USE4_Empirical_Notes

use model

Model Insight

The Barra US Equity Model (USE4) Empirical Notes

Yang Liu

Jose Menchero

D. J. Orr

Jun Wang

September 2011

http://wendang.chazidian.com

use model

Model Insight USE4 Empirical Notes September 2011

Contents

1. Introduction ........................................................................... 4

1.1. Model Highlights.............................................................................................. 4 2. Methodology Highlights ........................................................ 5

2.1. Optimization Bias Adjustment ....................................................................... 5

2.2. Volatility Regime Adjustment ......................................................................... 5

2.3. Country Factor.................................................................................................. 6

2.4. Specific Risk Model with Bayesian Shrinkage ............................................... 6 3. Factor Structure Overview .................................................... 7

3.1. Estimation Universe......................................................................................... 7

3.2. Industry Factors ............................................................................................... 7

3.3. Multiple Industry Exposures ........................................................................ 13

3.4. Style Factors .................................................................................................. 15

3.5. Performance of Select Factors..................................................................... 17 4. Model Characteristics and Properties ................................ 22

4.1. Country and Industry Factors ...................................................................... 22

4.2. Style Factors .................................................................................................. 25

4.3. Explanatory Power ....................................................................................... 27

4.4. Cross-Sectional Dispersion ........................................................................... 28

4.5. Specific Risk .................................................................................................... 32 5. Forecasting Accuracy........................................................... 34

5.1. Overview of Testing Methodology ............................................................. 34

5.2. Backtesting Results ....................................................................................... 37 6. Conclusion ........................................................................... 50

Appendix A: Descriptors by Style Factor ................................. 51

Beta ........................................................................................................................... 51

Momentum .............................................................................................................. 51

Size ............................................................................................................................. 51

Earnings Yield ........................................................................................................... 52

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Model Insight USE4 Empirical Notes September 2011

Residual Volatility ..................................................................................................... 52

Growth ...................................................................................................................... 53

Dividend Yield ........................................................................................................... 53

Book-to-Price ............................................................................................................ 53

Leverage .................................................................................................................... 54

Liquidity ..................................................................................................................... 55

Non-linear Size .......................................................................................................... 55

Non-linear Beta ........................................................................................................ 55

Appendix B: Decomposing RMS Returns ................................ 56

Appendix C: Review of Bias Statistics ...................................... 57

C1. Single-Window Bias Statistics .......................................................................... 57

C2. Rolling-Window Bias Statistics ......................................................................... 58

REFERENCES ............................................................................. 61

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Model Insight USE4 Empirical Notes September 2011

1. Introduction

1.1. Model Highlights

This document provides empirical results and analysis for the new Barra US Equity Model (USE4). These notes include extensive information on factor structure, commentary on the performance of select factors, an analysis of the explanatory power of the model, and an examination of the statistical

significance of the factors. Furthermore, these notes also include a thorough side-by-side comparison of the forecasting accuracy of the USE4 Model and the USE3 Model, its predecessor. The methodological details underpinning the USE4 Model may be found in the companion document: USE4 Methodology Notes, described by Menchero, Orr, and Wang (2011).

Briefly, the main advances of USE4 are:

? An innovative Optimization Bias Adjustment that improves risk forecasts for optimized portfolios by reducing the effects of sampling error on the factor covariance matrix

? A Volatility Regime Adjustment designed to calibrate factor volatilities and specific risk forecasts to current market levels

? The introduction of a country factor to separate the pure industry effect from the overall market and provide timelier correlation forecasts

? A new specific risk model based on daily asset-level specific returns

? A Bayesian adjustment technique to reduce specific risk biases due to sampling error

? A uniform responsiveness for factor and specific components, providing greater stability in sources of portfolio risk

? A set of multiple industry exposures based on the Global Industry Classification Standard (GICS®) ? An independent validation of production code through a double-blind development process to assure consistency and fidelity between research code and production code

? A daily update for all components of the model

The USE4 Model is offered in short-term (USE4S) and long-term (USE4L) versions. The two versions have identical factor exposures and factor returns, but differ in their factor covariance matrices and specific risk forecasts. The USE4S Model is designed to be more responsive and provide more accurate forecasts at a monthly prediction horizon. The USE4L model is designed for longer-term investors willing to trade some degree of accuracy for greater stability in risk forecasts. MSCI Portfolio Management Analytics © 2011 MSCI Inc. All rights reserved. Please refer to the disclaimer at the end of this document 4 of 62 http://wendang.chazidian.com

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Model Insight USE4 Empirical Notes September 2011

2. Methodology Highlights

2.1. Optimization Bias Adjustment

One significant bias of risk models is the tendency to underpredict the risk of optimized portfolios, as demonstrated empirically by Muller (1993). More recently, Shepard (2009) derived an analytic result for the magnitude of the bias, showing that the underforecasting becomes increasingly severe as the

number of factors grows relative to the number of time periods used to estimate the factor covariance matrix. The basic source of this bias is estimation error. Namely, spurious correlations may cause certain stocks to appear as good hedges in-sample, while these hedges fail to perform as effectively out-of-sample.

An important innovation in the USE4 Model is the identification of portfolios that capture these biases and to devise a procedure for correcting these biases directly within the factor covariance matrix. As shown by Menchero, Wang, and Orr (2011), the eigenfactors of the sample covariance matrix are systematically biased. More specifically, the sample covariance matrix tends to tends to underpredict the risk of low-volatility eigenfactors, while overpredicting the risk of high-volatility eigenfactors. Furthermore, removing the biases of the eigenfactors essentially removes the biases of optimized portfolios.

In the context of the USE4 Model, eigenfactors represent portfolios of the original pure factors. The eigenfactor portfolios, however, are special in the sense that they are mutually uncorrelated. Also note that the number of eigenfactors equals the number of pure factors within the model.

As described in the USE4 Methodology Notes, we estimate the biases of the eigenfactors by Monte Carlo simulation. We then adjust the predicted volatilities of the eigenfactors to correct for these biases. This procedure has the benefit of building the corrections directly into the factor covariance matrix, while fully preserving the meaning and intuition of the pure factors.

2.2. Volatility Regime Adjustment

Another major source of risk model bias is due to the fact that volatilities are not stable over time, a characteristic known as non-stationarity. Since risk models must look backward to make predictions about the future, they exhibit a tendency to underpredict risk in times of rising volatility, and to overpredict risk in times of falling volatility.

Another important innovation in the USE4 Model is the introduction of a Volatility Regime Adjustment for estimating factor volatilities. As described in the USE4 Methodology Notes, the Volatility Regime Adjustment relies on the notion of a cross-sectional bias statistic, which may be interpreted as an instantaneous measure of risk model bias for that particular day. By taking a weighted average of this quantity over a suitable interval, the non-stationarity bias can be significantly reduced.

Just as factor volatilities are not stable across time, the same holds for specific risk. In the USE4 Model, we apply the same Volatility Regime Adjustment technique for specific risk. We estimate the adjustment by computing the cross-sectional bias statistic for the specific returns.

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