复旦大学 研究生投资学讲义 CHPT14- The CAPM ---test
授课教师张宗新复旦大学金融研究院ppt课件

授课教师:张宗新
第五章
因素模型与套利定价理论(APT)
第一节 指数模型
一、因素模型的产生 1、资本资产定价模型(CAPM)在实际应用的两大 问题:
(1)要计算风险市场组合,计算量非常巨大。 (2)证券市场线实际上只考虑了风险市场组合的 预期回报率对证券或证券组合的期望收益率的影 响,即把市场风险(系统风险)全部集中地表现 在一个因素中,并没有将影响证券收益的宏观经 济变量(如国民收入、利率、通货膨胀率、能源 价格等)考虑在内。
R i ta i b i1 F 1 t b i2 F 2 t b iF k k te it
五、指数模型估计与因子识别
(一)模型估计方法
1.时间序列法:因素的值是已知的,而敏感度需要 估计,且对每个证券的分析是多个时期逐个进行的。
2.横截面法:敏感度是已知的,而因素的值需要估 计,且对每一组证券的分析是每一时期逐个进行。
GDP 增长率(%) 通货膨胀率(%)
1
5.7
1.1
2
6.4
4.4
3
7.9
4.4
4
7.0
4.6
5
5.1
6.1
6
2.9
3.1
公司i 的收益率(%)
14.3 19.1 23.4 15.6 9.3 13.0
单因素模型回归:证券回报率的构成
rt
已实现的收益
公司特质带来的收益
13%
et 3.2%
共同因素下证券的收益
Co(vi,j)0 Co(Fv,i)0 Co(Fv,F)F 2
Co(Rvi,Rj)Co(aivbiFi,aj bjFj) Co(bivFi,bjFj) Co(bivF,bjF)bibjF 2
投资学第章资本资产定价模型剖析ppt课件

与指数模型的期望形式:
E(ri ) rf i i[E(rM ) rf ] 可知二者差别在于,CAPM认为所有的i都为0。 市场模型:rf E(ri ) i[rf E(rM )] ei
如果CAPM有效,则市场模型等同于指数模型。
E(Ri ) kE(Ci ) ( L1 L2 L3 )
其中,E(Ci )为期望流动性代价; k为所有资产的调整后的平均持有期
为平均市场流动性的市场风险溢价净值 为系统性市场风险敏感度, L1、 L 2、 L3为流动性 E(RM CM ),CM 表示市场平均流动性溢价。
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流动性的三要素
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9.3 CAPM符合实际吗?
CAPM的实用性取决于证券分析。 9.3.1 CAPM能否检验 ▪ 规范方法与实证方法 ▪ 实证检验的两类 错误(数据、统计方法) 9.3.2 实证检验质疑CAPM
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9.3 CAPM符合实际吗?
9.3.3CAPM的经济性与有效性 ▪ CAPM在公平定价领域的广泛应用 ▪ CAPM被普遍接受的原因 9.3.4 投资行业与CAPM的有效性 投资公司更趋向于支持CAPM
39
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9.4 计量经济学和期望收益-贝塔关系
▪ 计量经济方法可能是引起CAPM被错误拒 绝的原因
▪ 相关改进
➢ 用广义最小二乘法处理残差相关性 ➢ 时变方差模型ARCH
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9.5 CAPM的拓展形式
两种思路: ▪ 假定的放宽 ▪ 投资者心理特征的应用
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9.5.1 零模型
有效前沿的三大性质:
▪ 两种有效前沿上的资产组合组成的任意资产组合仍在有 效前沿上
23
9.2.2 指数模型和已实现收益
复旦大学经济学院2013~2014学年第二学期期末考试试卷

复旦大学经济学院2013~2014学年第二学期期末考试试卷A卷答案(共4页)课程名称:投资学原理课程代码: ECON130031.01开课院系:经济学院考试形式:闭卷姓名:学号:专业:一、选择题(单选或多选:4’*5=20’)1、B2、A C3、B D4、ABD5、CDF二、名词(4’*5=20’)1、股权风险溢价股权风险溢价ERP(equity risk premium,ERP)是指市场投资组合或具有市场平均风险的股票收益率与无风险收益率的差额。
从这个定义可看出:一是市场平均股票收益率是投资者在市场参与投资活动的预期“门槛”,若当期收益率低于平均收益时,理性投资者会放弃它而选择更高收益的投资;二是市场平均收益率是一种事前的预期收益率,这意味着事前预期与事后值之间可能存在差异。
2、资本配置线正是由于无风险资产引入,才可以形成无风险资产和风险资产之间的资本配置线CAL(capital allocation line,CAL),但是CAL仅仅为风险资产和无风险组合的“一般搭配”,并非“最优搭配”;而市场组合M与无风险资产构成全部资产组合的集合形成资本市场线(CML),是与“有效边界”相切的资本配置线,是一种“最优的”资本配置线。
3、市盈率增长因子(PEG)市盈率增长因子(PEG)是对P/E静态性缺陷的重要补充。
PEG是将一只股票的市盈率除以该公司的成长性。
其中,用估计盈利增长率除市盈率可以测算公司成长的速度,这就是著名的预期市盈率增长因子(Prospective PEG)。
市盈率增长因子越低,表示公司的发展潜力越大,公司的潜在价值也就越高。
市盈率/公司利润增长率,大于1说明估值高;小于1说明便宜。
4、动量效应投资者行为的研究表明,股票上涨得越多,就有也越多的投资者认为它继续上涨,因而股价的上涨存在一种自我实现机制,即存在动量效应(momentum effect)。
在股价的正反馈机制中,噪声交易这对股价上涨起到推动作用,而明智的专业投资者将从噪声交易者的追逐动能效应策略的过程中获取收益。
《投资学》第六章CAPM模型剖析.

t时期收益率(而不是期望收益率)
E( F)是因素的期
– i 表示因素值为0时证券i的期望收益率,叫零因子
– bi 表示证券i对因素的敏感度,叫因素载荷
– eit 表示证券i的剩余收益率,叫随机误差项
– Ft 表示 t时期因素值
●单因素模型认为:证券的收益率受到某一个因素的影响。 (是市场因素而不是个别因素)
• 分离定理的价值之二:投资产品本身风险的大小 不再是影响投资决策的重要因素。不管风险偏好 如何,你都可以选择任何投资产品。
资本市场线(CML)的图形
E(rc)
E(rM)
M
rf
0
σM
σc
二、假定前提得出的推论3
• 推论3: • 资本市场线(CML) :无风险资产组合与
市场资产组合M相连的直线。(即最优的资 本配置线)
i
单因素模型
• CAPM用于表示事先的或是期望的收益,而在现实人们只 能观察到事后的或可实现的收益。为了完成从期望收益 到可实现收益的转变,使证券的收益-风险分析具有实用 价值,提出了单因素模型。
• 单因素模型: Rit =ai +bi Ft +eit
根据单因素模型,得到证券 E(ri) =ai +b
因素模型与均衡
1、因素模型不是一个资产定价的均衡模型;
• 比较 E(ri)=ai +biE( F)
•
E(ri)=rf +βi[E(rM)-rf ]
2、一定条件下,因素模型也可以是均衡模型
当取因素为市场组合的收益率,即F=rM 则,通过公式变形整理得到,
ai =(1-βi)rf bi =βi
举例说明
• 举例: 如果紫光的股票没有进入最优风险资产组合中,市场资
投资学讲义(DOC 246页)

投资学讲义免费的商务管理资料平台第一章財務管理概論恐龍蛋遊戲軟體設計公司,前一陣子推出「水滸傳」網路遊戲軟體。
這個公司成立迄今已有三年,已推出四個頗受市場歡迎的遊戲軟體,公司預計本年度的營收將達2億元。
目前,這家公司資本額為2千萬元,此外,為了籌措營運所需資金,公司準備將自有的廠房及倉庫抵押給銀行以取得4千萬元擔保放款。
這家公司還計畫進一步將營業項目擴展到商用以及教育應用軟體的開發設計。
為因應這些擴充計劃,公司財務副總經理發現現有的資金籌措方式將不足以應付未來公司對資金的需求。
更嚴重的是,公司將立即面臨短期營運現金不足的問題。
這家公司所面臨的幾個問題也正是財務管理這門課所關注的課題:(1)這家公司的未來投資策略應是什麼?(即,這家公司為何要進入商用及教育應用軟體的開發與生產?)如何評估並選擇最佳的投資計劃?(即,錢怎麼投資?)(2)一旦選定投資計畫,公司如何籌措投資計畫所需的資金?(即,錢從那裡來?)(3)這家公司日常營運需要多少週轉金?(即,如何管理現金?)企業選擇最適的投資策略前,必須先確立企業的經營目標。
選定後,經營目標的達成須借助投資計畫的評估,選擇以及執行。
投資計畫的評估,選擇就是投資決策的範疇。
投資決策(或稱資本預算決策,capital budgeting decisions),就是將公司資本支出預算用於購置公司營運所需的固定資產(如:廠房、機器設備)以及無形資產(如:商譽、商標及專利權),而公司經營目標就是追求所購置的資產創造最大的價值且必須大於資本支出。
亦即,若企業投資決策的目標是追求所購置的資產所創造的淨價值最大,則企業經營績效勢必取決於各項投資計畫能為股東創造多少的價值?故企業營運最終的目標就應是追求企業所有人所增加的財富極大。
1. 何謂財務管理?假設朱一決定自行創業成立公司生產CPU專用的散熱風扇。
公司設立前,朱一必須聘請會計、財務以及採購管理人員負責採購生產原物料以及財務、人事管理,找到合適的廠房、機器設備,並招募到足夠的工人從事生產。
复旦大学 研究生投资学讲义 CHPT13- Factor pricing model--CAPM

Chapter 13 Factor pricing modelFan LongzhenIntroduction•The consumption-based model as a complete answer to most asset pricing question in principle, does not work well in practice;•This observation motivates effects to tie the discount factor m to other data;•Linear factor pricing models are most popular models of this sort in finance;•They dominate discrete-time empirical work.Factor pricing models•Factor pricing models replace the consumption-based expression for marginal utility growth with a linear model of the form•The key question: what should one use for factors 11'+++=t t f b a m 1+t fCapital asset pricing model (CAPM)•CAPM is the model , is the wealth portfolio return.•Credited Sharpe (1964) and Linterner (1965), is the first, most famous, and so far widely used model in asset pricing.•Theoretically, a and b are determined to price any two assets, such as market portfolio and risk free asset.•Empirically, we pick a,b to best price larger cross section of assets;•We don’t have good data, even a good empirical definition for wealth portfolio, it is often deputed by a stock index;•We derive it from discount factor model by •(1)two-periods, exponential utility, and normal returns; •(2) infinite horizon, quadratic utility, and normal returns;•(3) log utility •(4) by seeing several derivations, you can see how one assumption can be traded for another. For example, the CAPM does not require normal distributions, if one is willing to swallow quadratic utility instead.wbR a m +=w RExponential utility, Normal distributions•We present a model with consumption only in the last period, utility is•If consumption is normally distributed, we have •Investor has initial wealth w, which invest in a set of risk-free assets with return and a set of risky assets paying return R.•Let y denote the mount of wealth w invested in each asset, the budget constraint is •Plugging the first constraint into the utility function, we obtain][)]([c eE c U E α−−=2/)()(22))((c c E e c U E σαα+−−=f R f y y w Ry R y c ff f ''+=+=yy R E y R y f f e c U E Σ++−−='2/)]('[2))((ααExponential utility, Normal distributions--continued •Applying the formula to market return itself, we have•The model ties price of market risk to the risk aversion coefficient.)()(2wf w R R R E ασ=−Quadratic value function, Dynamicprogramming-continued•(1) the value function only depends on wealth. If other variables enter the value function, m would depend on other variables. The ICAPM, allows other variables in the value function, and obtain more factors.•(other variable can enter the function, so long as they do not affect marginal utility value of wealth.)•(2) the value function is quadratic, we wanted the marginal value function is linear.Why is the value function quadratic•Good economists are unhappy about a utility function that have wealth in it.•Suppose investors last forever, and have the standard sort of utility function •Investors start with wealth which earns a random return andhave no other source of income;•Suppose further that interest rate are constant and stock returns are iid over time.•Define the value function as the maximized value of the utility function in this environment•∑∞=+=0)(j j t jt c u E U β0w w R {}11;);(..)()(''110,...,...,,max11==−==++∞=+∑++vtt tw tt t w t t j j t jt w w c c t R R c W R W t s c u E W V t t t t ωωβ,Why is the value function quadratic--continued•Without the assumption of no labor income, a constant interest rate, and I.I.d returns come in, the value maybe depend on the environment.For example, if D/P indicates returns would be high for a while, the investor might be happier and have a high value.•Value functions allow you to express an infinite-period problem as a two-period problem. Breaking up the maximization into the first period and the remaining periods, as follows•Or{}{}⎪⎭⎪⎬⎫⎪⎩⎪⎨⎧⎥⎦⎤⎢⎣⎡+=∑∞=+++++++11,...,,...,,,)()()(maxmax2121jjtjtwwccttwctcuEEcuWVttttttββ{}{})()()(1,max++=tttwctWVEcuWVttβLinearizing any model •Goal of linear model: derive variables that drive the discount factor; derive a linear relation between discount factor and these variables;•Following gives three standard tricks to obtain a linear model;Linearizing any model-Talyorexpansion•From •We have)(11++=ttfgm))())((('))((11111+++++−+≈ttttttttfEffEgfEgmComments on the CAPM and ICAPM•Is CAPM conditional or unconditional? Are the parameter changes as conditional information changes ?•The two-period quadratic utility-based deviation results in a conditional CAPM, since the parameters change over time.•The log utility CAPM hold both conditionally or unconditionally.•Should CAPM price options? The quadratic utility CAPM and log utility CAPM should apply to all payoffs.•Why linearize? Why not take the log utility model whichshould price any asset? Turn it into cannot price no normally distributed payoff and must be applied at short horizons.•it is simple to use regression to estimate in CAPM.•Now with GMM approach, nolinear discount factor model is easy to estimate.tt b a ,W R m /1=W t t t t R b a m 11+++=γβ,Comments on the CAPM and ICAPM---continued •Identify the factors: it is a art!。
投资学课件5 CAPM

SML
bM= 1.0
b
12
SML表达式
b= SML斜率 = =
[COV(ri,rm)] / m2 E(rm) - rf market risk premium
SML = rf + b[E(rm) - rf]
Betam = [Cov (ri,rm)] / m2
= m2 / m2 = 1 b体现的是具体的某个证券对市场组合风险的贡献度。b大于
by = .6 E(ry) = .03 + .6(.08) = .078 or 7.8%
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计算结果图示
E(r)
Rx=13% Rm=11% Ry=7.8%
3%
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.6 1.0 1.25
by
bx
»By
SML .08
b
15
不均衡举例:图示
E(r)
15% Rm=11%
SML
E(r)
E(rM) rf
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CML M
m
9
CML的斜率与市场风险溢价
M = Market portfolio rf = Risk free rate E(rM) - rf = Market risk premium
E(rM) - rf = Market price of risk
M
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CML与SML
与CML一样,SML也可以用于描述有效组合的收益率与风 险的关系。
rf=3%
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1.0 1.25
b
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不均衡举例:说明
假设一个b为1.25的证券能提供15%的预期收益. 根据SML,在均衡状态下,b为1.25的证券只能提
复旦大学本科生课件 - 投资学

投资学
第 1章
19
金融体系:间接融资、直接融资与资金、证券的流动
资金
金融中介 机构
资金
储蓄 证券
贷款 资金
贷款人(储蓄者 投资者): 1、个人与家庭 2、企业 3、政府 4、外国投资者
证券 资金
金融市场
证券 资金
借款人(筹资者、 发行者): 1、个人与家庭 2、企业 3、政府 4、外国投资者
20
投资学
金融资产的价值与其物质形态没有任何关系:债券可
能并不比印制债券的纸张更值钱。 整个社会财富的总量与金融资产数量无关,金融资产 不是社会财富的代表。
投资学 第 1章 5
金融资产在经济中的作用
1.
2.
3.
消费的时间安排( Consumption Timing):个 人现实消费与现实收入分离,将高收入期的购 买力转移到低收入期。 风险的分配( Allocation of Risk):风险来源 于实际资产,风险在全社会的分散和优化配置。 问题:金融工具能否减少总体经济的风险? 所有权与经营权分离( Separation of Ownership),复合所有权:变不可分割的资 产为可分割的资产。例如:GE的股东有50万, 股东的退出不影响公司的经营。
投资学
第 1章
7
1.2 金融市场
1.2.1 金融市场(Financial market)是金融 资产的交易场所。 合约性质:债券市场、股票市场、期货市 场、期权市场。 期限长短:货币市场和资本市场 功能:初级(一级)市场——发行市场, 二级市场——交易市场。
区别:第一市场、第二市场等
投资学 第 1章 17
1.2.3 金融机构(Financial institutions) (1)金融中介( Financial intermediaries)为 间接融资提供服务,包括:商业银行、保险 公司、投资基金、养老基金等。 (2)证券业(Security industry)为直接融资提 供服务:
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Chapter 14 The CAPM ---Applications and
tests
Fan Longzhen
Predictions and applications •CAPM: in market equilibrium, investors are only rewarded for bearing
the market risk;
•APT: in the absence of arbitrage, investors are only rewarded for bearing the factor risk;
•Applications:
•---professional portfolio managers: evaluating security returns and fund performance
•---corporate manager: capital budgeting decisions.
Testability of CAPM
•The wide acceptance of the CAPM and APT makes it all more important to test their predictions empirically.•How does a product of abstract reasoning hold in reality?•Unfortunately, the predictions of the CAPM and APT are hard to test empirically
•---neither the market portfolio in CAPM nor the risk factor in APT is observable;
•---expected returns are unobservable, and could be time-varying;
•---volatility is not directly observable, and is time-varying.
An ideal test of the CAPM
•In an idea situation, we have the following input:
• 1. Risk-free borrowing/lending rate ;
• 2. Expected returns on the market and on the risky asset ;• 3. The exposure to market risk ; •These input allow us to examine the relation between reward •and risk :• 1. More risk, more reward?• 2. Do they line up?
• 3. What is the reward for a risk exposure of 1?•
4. Zero risk, zero reward?
f R )(M R E )(i R E )
var(/),cov(M i M i R R R =β))((f i R R E −i
β
Testing the linear relation
•Pick a proxy for the market portfolio, and record N monthly returns:
•For the same sample period, collect a sample of I firms, each with N monthly returns:•Construct sample estimates •For , test the linear relation:
•N
t R M t ,...,2,1:=N t and I i R i t ,...,2,1,...,2,1:==i
M i m m βˆ,ˆ,ˆI i ,...,2,1=i
f i R m βγγˆˆ10+=−
Implication of the CAPM and testing
•Implication of CAPM: with •
: zero exposure, zero reward;•: one unit of exposure, the same reward as the market.•With 43 industrial portfolios, the test tells us that this relation does not hold exactly.
•One possibility: our measures of the expected returns are contaminated by noises that are unrelated to the beta’s;
•What we still would like to know:•---on average, is reward related to risk at all? Or not?•---On average, does zero risk results in zero reward? ? Or not?•---on average, does one unit of risk exposure pay market return?•or not i f i R m βγγˆˆ10+=−00=γf M R m
−=ˆ1γ01=γ00=γ%9.5ˆ1=−=f M R m
γ
A summary of the CAPM tests •In general, the test results depend on the sample data, sample periods, statistical approaches, proxy for the market portfolio, ect. But the
following findings remain robust:
•the relation between risk and reward is much flatter than that predicted by the CAPM;
•The risk beta can not even to begin to explain the cross-sectional variation in the expected returns;
•Contrary to the prediction of the CAPM, the intercept is significant different from zero.
Some possible explanations
• 1. Is the stock market index a good proxy for the market portfolio?
•Only 1/3 non-governmental tangible assets are owned by the corporate sector;•Among the corporate assets, only 1/3 are financed by equity
•What are about intangible assets, like human capital;
•What about international markets?
• 2. Measurement error in beta
•Except for the market portfolio, we never observe the true beta;
•To test CAPM, we use estimates for beta, which are measured with errors • 3. Measurement error in expected returns
•We use sample mean as the unobservable expected returns;
•There are noises in our estimates;
•Is the noises are correlated, then we have a statistical problem(error-in-variables)
• 4. Borrowing restrictions
•Fisher black showed that borrowing restrictions might cause low-beta stocks to have higher expected returns than the CAPM predicts.
Going beyond the CAPM •Is beta a good measure of risk exposure? What about the risk
associated with negative skewness?
•Could there be other risk factors?
•Time-varying volatility,
•Time-varying expected returns,
•Time –varying risk aversion,
•And time-varying beta?。