INVESTMENTS 投資學(xué) （博迪BODIE, KANE, MARCUS）Chap027 The Theory of Active Portfolio Management.ppt

1、CHAPTER 27,The Theory of Active Portfolio Management,2,Overview,Treynor-Black model The optimization uses analysts forecasts of superior performance. The model is adjusted for tracking error and for analyst forecast error. Black-Litterman model,3,Table 27.1 Construction and Properties of the Optimal

2、 Risky Portfolio,4,Spreadsheet 27.1 Active Portfolio Management,5,Spreadsheet 27.1,An active portfolio of six stocks is added to the passive market index portfolio. Table D shows: Performance increases are very modest. M-square increases by only 19 basis points.,6,Table 27.2 Stock Prices and Analyst

3、s Target Prices for June 1, 2006,Lets add these new forecasts to the spreadsheet model and re-calculate Table D.,7,Figure 27.1 Rates of Return on the S&P 500 (GSPC) and the Six Stocks,8,Table 27.3 The Optimal Risky Portfolio,9,Results,The Sharpe ratio increases to 2.32, a huge risk-adjusted return a

4、dvantage. M-square increases to 25.53%.,10,Results,Problems: The optimal portfolio calls for extreme long/short positions that may not be feasible for a real-world portfolio manager. The portfolio is too risky and most of the risk is nonsystematic risk. A solution: Restrict extreme positions. This r

5、esults in a lack of diversification.,11,Table 27.4 The Optimal Risky Portfolio with Constraint on the Active Portfolio (wA 1),12,Figure 27.2 Reduced Efficiency when Benchmark is Lowered,Benchmark risk is the standard deviation of the tracking error, TE = RP-RM. Control it by restricting WA.,13,Table

6、 27.5 The Optimal Risky Portfolio with the Analysts New Forecasts,14,Adjusting Forecasts for the Precision of Alpha,How accurate is your forecast? Regress forecast alphas on actual, realized alphas to adjust alpha for the accuracy of the analysts previous forecasts.,15,Figure 27.4 Organizational Cha

7、rt for Portfolio Management,16,The Black-Litterman Model,The Black-Litterman model allows portfolio managers to incorporate complex forecasts (called “views”) into the portfolio construction process.,Historical returns, even over long periods, have very limited power to infer expected returns for th

8、e next month. The business cycle and other macroeconomic variables may be better forecasters of expected returns. Historical variance is a good predictor of expected future variance.,17,Steps in the Black-Litterman Model,Estimate the covariance matrix from recent historical data. Determine a baselin

9、e forecast. Integrate the managers private views.,Develop revised (posterior) expectations. Apply portfolio optimization.,18,Figure 27.5 Sensitivity of Black-Litterman Portfolio Performance to Confidence Level,19,Figure 27.6 Sensitivity of Black-Litterman Portfolio Performance to Confidence Level,20

10、,BL Conclusions,The Black-Litterman (BL) model and the Black-Treynor (TB) model are complements. The models are identical with respect to the optimization process and will chose identical portfolios given identical inputs. The BL model is a generalization of the TB model that allows you to have view

11、s about relative performance that cannot be used in the TB model.,21,BL vs. TB,Black-Litterman Model,Optimal portfolio weights and performance are highly sensitive to the degree of confidence in the views. The validity of the BL model rests largely upon the way in which the confidence about views is

12、 developed.,Treynor-Black Model,TB model is not applied in the field because it results in “wild” portfolio weights. The extreme weights are a consequence of failing to adjust alpha values to reflect forecast precision.,22,BL vs. TB,Black-Litterman Model,Use the BL model for asset allocation. Views

13、about relative performance are useful even when the degree of confidence is inaccurately estimated.,Treynor-Black Model,Use the TB model for the management of security analysis with proper adjustment of alpha forecasts.,23,Value of Active Management,Kane, Marcus, and Trippi show that active manageme

14、nt fees depend on: the coefficient of risk aversion, the distribution of the squared information ratio in the universe of securities, the precision of the security analysts.,24,Table 27.6 M-Square for the Portfolio, Actual Forecasts,25,Table 27.7 M-Square of Simulated Portfolios,26,Concluding Remarks,The gap between theory and practice has been narrowing in recent years.