中国大宗商品期货市场定价机制研究

大宗商品期货市场是我国资本市场的重要组成部分,其定价有效性关系到投资者套期保值和价格发现等功能的实现。本文对国际前沿研究中常用的定价因子进行全面系统梳理,并对这些因子对我国商品期货合约收益率的解释和预测能力进行检验。在此基础上,本文构建了适用于我国大宗商品期货市场的包含市场、基差以及基差动量的三因子定价模型。进一步研究表明,基于大宗商品存储理论和现货存货数据构建的投资组合收益率可以被本文三因子模型有效解释,验证了经典的存储理论在我国的适用性。此外,本文对基差与基差动量两个重要因子的经济学意义进行了阐释。本文研究为进一步厘清大宗商品期货市场定价机制提供了一定参考。     

收益率差异视角下我国股票流动性测度指标的比较研究

在传统资产定价模型中依次引入换手率、成交金额、Amihud非流动性比率三种流动性度量指标,构造出改进后的Fama三因子模型,通过Fama-Macbeth两阶段回归的方法来探讨我国A股市场流动性溢价效应以及三种流动性度量指标的不同表现;然后,采用分位数回归的方法进一步检验三种流动性指标各自的适用范围。研究表明:中国A股市场存在较为显著的流动性风险溢价现象;不同的流动性指标与股票收益率之间的关系不同,换手率适合在低收益率情况下流动性的测度,Amihud非流动性比率更适合在中高收益率情况下流动性的测度,而成交金额指标未能通过检验,表现相对较差。     

影响股票收益的基本面因子略探——基于中小板上市公司的实证分析

选取公司规模、股价、股东人数变动、换手率、每股盈余、净资产收益率、毛利率、市盈率相对盈利增长比率及市净率等基本面因子作为影响股票收益的自变量,利用中小板上市公司2010年7月—2016年7月的季度数据进行了实证分析。结果表明:规模、股价、股东人数变动、换手率及毛利率五个指标对股票收益有显著影响;单从系数上看,规模的影响最大,其次分别依次是毛利率、换手率、价格、股东人数变动。采用简单赋值的方法,对这5个指标进行赋值,并选出得分靠前的股票进行等额投资,可以获得不错的收益;其中最优组合的阈值为N=16,该组合自2010年来获得了36.34%的年化收益率及2.6%的回撤深度。     

基于行为金融理论的A股市场投资策略研究

本文在对行为金融理论介绍基础上,揭示出在非理性行为时有发生的我国证券市场,借鉴行为金融理论进行投资操作具有重要的现实意义。之后,参考Jegadeesh＆Titman（1993）等的方法,利用A股主板市场数据按股票收益率、换手率、市盈率的选股标准在研究期内选择赢家组合和输家组合。考察其在持有期的超额收益率,分析各种投资策略的盈利性,以期寻找适用于我国A股市场的投资策略。     

卖空限制与收益可预测性——A股融资融券制度的证据

基于A股市场融资和融券余额的巨大差距,本文拓展了Hong et al.(2016)的理论模型,在融券端和融资端分别找到了影响股票收益率的变量:融券比率(融券余额/流通市值)和融资回补天数(融资比率/日均换手率)。进一步,本文利用组合价差法和Fama-MacBeth横截面回归法,实证检验了A股市场中融券比率与融资回补天数解释和预测股票收益率的能力。实证结果表明,在存在融券限制条件下,融券比率相比融券回补天数(融券比率/日均换手率)能更好地代表套利者对股票价格高估程度的看法,根据融券比率构建的等权重多空组合能带来月均1.58%的显著收益;而由于融资约束相对较少,融资回补天数相比融资比率(融资余额/流通市值)能更好地代表套利者对股票价格低估程度的看法,根据融资回补天数构建的等权重多空组合能带来月均1.28%的显著收益。实证结果与本文存在融券数量限制下的理论模型相符,且该收益率不能被多因子模型和常规股票特征所解释。     

卖空限制与收益可预测性——A股融资融券制度的证据

基于A股市场融资和融券余额的巨大差距,本文拓展了Hong et al.(2016)的理论模型,在融券端和融资端分别找到了影响股票收益率的变量:融券比率(融券余额/流通市值)和融资回补天数(融资比率/日均换手率)。进一步,本文利用组合价差法和Fama-MacBeth横截面回归法,实证检验了A股市场中融券比率与融资回补天数解释和预测股票收益率的能力。实证结果表明,在存在融券限制条件下,融券比率相比融券回补天数(融券比率/日均换手率)能更好地代表套利者对股票价格高估程度的看法,根据融券比率构建的等权重多空组合能带来月均1.58%的显著收益;而由于融资约束相对较少,融资回补天数相比融资比率(融资余额/流通市值)能更好地代表套利者对股票价格低估程度的看法,根据融资回补天数构建的等权重多空组合能带来月均1.28%的显著收益。实证结果与本文存在融券数量限制下的理论模型相符,且该收益率不能被多因子模型和常规股票特征所解释。     

单因素资本资产定价模型在中国市场的应用研究

根据单因素资本资产定价模型（CAPM）模型，股票的β值与期望收益率呈正比例关系，β值为通常收益率的解释因素。本文从统计学角度阐述了模型和回归方程之间的关系，应用回归分析中的相关理论解决模型参数估计和模型检验等问题，并对一些统计指标作出有实际意义的解释。我们主要运用中国股市牛市期间和熊市期间的数据研究β值和未来30个交易日后的收益率之间的关系。实证结果表明，牛市期间收益率随β值的增大总体上呈现上升趋势，熊市期间收益率随β值的增大总体上呈现下降趋势。这说明β值是决定未来收益的一个风险因素，投资者可以依据β值对未来收益从而决定投资那只股票（组合）做出判断。但是两者的相关关系不强，后期线性趋势不好，因此投资时还要考虑其它一些风险因素，比如涨面／市值比、市盈率等等。     

投资者关注与股市收益 ——基于股吧数据挖掘的实证分析

采用股吧发帖量衡量个股投资者关注度,并构建了市场整体的关注度指标以及关注度风险因子,通过对市场整体以及异质性股票的实证检验,结果发现:(1)总体关注度指标只对当期市场收益率有影响,对未来预测能力的影响并不显著,同时投机性较强的股票对关注度指标的敏感性更高;(2)在控制了现有定价因子(MKT、SMB、HML、MOM、ILLIQ)后,关注度因子仍然能够获得显著的正阿拉法收益;(3)多因子回归结果显示关注度因子对投资组合收益率有显著的解释能力,说明关注度因子能够为现有的定价模型带来增量信息.总体来看,采用个股关注度简单加权的市场总体关注度指标并不能对市场收益率进行有效预测,但是采用分组排序法构造的关注度因子却对市场收益率具有较强的解释能力.     

影响个股收益率的因素分析

本文通过对中国股票市场的实证分析，验证了除个股的β值之外，公司的流通股数量，公司股票的市盈率，个股收益率与市场收益率的相关系数等因素都对个股的收益率有一定的解释作用，由于我国股票市场定价机制不甚完善，这些因素对收益率的解释作用甚至强于β值。但是在这些因素受到有效控制的情况下，β值依然有一定的解释作用，这说明我国股票市场仍然是理性市场，但是，在公司财务，投资估价等领域运用CAPM和ATP理论时必须注意进行相应的修正。     

中国银行股收益率的影响因素分析

众多研究表明,股票的收益率与横截面水平上许多因素相关。基于之前学者对中国市场上股票预期收益的多因素分析,本文从更微观的角度对银行股板块进行因素分析,运用的主要方法有:Fama和French在1992年提出的关于股票收益率与账面市值比、股本总值的关系的分组模型;Fama和MacBeth在1973年提出的回归模型,再结合其他关于研究股票收益率的多因素模型分析的研究成果,考虑账面市值比、市盈率倒数、股本总值等对银行股收益率的影响。经研究发现,随着个股市值(市场规模)的扩大,收益率先增后减;而换手率与收益率的关系是显著的正相关关系。     

Tracking down distress risk

This paper shows that exposure to aggregate distress risk is the underlying source of the premiums for the Fama-French size (SMB) and value (HML) factors. Using a unique data set of aggregate business failures of both private and public firms from 1926 to 1997, I build portfolios that track news about future firm failures. These tracking portfolios optimally hedge aggregate distress risk and earn a Capital Asset Pricing Model (CAPM) alpha of approximately −4% a year. Both HML and SMB predict changes in future failure rates. Small stocks have lower returns than large stocks and value stocks have lower returns than growth stocks when the market expects an increase in future failure rates. Finally, a two-factor model with the market and the tracking portfolio for aggregate distress as factors does as well as the Fama-French three-factor model in pricing the 25 size and book-to-market sorted portfolios.     

Possible explanations of no-synergy mergers and small firm effect by the Generalized Capital Asset Pricing Model

The Sharpe-Lintner Capital Asset Pricing Model (CAPM) and the General Capital Asset Pricing Model (GCAPM) suggested by Levy (1978), Merton (1987), and Markowitz (1989) are compared and analyzed. Under the GCAPM we obtain the following main results: 1) the value additivity principle breaks down, which explains mergers and acquisitions; 2) beyond a certain limit, the profit from additional merger is negative; and 3) in a GCAPM equilibrium, small firms earn an abnormal profit in comparison to what is predicted by the CAPM. These results, which are indeed observed in the market, are fully consistent with the GCAPM, but are in contradiction to the CAPM.     

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.     

Die Ermittlung von Risikozuschlägen bei der Zeitwertbewertung von Verpflichtungen aus Schadenversicherungsgeschäften

The IASB proposes fair value accounting of insurance liabilities in the new IFRS on insurance contracts. These liabilities are not systematically traded in markets. Therefore the estimation of a fair value is only possible by simulating a market transaction. This simulation can be carried out by using financial models like the Capital Asset Pricing Model and the Economic Capital Model. In order to determine the fair value it has to be tested if those models can realistically calculate the insurance risk of the liabilities. This includes analysing the nature and extent of risk measurement as well as the assumptions the models are based on. The particular problem of the Capital Asset Pricing Model consists in measuring the risk by betas. An insurance beta can only be determined by relating it to other directly measurable betas. Those relationships can only be developed by putting forward special assumptions which increases the likelihood of a subjective valuation. The Economic Capital Model on the opposite is able to measure the insurance risk. The analysis of the models is carried out under simplified assumptions. Therefore it remains to be proven that the Economic Capital Model can also handle a more specific view of the insurance risk.     

The likelihood ratio test statistic of mean-variance efficiency without a riskless asset

The question whether a given porfolio is mean-variance efficient is a basic problem of investment analysis. Mean-variance efficiency is also the basis of the Capital Asset Pricing Model. This paper presents the explicit form of the likelihood ratio test of the hypothesis that a given portfolio, or a particular market index, is ex-ante mean-variance efficient in the case where there is no riskless asset. Geometric relations are illustrated to provide intuition about the constrained maximum likelihood estimators and the test statistic, and two simple economic interpretations of the test are given.     

Arbitrage Pricing Theory and Utility Stock Returns

This paper presents some new evidence that Arbitrage Pricing Theory may lead to different and better estimates of expected return than the Capital Asset Pricing Model, particularly in the case of utility stock returns. Results for monthly portfolio returns for 1971–1979 lead to the conclusion that regulators should not adopt the single-factor risk approach of the CAPM as the principal measure of risk, but give greater weight to APT, whose multiple factors provide a better indication of asset risk and a better estimate of expected return.     

Yes,The APT Is Testable

The Arbitrage Pricing Theory (APT) has been proposed as an alternative to the mean-variance Capital Asset Pricing Model (CAPM). This paper considers the testability of the APT and points out the irrelevance for testing of the approximation error. We refute Shanken's objections, including his assertion that Roll's critique of the CAPM is applicable to the APT. We also explain the testability of the APT on subsets, and we explore the relationship between the APT and the CAPM.     

THE COST OF EQUITY UNDER THE AUSTRALIAN DIVIDEND IMPUTATION TAX SYSTEM

This paper derives a Capital Asset Pricing Model (“CAPM”) in the context of the Australian dividend imputation tax system. The effect of dividend imputation on the estimated risk premium is discussed. The structure and implications of the derived CAPM are also examined.     

ECONOMIES OF SCOPE,SYNERGY, AND THE CAPM

The purpose of this paper is to integrate the multiproduct theory of the firm into financial theories. The relationship between economies of scope and synergy in conglomerate mergers is formally established in the context of the Capital Asset Pricing Model. It also is shown that a competitive market for corporate control would compete these benefits away.