INDUSTRIAL ENGINEERING APPLICATIONS IN FINANCIAL ASSET MANAGEMENT:CAVEATS

CAVEATS

Shortcomings of Mean-Variance Analysis

One must be cognizant that the MV approach has several important shortcomings that limit its effectiveness. First, model solutions are sometimes very sensitive to changes in the inputs. Second, the number of assets that can be included is generally bounded. Otherwise, collinearity problems can result that produce unstable allocations and extreme asset switching in the optimal portfolios. Third, the asset allocation is only as good as forecasts of prospective returns, risk, and correlation. Inaccurate forecasts produce very poorly performing portfolios. The first two limitations can be addressed through skillful model specification. The third requires that one have superlative forecasting ability.

A common mistake committed by naive users of MV analysis is to use recent returns, risk, and correlation as predictors of the future. Portfolios produced using such linear extrapolation methods normally exhibit poor performance. Table 2 shows historical returns and risk for various asset classes over the last decade. A variety of extraneous market developments, shocks, and one-time events produced these results. Rest assured that future time paths will not closely resemble those of the 1990s. For this reason, while one cannot ignore history, extending past performance into the future is a poor technique.

Dangers of Extrapolating from History

As an example of the extrapolation fallacy, consider portfolio performance over the last two decades. If one constructed an efficient portfolio in 1990 based on the 1980s history, large allocations would have been made to international equities. This is primarily due to the fact that Japanese stocks produced the best returns in the world up to 1989. Yet in the 1990s, Japanese equities fell by more than 50% from their 1989 peak, and the best asset allocation would have been to U.S. equities. Using the 1980s history to construct MV portfolios would have produced dismal portfolio returns (Table 3).

Empirical work by Chopra and Ziembra (1993) demonstrated that the most critical aspect of constructing optimal portfolios is the return forecast. For this reason, a shortcut employed by some practitioners is to concentrate on the return forecast and use historical risk and correlation to construct optimum portfolios. This may prove satisfactory because correlations and risk are more stable than returns and are therefore more easily predicted. However, this line of attack may be ineffective if return forecasts substantially deviate from history and are combined with historical risk and corre- lations. In this case, the optimal allocations can skew overwhelmingly to the high return assets Whatever method is used to obtain forecasts, once the optimum portfolio is determined, the manager can momentarily relax and wait. Of course, actual outcomes will seldom match expectations.

No investment manager possesses perfect foresight. Errors in forecasting returns, risk, and correlation will produce errors in portfolio composition. Obviously, managers with the best forecasting ability will reap superior results.

Asset Selection

If a manager does not forecast extremely well, it is possible to produce superior investment perform- ance via asset selection. That is, choosing an exceptional array of candidate assets, can enhance portfolio returns due to diversification benefits that other managers miss.

For example, consider a simple portfolio of U.S. equities and bonds. Normally managers with the best forecasts will achieve better performance than other managers investing in the same assets. But another manager, who may not forecast U.S. equity and bond returns extremely well, can outperform by allocating funds to assets such as international equities and bonds. These assets possess different returns, risks, and correlations with each other and U.S. assets. Their inclusion shifts the efficient frontier upward beyond that resulting when only U.S. stocks and bonds are considered.

The primary distinction between asset selection and asset allocation is that the thought processes differ. In asset selection, a manager focuses on defining the candidate universe broadly. In asset allocation, assets are typically viewed as given and the effort is on forecast accuracy.

A deep knowledge of markets and investment possibilities is necessary to identify the broadest possible asset universe. A manager who incorporates new assets with enticing features has a larger information set than a manager laboring in a narrowly defined world. This is why astute investment managers are constantly searching for new assets—their goal is to gain an edge over competitors by shifting the efficient frontier outward (Figure 4). Neglecting the opportunity to employ an ubiquitous asset domain is a common failure of beginners who rely on black box MV solutions they do not fully understand.