INDUSTRIAL ENGINEERING APPLICATIONS IN FINANCIAL ASSET MANAGEMENT:THE ASSET-MANAGEMENT PROBLEM

By -

THE ASSET-MANAGEMENT PROBLEM

The job of investment managers is to create portfolios of assets that maximize investment returns consistent with risk tolerance. In the past, this required simply selecting a blend of stocks, bonds, and cash that matched the client’s needs. Asset managers typically recommended portfolios heavily weighted in stocks for aggressive investors desiring to build wealth. They proffered portfolios over- weight in bonds and cash for conservative investors bent on wealth preservation. Aggressive stock- heavy portfolios would be expected to yield higher returns over time but with considerable fluctuations in value. Concentrated cash and bond portfolios would be less volatile, thus preserving capital, but produce a lower return.

Origins

Until recently, a casual rule-of-thumb approach sufficed and was deemed adequate to produce rea- sonable performance for investors. For example, a portfolio consisting of 65% equities and 35% bonds generated returns and risk similar to a 75% equities and 25% bonds portfolio. However, following the inflation trough in the 1990s, bond returns declined and investors with low equity exposure suffered. In addition, those investors ignoring alternative assets such as hedge funds and venture capital found themselves with lagging performance and greater portfolio volatility.

Studies have consistently shown that selection of the asset mix is the most important determinant of investment performance. Early influential research by Brinson et al. (1986, 1991) and a more recent update by Ibbotson and Kaplan (1999) indicate that asset allocation explains up to 90% of portfolio returns. Security selection and other factors explain the remainder. Consequently, the asset blend is the key intellectual challenge for investment managers and should receive the most attention. Traditional rules of thumb no longer work in a dynamic world with many choices and unexpected risks.

Problem Structure and Overview

Markowitz was the first to propose an explicit quantification of the asset-allocation problem (Mar- kowitz 1959). Three categorical inputs are required: the expected return for each asset in the portfolio, the risk or variance of each asset’s return, and the correlation between asset returns. The objective is to select the optimal weights for each asset that maximizes total portfolio return for a given level of portfolio risk. The set of optimum portfolios over the risk spectrum traces out what is called the efficient frontier.

This approach, usually referred to as mean-variance (MV) analysis, lies at the heart of modern portfolio theory. The technique is now mainstream, regularly taught in investment strategy courses. A massive literature exists exploring the methodology, its intricacies, and variations. ‘‘Black box’’ MV optimization programs now reside on the desks of thousands of brokers, financial advisors, and research staff employed by major financial institutions.

The principal advantage of MV analysis is that it establishes portfolio construction as a process that explicitly incorporates risk in a probabilistic framework. In this way, the approach acknowledges that asset returns are uncertain and requires that precise estimates of uncertainty be incorporated in the problem specification.

On the surface, MV analysis is not especially difficult to implement. For example, it is very easy to guess at future stock and bond returns and use historical variances and correlations to produce an optimum portfolio. It is not so simple to create a multidimensional portfolio consisting of multiple equity and fixed income instruments combined with alternative assets such as private equity, venture capital, hedge funds, and other wonders. Sophisticated applications require a lot of groundwork, creativity, and rigor.

Implementation

Because the vast majority of MV users are neophytes who are not fully aware of its subtleties, nuances, and limitations, they are sometimes dismayed by the results obtained from MV optimization programs. The reason is that achieving sensible outcomes is highly dependent on quality input. Very often the return, risk, and correlations injected into MV models are empirically and theoretically inconsistent. This generates fallacious and highly distorted portfolios, leading many novices to reject the approach as capricious and unrealistic.

Mean-variance analysis must be integrated with a specific investment process if the results are to be useful. The steps required include:

1. Specifying the investment alternatives to be considered

2. Accurately forecasting expected asset returns, variances, and correlations

3. Executing the optimization

4. Choosing the appropriate implementation vehicles that deliver the performance characteristics embedded in the analysis Optimization results must be carefully reviewed to ensure that assumptions satisfy consistency re- quirements and examined for solution sensitivity to changes in inputs.

Comments

Post a Comment

Popular posts from this blog

NETWORK OPTIMIZATION MODELS:THE MINIMUM SPANNING TREE PROBLEM

By -
Image
THE MINIMUM SPANNING TREE PROBLEM The minimum spanning tree problem bears some similarities to the main version of the shortest-path problem presented in the preceding section. In both cases, an undi r ecte d and connected network is being considered, where the given information includes some mea- sure of the positive length (distance, cost, time, etc.) associated with each link. Both problems also involve choosing a set of links that have the shortest total length among all sets of links that satisfy a certain property. For the shortest-path problem, this property is that the chosen links must provide a path between the origin and the destination. For the minimum spanning tree problem, the required property is that the chosen links must provide a path between ea c h pair of nodes. The minimum spanning tree problem can be summarized as follows: 1. You are given the nodes of a network but not the links. Instead, you are given the po- tential links and the positive length for each
Read more

DUALITY THEORY:THE ESSENCE OF DUALITY THEORY

By -
Image
THE ESSENCE OF DUALITY THEORY Given our standard form for the primal problem at the left (perhaps after conversion from another form), its dual problem has the form shown to the right. Thus, with the primal problem in maximizatio n form, the dual problem is in minimization form instead. Furthermore, the dual problem uses exactly the same pa r amete r s as the primal problem, but in different locations, as summarized below. 1. The coefficients in the objective function of the primal problem are the right-hand sides of the functional constraints in the dual problem. 2. The right-hand sides of the functional constraints in the primal problem are the coef- ficients in the objective function of the dual problem. 3. The coefficients of a variable in the functional constraints of the primal problem are the coefficients in a functional constraint of the dual problem. To highlight the comparison, now look at these same two problems in matrix notation (as introduced at the beginn
Read more

DECISION SUPPORT SYSTEMS:DIALOG GENERATION AND MANAGEMENT SYSTEMS

By -
Image
DIALOG GENERATION AND MANAGEMENT SYSTEMS In our efforts in this chapter, we envision a basic DSS structure of the form shown in Figure 12. This figure also shows many of the operational functions of the database management system (DBMS) and the model base management system (MBMS). The primary purpose of the dialog generation and management system (DGMS) is to enhance the propensity and ability of the system user to use and benefit from the DSS. There are doubtless few users of a DSS who use it because of necessity. Most uses of a DSS are optional, and the decision maker must have some motivation and desire to use a DSS or it will likely remain unused. DSS use can occur in a variety of ways. In all uses of a DGMS, it is the DGMS that the user interacts with. In an early seminal text, Bennett (1983) posed three questions to indicate this centrality: 1. What presentations is the user able to see at the DSS display terminal? 2. What must the user know about what is seen at the display
Read more