A Multi-Faceted Representation of Systematic Risk

• By Jeffrey S. Coons; PhD, CFA
• By Christopher J. Cummings; CFA, CFP™

Introduction 

Did you know that the economist Stephen Ross PhD developed a more generalized Modern Portfolio Theory [MPT] model called Arbitrage Pricing Theory (APT)? 

Definition

APT is based upon somewhat less restrictive assumptions than the Capital Asset Pricing Model [CAP-M] and results in the conclusion that there are multiple factors representing systematic risk.  The APT incorporates the fact that different securities react in varying degrees to unexpected changes in systematic factors other than just beta to the market portfolio.

The risk-free return plus the expected return for exposure to each source of systematic risk times the beta coefficient to that risk is what determines the expected rate of return for a given security.

Physician-Investors

An important point for physicians to keep in mind is that the APT focuses on unexpected changes for its systematic risk factors. The financial markets are viewed as a discounting mechanism, with prices established for various securities reflecting investors’ expectations about the future, so any excess return for an expected change will be arbitraged away (i.e., the price of that risk will be bid down to zero). 

For example, market prices already reflect physician and other investors’ expectations about GNP growth, so prices of assets should only react to the extent that GNP growth either exceeds or falls short of expectations (i.e., an unexpected change in GNP growth).

A Rhetorical Interrogative?

And so – we can ask – why do lay investors, medical professionals and their advisors go wrong in making passive asset allocation decisions using MPT?   The problem has less to do with the limitations of CAPM or APT as theories and more to do with how these theories are applied in the real world.

The basic premise behind the various MPT models is that both return and risk measures are the expectations assessed by the investor.   Too often, however, decisions are made based on what investors see in their rear view mirror rather than what lies on the road ahead of them.

Theoretical?

In other words, while modern portfolio theory is geared towards assessing expected future returns and risk, investors and financial professionals all too often simply rely on historical data rather than develop a forecast of expected future returns and risks.

While it is clearly difficult for physicians and all investors to accurately forecast future returns or betas, whether they are for the market as a whole or an individual security, there is no reason to believe that simply using historical data will be any more accurate.  

MPT Shortcomings

One major shortcoming of modern portfolio theory as it is commonly applied today is the fact that historical relationships between different securities are unstable.  And, it would seem that a physician or other healthcare provider should not rely on historical averages to establish a passive asset allocation.  

Of course, the use of unstable historical returns in modern portfolio theories clearly violates the rule-of-thumb related to the dangers of projecting forward historical averages; MPT is nonetheless an important concept for medical professionals to understand as a result of its frequent use by investment professionals. 

Critical Elements of Investing

Furthermore, MPT has helped focus investors on two extremely critical elements of investing that are central to successful investment strategies: 

1. First, MPT offers the first framework for investors to build a diversified portfolio.   
2. Second, the important conclusion that can be drawn from MPT is that diversification does in fact help reduce portfolio risk.

Assessment

MPT approaches are generally consistent with the first investment rule of thumb, “understand and diversify risk to the extent possible.”  

Additionally, the risk/return tradeoff (i.e., higher returns are generally consistent with higher risk) central to MPT based strategies has helped investors recognize that if it looks too good to be true, it probably is. 

Conclusion

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