Illusions of Precision, Completeness and Control: A Case for Simple, Transparent Portfolios via Brandes Institute
The Financial Crisis of 2008/2009 increased plan sponsors’ desire to control risk—and we are still seeing the unfortunate effects. Many approaches adopted to control risk are illusions of risk control. Of particular concern is how sponsors are misapplying tools designed to monitor portfolios, and instead, are relying on them to build portfolios. Portfolio design and reallocation decisions often are now driven by complex, but often incomplete, measurement tools. The premise of these tools assumes greater detailed structuring and monitoring leads to greater control over the generation of risk-adjusted returns. But is the promise of this approach paying off and are the trade-offs of complexity and lack of transparency worth it?
The purpose of this paper is to challenge what I see as an increasingly popular approach to portfolio construction and evaluation that relies on complex, quantitative models. As an alternative, I make a case for simple and transparent portfolios. I will focus as an illustrative example on the misuse of meanvariance optimization and “nine-box” investment models, as well as the elusive search for alpha.
Transparent Portfolios: Mean-Variance Models and the Illusion of Precision
Mean-variance optimization produces precise numbers and predictions. Yet, these precise numbers are based on questionable assumptions of typical investor behavior and problematic investment expectations. There also are practical limitations to implementing a mean-variance model’s recommendations. Let’s explore each of these issues in more detail.
Continued from part one... Q1 hedge fund letters, conference, scoops etc Abrams and his team want to understand the fundamental economics of every opportunity because, "It is easy to tell what has been, and it is easy to tell what is today, but the biggest deal for the investor is to . . . SORRY! Read More
Inconsistent Investor Behavior
The traditional mean-variance model treats all volatility equally; excess returns are as risky as poor returns. It also assumes that investment behavior will be similar for a gain and an equivalent loss. Such behavior clearly is not the case. The work of Daniel Kahneman and Amos Tversky shows that losses are at least twice as influential as gains when making an investment decision.1 Such work has helped spread awareness that assumptions about behavior at the heart of mean-variance models are, at best, incomplete. Some investors, however, remain unconvinced or ignore research results highlighting differences between investment theory and reality.
Inaccurate Investment Assumptions
Whether full variance or semi-variance, models assume log-normal distributions, but actual returns are not normally distributed. Extreme events, like “fat tails,” can skew returns. Plus, the severity and frequency of extreme events can be greater than predicted. Actual monthly U.S. equity returns have been different than forecast by a traditional bell-shaped distribution.2 Exhibit 1, on the following page, shows actual returns (gray line) have been milder and wilder than expectations (tan bars). Note the narrow, higher peak near the median and sharp, upward spikes at the tails.
Also, actual returns tended to have a higher frequency of modest returns, creating periods with a false sense of calm (with a false sense of confidence in skill). While the outliers or fat tail events were far less common, they did great short-term damage—both financially and psychologically. However, volatility fades over time. Exhibit 2 shows that annualized 5-year rolling stock returns were more consistent with expected returns.
In addition, because mean-variance models are linear, they do not account for discontinuous events. That is, there is no way for those models to adequately account for the long stretches of mild returns interrupted by bursts of dramatic swings in the market.
Having multiple asset classes with small allocations (less than 5%-10% of the total portfolio) adds rigidity and complexity to the portfolio—without adding a real benefit. A model may recommend small proportions to particular asset classes (e.g., 3% to private equity or 2% to high yield debt). Yet under normal ranges of risk and correlation, it takes around a 5-10% allocation to an asset class to make an appreciable impact on overall portfolio risk or return.
Furthermore, the benefit in lowering standard deviation falls off precipitously after three asset classes are added to a portfolio (see Exhibit 3).
See full in PDF format here.