博迪投资学第九版课件

• M-square increases to 25.53%.
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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 results in a lack of diversification.
• Historical returns, even over long periods, have very limited power to infer expected returns for the 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.
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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 views about relative performance that cannot be used in the TB model.
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Table 27.5 The Optimal Risky Portfolio with the Analysts’ New Forecasts
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Adjusting Forecasts for the Precision of Alpha
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Table 27.4 The Optimal Risky Portfolio with Constraint on the Active Portfolio (wA ≤1)
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Table 27.1 Construction and Properties of the Optimal Risky Portfolio
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Spreadsheet 27.1 Active Portfolio Management
Let’s add these new forecasts to the spreadsheet model and re-calculate Table D.
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Figure 27.1 Rates of Return on the S&P 500 (GSPC) and the Six Stocks
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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 developed.
• Kane, Marcus, and Trippi show that active management fees depend on: 1.the coefficient of risk aversion, 2.the distribution of the squared information ratio in the universe of securities, 3.the precision of the security analysts.
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Table 27.3 The Optimal Risky Portfolio
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Results
• The Sharpe ratio increases to 2.32, a huge risk-adjusted return advantage.
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Figure 27.6 Sensitivity of Black-Litterman Portfolio Performance to Confidence Level
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– Performance increases are very modest. – M-square increases by only 19 basis points.
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Table 27.2 Stock Prices and Analysts’ Target Prices for June 1, 2006
CHAPTER 27
The Theory of Active Portfolio Management
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McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.
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• The Black-Litterman model allows portfolio managers to incorporate complex forecasts (called “views”) into the portfolio construction process.
Hale Waihona Puke Baidu
4. Develop revised (posterior) expectations. 5. Apply portfolio optimization.
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Figure 27.5 Sensitivity of Black-Litterman Portfolio Performance to Confidence Level
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Steps in the Black-Litterman Model
1. Estimate the covariance matrix from recent historical data. 2. Determine a baseline forecast. 3. Integrate the manager’s private views.
• Use the TB model for the management of security analysis with proper adjustment of alpha forecasts.
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Value of Active Management
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Spreadsheet 27.1
• An active portfolio of six stocks is added to the passive market index portfolio. • Table D shows:
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Figure 27.4 Organizational Chart for Portfolio Management
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The Black-Litterman Model
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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
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BL vs. TB
Black-Litterman Model Treynor-Black Model
• Use the BL model for asset allocation. • Views about relative performance are useful even when the degree of confidence is inaccurately estimated.
• How accurate is your forecast? • Regress forecast alphas on actual, realized alphas to adjust alpha for the accuracy of the analysts’ previous forecasts.
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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.
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.