Investments, tenth edition

  Organizational Structure and Performance 

 The mathematical property of the optimal risky portfolio reveals a central feature of invest-

ment companies, namely, economies of scale. From the Sharpe measure of the optimized 

portfolio shown in  Table 27.1 , it is evident that performance as measured by the Sharpe 

ratio and  M -square grows monotonically with the squared information ratio of the active 

portfolio (see Equation 8.22, Chapter 8, for a review), which in turn is the sum of the 

squared information ratios of the covered securities (see Equation 8.24). Hence, a larger 

force of security analysts is sure to improve performance, at least before adjustment for 

cost. Moreover, a larger universe will also improve the diversification of the active port-

folio and mitigate the need to hold positions in the neutral passive portfolio, perhaps even 

allowing a profitable short position in it. Additionally, a larger universe allows for an 

increase in the size of the fund without the need to trade larger blocks of single securities. 

Finally, as we will show in some detail in Section 27.5, increasing the universe of securi-

ties creates another diversification effect, that of forecasting errors by analysts. 

 The increases in the universe of the active portfolio in pursuit of better performance 

naturally come at a cost, because security analysts of quality do not come cheap. However, 

the other units of the organization can handle increased activity with little increase in cost. 

All this suggests economies of scale for larger investment companies provided the organi-

zational structure is efficient. 

 Optimizing the risky portfolio entails a number of tasks of different nature in terms of 

expertise and need for independence. As a result, the organizational chart of the portfolio 

management outfit requires a degree of decentralization and proper controls.  Figure 27.4  

shows an organizational chart designed to achieve these goals. The figure is largely self-

explanatory and the structure is consistent with the theoretical considerations worked out 

in previous chapters. It can go a long way in forging sound underpinnings to the daily work 

of portfolio management. A few comments are in order, though.  

 The control units responsible for forecasting records and determining forecast adjust-

ments will directly affect the advancement and bonuses of security analysts and estimation 

experts. This implies that these units must be independent and insulated from organiza-

tional pressures. 

 An important issue is the conflict between independence of security analysts’ opinions 

and the need for cooperation and coordination in the use of resources and contacts with 

corporate and government personnel. The relative size of the security analysis unit will 

further complicate the solution to this conflict. In contrast, the macro forecast unit might 

become  too  insulated from the security analysis unit. An effort to create an interface and 

channels of communications between these units is warranted. 

 Finally, econometric techniques that are invaluable to the organization have seen a 

quantum leap in sophistication in recent years, and this process seems still to be accelerat-

ing. It is critical to keep the units that deal with estimation updated and on top of the latest 

developments.    

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  Applied Portfolio Management

    27.3 

The Black-Litterman Model 

Product

Final Risky Portfolio

Customers

Control

Is final portfolio

super-efficient?

Product

Active Portfolio

Product

Passive Portfolio

Data

Feedback

Feedback

Feedback

Control

Is alpha positive?

Data

Feedback

Data

Passive Portfolio Manager

Form passive portfolio

at minimum  cost

Feedback

Control

Is correlation

with market 1?

Feedback

Feedback

Data

Macro Analyst

Estimate

E(R

M

), σ(M)

Forecasts