2015) five-factor model outperforms other models in the Chinese mutual fund industry and in most fund segments. The factor models we tested are more effective in explaining the return of index funds than other types. Meanwhile, we also find that the capital asset pricing model (CAPM) better controls the estimated alpha dispersion than other models. Though most multifactor models including Carhart (1997) have higher R-squared than CAPM, the cross-sectional differences between them are not statistically significant.     

What does high-dimensional factor analysis tell us about risk factors in the Australian stock market?

Estimates of the cost of equity are often sensitive to the specification of the linear factor model used in their construction. In this article, we use techniques developed for high-dimensional factor models to consider the identity of systematic risk factors in the Australian equities market. Our results support the use of neither the Capital Asset Pricing Model (CAPM) nor the Fama and French model, although they provide an explanation for the empirical performance of these models. Many other model specifications are also rejected. We find that a single-factor model with an equal-weighted market index is the best model for estimating the cost of equity in the Australian context.     

The effect of industry classification on analyst following and the properties of their earnings forecasts

Using a comprehensive data set, we compare four broadly available industry classification schemes (Standard Industrial Classification (SIC), North American Industry Classification System (NAICS), Fama–French classification (FF) and Global Industry Classification Standard (GICS)) in their effectiveness to group analysts and their earnings forecast properties. We demonstrate the advantage of the GICS to be consistent across different forecasting properties and across different groups of firms. Our results suggest that GICS should be utilized in research designs, either in the primary analysis or as a necessary corroboration.     

中国股票市场的二因素模型

参照Fama和French鉴定美国股票市场三因素模型的方法，研究中国股票市场的资产定价模型。在中国股票市场，账面市场效应并不存在，但与规模相关的某种系统风险因素在股票定价中起到了重要作用。由此鉴定出中国股票定价的二因素模型。     

Adapting and testing the Fama and French model,with some variations of company characteristics

We examine whether the Fama and French (1992) (F&F) model can be adapted to become a more versatile and flexible tool, capable of incorporating variations of company characteristics in a more dynamic form. For this, the risk factors are reconstructed at the end of each reading of monthly data. We argue that, over time, the evaluation of a company may change as a result of variations in its market price, size or book price, and we are aware that the F&F model does not accurately reflect these dynamics. Our results show that the adapted model is able to capture the behaviour of a greater number of stocks than the original F&F model and risk factors are more significant when building them through our procedure. In addition, we carry out these adaptations during a period of instability in financial markets.     

The Cross–section of Risk Premia in the Italian Stock Market

Our analysis compares multi–factor models with Italian stock market data for the period 1990–2000. The first, the simple CAPM, is the relevant benchmark because of its simplicity. The second, the extended Fama–French model (including the momentum portfolio), is the best candidate for substituting the one–factor model. The third is a multi–factor model including sectors; and the fourth is a multi–factor model including the change in short–term interest rates as an extra factor. The results of our research are mildly positive. The Fama–French multi–factor model behaves rather well in time series tests. However, in the cross–section, the average premia are not significantly different from zero, supporting the idea that they are not able to explain the cross–section of returns in the Italian market. Moreover, there is weak evidence that the factor portfolios have predictive power for macroeconomic variables characterizing the state of the economy. What may explain the results? There are two main components: first is the presumably large size of shocks to returns in the Italian case, which makes it difficult to explore the relation among expected returns; second is the presence of extra factors which are not accounted for in our analysis. (J.E.L.: G11, G12).