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FTSE BIRR

Edwin Burmeister Research Professor of Economics Emeritus, Duke University

Commonwealth Professor of Economics Emeritus, University of Virginia Former President, BIRR Portfolio Analysis, Inc.

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FTSE Russell is not an investment firm and this presentation is not advice about any investment activity. None of the information in this presentation or reference to a FTSE Russell index constitutes an offer to buy or sell, or a promotion of a security. This presentation is solely for informational purposes. Accordingly, nothing contained in this presentation is intended to constitute legal, tax, securities, or investment advice, nor an opinion regarding the appropriateness of making any investment through our indexes. Views expressed by are subject to change. These views do not necessarily reflect the opinion of FTSE Russell or London Stock Exchange Group plc.

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Contents

1 The problem: Inadvertent Risk

2 A solution

3 About FTSE BIRR

4 How is BIRR used?

5 What type of investor uses BIRR?

6 User (Market) profile

7 Alternative approaches

8 The BIRR advantage

9 Some illustrations

10 Summary

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The problem: Inadvertent risk taking

• Risk is directly related to return • Investment managers are always concerned about the riskiness

of their portfolios • But they often fail to recognize certain risks that are inherent to

their portfolios and therefore make unintended bets • This results in unexpected performance in the face of

unanticipated macroeconomic events • Underperformance of retail stocks when there is an unexpected

downturn in the business cycle is one example

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The Solution

Arbitrage Pricing Theory (APT) combined with decades of economic research identified five factors that drive portfolio volatility:

• Unexpected changes in inflation

• Unexpected changes in the business cycle

• Unexpected changes in the time horizons of investors

• Unexpected changes in investor confidence

• Market Timing (Market Sentiment)

These five factors explain the majority of risk in a diversified equity portfolio (as measured by R2)

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About FTSE BIRR

BIRR enables managers to identify and control asset exposures to economy-wide surprises

• Every asset has a unique Risk Exposure Profile to such surprises (5 betas measuring sensitivities to each factor)

BIRR is designed for risk management, performance attribution, and portfolio construction

• BIRR allows managers to understand their risks and potential returns as a result of these exposures

BIRR offers portfolio construction, optimization, and performance attribution software

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How is BIRR used?

• To understand the macroeconomic risks inherent in a portfolio • To explain past returns • To identify what risks a manager is taking • To quantify manager skill • To design portfolios with targeted risk exposures relative to

a benchmark • To construct portfolios with specific risk profiles

• Tilted towards stocks with high confidence sensitivities

• Tilted towards stocks with low inflation sensitivities

• Tilted toward stocks with large sensitivities to business cycle risk

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What type of investors use BIRR?

Risk Managers

• Risk Measurement

Chief Investment Officers

• Asset Allocation

• Manager Selection

• Risk Measurement

• Benchmarking

Traders

• Strategy development and risk measurement

Portfolio Managers

• Portfolio construction, index tracking, rebalancing, risk measurement

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Usage breakdown* % Industry revenue generated per client segment

63%

15%

11%

6% 5%

Asset Managers Broker Dealers Hedge Funds Asset Owners Other

*Publicly disclosed data indicates a current user base of 2,230 firms for BIRR-type products, with multiple users at many firms.

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Alternative approaches

Asset-specific financial characteristics models (Micro factors) • Uses multiple accounting variables to compute measures such as P/E, P/B,

and Size to explain (attribute) risk

• Models can include 70+ variables when industry sectors are included

Statistical models • Methods such as Principal Components Analysis identify factors

• While effective at identifying the sources of risk, it doesn’t provide an intuitive framework for what the factors are (factors are unnamed) and these statistical factors change over time

Value at risk • Often assumes a lognormal distribution to show potential maximum losses at

given probabilities

• Value at Risk can be employed as a complement to the BIRR approach

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The BIRR advantage

• Provides an intuitive explanation of risk

“Simple, but not simple-minded”

• Provides a superior explanation of stock returns with R2 values that are almost always substantially higher than other approaches

• Better isolates the stock-specific effects that result from a manager’s stock selection process

• Identifies the attributes that point to a potential for superior future performance

• Combines micro factors (firm-specific financial characteristics) with macroeconomic factors for a more comprehensive understanding of returns

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A few illustrations

• Risk Exposure Profiles for FTSE 100, FTSE USA, and FTSE Asia Pacific

• Evidence for global integration • Cumulative wealth comparisons • Value at Risk • Optimal selection of FTSE Indexes

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Risk exposure profiles for FTSE 100, FTSE Asia Pacific, and FTSE USA

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Confidence Time Horizon Inflation Bus.Cycle Market Timing CAPM Beta

FTSE 100 FTSE Asia Pacific FTSE USA

Risk Exposures: FTSE 100, FTSE Asia Pacific, FTSE USA Period 2004.07 to 2010.06

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Evidence for global integration: FTSE 100

Dependent Variable: FTSE100N Method: Least Squares Date: 07/28/10 Time: 20:41 Sample: 2004:07 2010:06 Included observations: 72 Variable Coefficient Std. Error t-Statistic Prob. C 0.001690 0.002784 0.607169 0.5458 G1 1.257544 0.121832 10.32194 0.0000 G2 0.220893 0.173302 1.274614 0.2069 G3 -0.066240 0.068855 -0.962018 0.3396 G4 2.004944 0.535216 3.746048 0.0004 G5 0.827376 0.076096 10.87279 0.0000

R-squared 0.823013 Mean dependent var 0.001075 Adjusted R-squared 0.809604 S.D. dependent var 0.053398 S.E. of regression 0.023300 Akaike info criterion -4.601098 Sum squared resid 0.035830 Schwarz criterion -4.411376 Log likelihood 171.6395 Hannan-Quinn criter. -4.525569 F-statistic 61.38154 Durbin-Watson stat 2.150590 Prob (F-statistic) 0.000000

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Evidence for global integration: FTSE Asia-Pacific Dependent Variable: FTSE ASIA PACIFIC Method: Least Squares Date: 07/28/10 Time: 19:55 Sample: 2004:07 2010:06 Included observations: 72 Variable Coefficient Std. Error t-Statistic Prob. C 0.003652 0.003207 1.138903 0.2589 G1 1.296858 0.140344 9.240538 0.0000 G2 0.462538 0.199635 2.316919 0.0236 G3 -0.047632 0.079317 -0.600521 0.5502 G4 1.077131 0.616542 1.747054 0.0853 G5 0.890753 0.087659 10.16159 0.0000 R-squared 0.778209 Mean dependent var 0.003474 Adjusted R-squared 0.761407 S.D. dependent var 0.054948 S.E. of regression 0.026840 Akaike info criterion -4.318187 Sum squared resid 0.047546 Schwarz criterion -4.128464 Log likelihood 161.4547 Hannan-Quinn criter. -4.242658 F-statistic 46.31548 Durbin-Watson stat 1.982501 Prob (F-statistic) 0.000000

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Cumulative wealth comparisons

0.00

25.00

50.00

75.00

100.00

125.00

150.00

175.00

200.00

225.00

04 05 06 07 08 09 10

FTSE USA FTSE Asia Pacific FTSE 100

Cumulative Wealth: FTSE 100, FTSE Asia Pacific, FTSE USA Period 2004.06 to 2010.06

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VaR comparisons

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0 Mos. 2 4 6 8 10 12 Mos.

FTSE 100 FTSE Asia Pacific FTSE USA

Value at Risk At 95%: FTSE 100, FTSE Asia Pacific, FTSE USA Sample period 2004.07 to 2010.06

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Optimal FTSE index mix

Index Weight (%) E[Return] Std. Deviation

FTSE Asia Pacific 90.859 6.6127 19.0540

FTSE USA 9.141 2.0639 15.7071

FTSE Europe 0.000 5.8358 20.6967

FTSE 100 0.000 3.7378 18.5511

Optimal Mix 100.000 6.2005 18.5500

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Efficient FTSE index frontier: An illustration

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In summary: Why BIRR?

It outperforms alternative approaches

• It produces higher R2 values, better accounting for the behavior of stock returns than alternative risk models

• It better isolates the specific performance resulting from manager stock selection

It’s based on sound economic theory (APT) Competing models are flawed

• Accounting rules differ across firms and aren’t backed by theory, negating the value of accounting-based models

• Statistical models are difficult to interpret and change over time

BIRR is backed by theory, produces superior results, is competitively priced, and is easy to use

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About FTSE Russell

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