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

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.