The measure of total return for the i^{th} asset is its expected annual return, r_{i},_{ }and the measure of total risk is the annual standard deviation of total return, s _{i}. In order to simplify the example, all systematic risk is assumed to be captured by a single systematic risk factor that is referred to as the market. The amount of systematic risk contained in the i^{th} asset is given by its beta, b _{i}. The only other information that is needed to apply the TreynorBlack model to these four assets are the riskfree rate of return (r_{f}), which is taken to be 5% per annum; the market rate of return (r_{m}), which is taken to be 10% per annum; and the market risk (s _{m}), which is taken to be 20% per annum. Before proceeding with the calculation of optimal portfolio shares, we will notice how the four assets differ. The first asset has both high risk and high return, but is uncorrelated with the market (b _{1}=0). Managers often view such an asset as an ideal addition to a portfolio because its lack of correlation with other assets helps to diversify risk. This observation is true, but as we shall see to a much more limited degree than one might suspect. In contrast, the second asset has a beta of two, characteristic of a highly leveraged asset. Its risk and return are even higher than the first asset, but in the same overall ratio. The final two assets have both lower risk and return than the first two assets, although the ratio of return to risk appears higher than that of the first two assets. Both have a modest beta of 0.5 and the latter of the two has both lower risk and return. For the i^{th} asset, alpha (a _{i}) and the square of specific risk (a _{i }/s ^{2}(e_{i})) can be computed directly from the information above using the following standard formulas (and then substituting the appropriate market parameters): a _{i }=_{ }r_{i} 
(r_{m}r_{f})b _{i} r_{f } and s ^{2}(e_{i})
= s
_{i}^{2}  s _{m}^{2}b^{ }_{i}^{2} Performing these substitutions and then computing the TreynorBlack weights (and the share implied by them) generates the following table:
Notice that Asset 1, the great diversifier, receives the smallest weight in the portfolio. Although its zero beta keeps its alpha high, it does nothing to lessen its high risk, leaving it with a large amount of specific risk and a correspondingly small share of 7.78%. Asset 4 is the greatest beneficiary of the TreynorBlack model, even with the lowest alpha, its very low level of specific risk makes it the favored holding. Except for those rare instances where high return is accompanied by low risk (and such opportunities are frequently limited in quantity, an issue discussed below), the TreynorBlack model tends to favor assets with low risk and low return. Its aversion to high risk/high return assets tends to be greater than one would intuitively think it should be. The basic properties of the portfolios selected by the TreynorBlack model can be seen from this example. First, the relative allocation among assets is independent of the amount of money to be allocated among them because allocations are expressed in terms of shares, not monetary amounts. In addition, adding or removing assets does not change the relative allocation among any of the existing or remaining assets. This property makes the TreynorBlack model suitable for use in decentralized applications. Assets can be partitioned into several groups, and the allocation decision within any individual group can be made without any knowledge of the assets in any other group. Another important property is the stability of the model, its lack of sensitivity to
small changes in the parameters of the model. In particular, all allocations are
"wellbehaved" functions of all the risk and return parameters, so that a small
change in any parameter (or set of parameters) cannot lead to a fundamental change in the
portfolio. As noted at the beginning of this paper, stability does not characterize more
complex portfolio optimization models. Applying TreynorBlack to the EnterpriseBecause of its inherent modularity and modest data requirements, the TreynorBlack model can be extremely valuable as a strategic planning tool for a multiproduct or multidivisional financial enterprise. In this case an "asset" represents a product (or division) within the enterprise and the output of model is used to target the relative share of each product. This section will outline a process for applying TreynorBlack at the enterprise level and the following section will discuss some extensions to the model that can increase its value for this and other applications. The application of the TreynorBlack model at the product level can be viewed as a threestep process that may need to pass through several iterations before it is complete. These steps are:
Riskbased product definition is a necessary first step in the process because the TreynorBlack model assumes that all correlation between products is captured by the systematic risk factors, leaving specific risk to be distributed independently from product to product. The potential for serious problems can be readily seen by considering the example above and adding a fifth asset that is simply a "clone" of one of the other four assets. The insertion of this artificial asset has the effect of doubling the weight in the portfolio of the asset from which it was cloned. Thus, an enterprise with several products that contain virtually identical risks will tend to overload itself with that risk unless it appropriately consolidates the products when applying the TreynorBlack model. Despite its numerous virtues, the TreynorBlack model’s independence assumption has the effect of making the portfolio allocation depend on the way in which assets are defined—a difficulty that more complex models are designed to avoid. The diligence that is required to apply the model properly might be viewed as the price that is paid for its simplicity. The independence requirement for specific risk means that most enterprises cannot use their existing definition of product lines or division as the basis for input into the TreynorBlack model. Nonetheless, merely going through the process of examining a large enterprise’s risk from the perspective of the TreynorBlack model can be enlightening: it is common for a specific type of exposure to be found in a variety of forms throughout the enterprise. One way to approach the process is to create a product matrix where each dimension of the matrix represents a product attribute that affects specific risk. One can then create natural groupings of products starting by selecting products along the most important rows and columns of the matrix. These products groups will then define the "assets" for use in the TreynorBlack model. In general, some groups will be determined by geographic exposure (countries, regions, etc.), some by industry (financial, energy, etc.), and some by traditional product category (consumer, corporate, etc.). Also, the potential for overlap can be addressed by performing multiple runs of the model, one along each relevant dimension of risk so that separate target levels of exposure are set along each dimension. The number of riskbased products (or groups of products) generated by this process will depend on the size and diversity of the enterprise. The partition should be fine enough so that fundamentally different risks appear separately, but not so fine that the duplication problem noted above starts to appear. As a rough guide, 20 to 100 groups tend to be a reasonable amount to work with; however, more or less than this may be appropriate in some circumstances. The second step in the process is to determine the alpha for each of the products. The approach taken in the original TreynorBlack paper is used securities analysis as the basis for determining alpha. Each asset is viewed as a security for which an analyst develops an opinion based on her or his research. This opinion is then converted into an estimate of the excess return. As noted above, Treynor and Black even suggest that existing recommendation scales (Buy/Hold/Sell) can be converted directly to alphas The exercise of determining productlevel alphas is essentially one of uncovering sources of competitive advantage and then estimating how they will contribute to returns in the future. In applying the TreynorBlack model the important thing is not what returns historically have been, but rather what they will be going forward. Existing mechanisms for computing riskadjusted returns may have to be modified or supplemented in order to make them suitable for this application. Of particular concern are those cases where the excess return of a product is determined to be negative, i.e., its return does not exceed its hurdle rate. In the absence of overriding business reasons (relationship building, etc.) to continue the product, this is a clear signal to limit new business in the product and to consider mechanisms for laying off or hedging any existing risk from the product. In those fortunate cases where products closely parallel positions in activelytraded assets, the hurdle rate for a product can be determined using regression analysis on the appropriate time series of historical returns. The statistical estimates of alpha that are generated this way should not be used as inputs to the TreynorBlack model because they are likely to be poor estimates of future returns. Instead, an independent assessment of future returns should be made, and the hurdle rate determined statistically should be netted out to compute an alpha. The third and final step of determining the level of specific risk for each product can also be determined either subjectively or objectively. As noted above, when sufficient time series data relevant to a product are available, the specific risk can be obtained directly from a regression: it is the square root of the residual (or unexplained) variance of the regression. In many cases, however, the estimation of specific risk will not be this easy. Building on the methodology for enterprise risk management developed in Greene and Miller (1996) one can construct a risk scorecard for each of the products that gauges the risk specific to the product on several dimensions. Even model risk can be included as a dimension in the analysis, where this risk will tend to increase as the experience one has with a product decreases. The individual scores on each dimension can then be aggregated using any of the standard methods ranging from the estimation of an aggregation function to the construction of an expert system. Regardless of the aggregation method used, the specific risk number produced must be calibrated so that it properly reflects the standard deviation of return that is not captured by systematic risk factors. The risk scorecard for each product, along with the information used to estimate its alpha, can be summarized in an assessment of the product that can usually fit in a singlepage format. The historical, actual, and target shares for the product can also be included. "Onepagers" constructed this way can provide a valuable tool for strategic management regardless of how seriously one takes the output of the TreynorBlack model. As noted earlier, one may need to repeat the three steps of the process until a final
set of inputs for the TreynorBlack model is determined. The process of determining alphas
and specific risk quantities for a given partition of the enterprise’s business into
products can provide insights that lead to an even better partition. Also, as the product
mix changes over time, the riskbased product definitions will need to be updated. There
is no limit on how frequently or infrequently one may wish to update the estimates of
alpha and specific risk. Major maintenance should probably be done annually, with minor
changes incorporated on a quarterly or monthly basis. Extending the ModelThe TreynorBlack model, like any other optimizationbased model, can be extended by the addition of constraints that can reflect real limitations on behavior. For example, we have already developed the model with a shortselling constraint by omitting products that do not cover their hurdle rates, giving them an automatic target share of zero. Likewise, it is usually the case that the amount of business that can be generated in any product line is constrained either by the internal capacity to originate that business or by the depth of the market for the product. It is quite easy to add capacity constraints for each product in the portfolio through an iterative maximization process. In the example given above, suppose that Asset 4 is limited to a 40% share of the portfolio even though the model indicates that it should receive a 47.74% share. The excess 7.74% share is then allocated to the other three assets according to their TreynorBlack weights. If multiple products are constrained, the reallocation process is performed iteratively until the entire portfolio is allocated. Although the TreynorBlack model no longer technically has a closedform solution, the underlying logic of allocating shares proportional to alpha and inversely proportional to the square of specific risk remains. "Soft" capacity constraints lead to further complications. With a soft constraint, capacity can be increased, but only through a reduction in return, i.e., there is a measurable market impact to doing business. If the impact can be modeled as a few discrete "lumps," then an iterative process similar to that for the hard capacity constraint can be used. If the tradeoff between share and pricing is more complex, e.g., it takes the form of an upwardsloping "supply" curve, the extension of TreynorBlack that results from the addition of this constraint can usually be solved analytically. The constraint that is likely to be the most important one for the firm—and the one too often ignored in portfolio optimization—is that of economic viability, which in most cases is equivalent to the notion of capital adequacy. In the original application of the TreynorBlack model to an investment portfolio capital adequacy was not a problem because the capital structure of the portfolio allocator is assumed to consist of all equity and no debt. When it comes to the constraint of maintaining adequate capital for its ongoing operations, the leveraged firm may find itself facing several constraints because of the demands of regulators and rating agencies in addition to its own determination of economic capital. It is likely that at any point in time one or more of these constraints will be binding on the firm, so it is not possible to perform a true optimization without taking them into account. As noted in the introduction to this paper, the optimization performed by portfolio models such as TreynorBlack accounts for possibilities along the entire probability distribution of outcomes and gives credit for the return associated with risk. The issue of capital adequacy, on the other hand, is entirely concerned with controlling the lowend tail of the distribution without concern for the returns to be gained at the cost of expanding that tail. The addition of a constraint related to the tail of the probability distribution is also very useful in cases when the risk is skewed to the downside, as is the case for many debt or optionbased assets. The addition of a downside penalty in the form of a capital adequacy constraint can compensate for serious departures from the normality assumptions of the TreynorBlack model. In contrast with the capacity constraints considered above, capital constraints are trickier to incorporate into the analysis while maintaining its simplicity. At a technical level, each capital constraint has associated with it a Lagrange multiplier that measures how tightly the constraint bites and serves as a "shadow price" for capital associated with that constraint. Products that are significant consumers of one or more types of scarce capital have their TreynorBlack weights reduced to reflect their capital utilization. In some cases it may be possible to retain the original simplicity of the TreynorBlack model simply by netting an implied charge for capital adequacy out of the excess return. Since the excess return already accounts for the riskadjusted hurdle rate, this must be done carefully to avoid doublecharging for capital. Also, although this paper has focused on the aspect of the TreynorBlack model that concerns active portfolio management, the overall optimal holdings for the firm will include passive holdings, most significantly, the capital held to deleverage or buffer the active portfolio The passive holdings of the firm are also influenced by the degree to which it decides to hedge the systematic risk that it has taken on in constructing the optimal active portfolio. In any event, the important point is that the TreynorBlack model can coexist with established risk management procedures and can assure that the firm not only meets the standards required for survival according to overall risk guidelines, but also attains its greatest potential for profitability on a quartertoquarter basis.
