CFA level 1level II 公式表Word文档格式.docx

1、e） Preservation of Confidentiality 4. Duties to Employers a） Loyalty b） Additional Compensation Arrangements c） Responsibilities of Supervisors 5. Investment Analysis, Recommendations, and Action a） Diligence and Reasonable Basis b） Communication with Clients and Prospective Clients. c） Record Reten

2、tion 6. Conflicts of Interest a） Disclosure of Conflicts b） Priority of Transactions c） Referral Fees. 7. Responsibilities as a CFA Institute Member or CFA Candidate a） Conduct as Members and Candidates in the CFA Program b） Reference to CFA Institute, the CFA designation, and the CFA Program. Globa

3、l Investment Performance Standards （GIPS） 1. Compliance Statement:” Insert name of firm has prepared and presented this report in compliance with the Global Investment Performance Standards （GIPS）.” Compliance must be applied on a firm-wide basis. 2. Eight sections: a） Fundamentals of compliance b）

4、Input data c） Calculation methodology d） Composite construction e） Disclosures f） Presentation and reporting g） Real estate - 1 - h） Private equity QUANTITATIVE METHODS 1. Time Value of Money Basics a） Future Value （FV）: amount to which investment grows after one or more compounding periods. Nb） FV,

5、PV（1，I/Y） c） Present Value （PV）: current value of some future cash flow Nd） PV,FV/（1，I/Y） e） Annuities: series of equal cash flows that occur at evenly spaced intervals over time. f） Ordinary annuity: cash flow at end-of-time period. g） Annuities due: cash flow at beginning-of-time period. h） Perpet

6、uities: annuities with an infinite life i） PV,PMT/（I/Y） perpetuity2. Means a） Arithmetic mean: sum of all observation values in sample/population, divided by # of observations. b） Geometric mean: used when calculating investment returns over multiple periods or to measure compound growth rates. c） G

7、eometric mean return:1/NR,（1，R），.，（1，R）,1 G1NNHarmonic mean = N，1,Xi,1i，3. Variance and Standard Deviation a） Variance: average of squared deviations from mean. N2X（,）,i2,i1b） Population variance = , NN2（X,X）,i2,1ic） Sample variance = s, n,1d） Standard deviation: square root of variance. 4. Holding

8、Period Return （HRP） ,，PPDPD tt,1ttt,1 RtPPt,1t,1- 2 - 5. Coefficient of Variation a） Coefficient of variation （CV）: express how much dispersion exists relative to mean of a distribution; allows for direct comparison of dispersion across different data sets. CV is calculated by dividing standard devi

9、ation of a distribution by the mean or expected value of the distribution. Sb） CV,X6. Sharpe Ratio a） Sharpe Ratio: measures excess return per unit of risk. r,rpfb） Sharpe Ratio = ,pr,rargptetc） Roys safety first ratio: ,p7. Expected Return/Standard Deviation a） Expected return: E（X）,P（x）x,iiE（X）,P（

10、x）x，P（x）x，？，P（x）x 1122nnb） Probabilistic variance:22（X）,P（x）,x,E（X）,ii 222,P（x）x,E（X），P（x）x,E（X），？，P（x）x,E（X）nn1122c） Standard deviation: take square root of variance. 8. Correlation and Covariance a） Correlation = covariance divided by product of the two standard deviations. COV（R,R）ij, corr（R,R）ij

11、,（R）,（R）ijb） Expected return, variance of 2-stock portfolio:E（R）,wE（R），wE（R） pAABB2222var（R,）,w（R，）,w（R，）2ww,（R,）（R）,（R,R） pAABBABABAB9. Normal Distributions a） Normal distribution is completely described by its mean and variance. 68% of observations fall within ,1, 90% fall within ,1.65, 95% fall w

12、ithin ,1.96, 99% fall within ,2.58, 10. Computing Z-scores a） Z-score: “standardizes” observation from normal distribution; represents # of standard deviations a given observation is from population mean. - 3 - observation,populationmeanx, z,standarddeviation,11. Binomial Models a） Binomial distribu

13、tion: assumes a variable can have one of two values （success/failure） or, in the case of a stock, movements （up/down）. A Binomial Model can describe changes in the value of an asset or portfolio; it can be used to compute its expected value over several periods. 12. Sampling Distribution a） Sampling

14、 distribution: probability distribution of all possible sample statistics computed from a set of equal-size samples randomly drawn from the same population. The sampling distribution of the mean is the distribution of estimates of the mean. 13. Central Limit Theorem a） Central limit theorem: when selecting simple random samples of size n from population with mean and finite variance 2, the sampling distribution of sample mean 、2appr