Make the inputs better, but optimize

Mean-variance optimization can be a useful planning tool if forecast uncertainty is incorporated.

Mar 1, 2004 @ 12:01 am

Classical mean-variance optimization is a quantitative tool that many investment advisers use to build portfolios for clients. The goal of MV optimization is to find portfolio weights that optimally diversify risk without reducing expected return and ultimately automate the asset management process. The procedure is based on the pioneering work of Harry Markowitz, the Nobel-winning economist who is widely recognized as the father of modern portfolio theory. While the aim is admirable, the results so far are disappointing.

Anyone who spends time working with most commercially available optimizers usually reaches the conclusion that classical MV optimization fails to live up to its promise. One problem is that it is highly unstable; even small changes in your forecasts of risk or return can often result in totally divergent recommended portfolios. Not being 100% certain of your information, which of the divergent portfolios should you recommend to your client?

Another problem is that MV optimization produces biased portfolios. MV optimized portfolios are "error maximized"; the optimizer overuses the information you give it, ending up with portfolios that are non-intuitive and have little, if any, actual investment value. That's why optimized portfolio return is, on average, an overestimate and why the portfolios that are based on this biased information typically do not perform well. Despite the sophistication of the underlying mathematics and ideas, advisers quickly conclude that the process is somehow critically flawed.

The conventional way of dealing with biases in MV optimized portfolios is to constrain the optimization or manage the inputs so that the investor has a portfolio that you think is appropriate. But this doesn't solve the problem. In this case, optimizers produce predefined portfolios and little more than a scientific veneer for an ad hoc process.

It is natural for investors to blame the problems of MV optimization on flawed inputs. Certainly, it is hard to argue against trying to improve the inputs. As a result, investment institutions typically focus the bulk of their human and capital resources on improving the reliability of forecasts of asset risks and returns. In doing this, however, they often ignore the optimization technology used to transform that information into an investment portfolio. At the end of the day, good inputs are no better than bad ones if the portfolios that represent the information have little real investment value.

Our research shows that the focus on developing inputs and ignoring the optimizer is counterproductive. MV optimization typically creates overestimates of portfolio return (relative to risk) and inferior investment portfolios whatever the quality of the information in the optimization inputs. This bias seriously limits the investment value of MV optimization for many financial planning and asset management objectives, including multiperiod cash flow forecasting.

One way to understand what is happening is that classical optimizers assume that all the information you include in the optimization is certain. This leads to the root cause of the problem: Investor uncertainty is not captured in MV optimization. In simple terms, the model has no option for including any level of analyst uncertainty in the optimization process.

But investors know that their information has uncertainty. What a good investor expects to see in a portfolio optimization and what actually is computed is often very different.

The necessary solution is to incorporate forecast uncertainty in portfolio optimization. Such a process would see the investment world as it really is - in shades of gray rather than black or white. At New Frontier Advisors, we use Monte Carlo simulation to generate hundreds of hypothetical scenarios relative to your inputs to define MV optimized portfolios that reflect forecast uncertainty.

The logic of MV optimization is seductive, but this is mostly an illusion that is all too apparent in the investment period. As used currently, MV optimization has largely a marketing, rather than investment, function.

The demonstrable biases in MV optimization indicate that even the most sophisticated analysts rely largely on their intuition when developing recommended investment portfolios.

This may give rise to a significant fiduciary concern, since technology is now available to improve investment performance and to avoid the unreliability of the ad hoc process underlying many recommended portfolios.

MV optimization is an important idea with many potential investment benefits. But the nearly 50-year-old promise of better-diversified portfolios, improved investment performance, and automatable asset management is likely to be achieved only when uncertainty is integrated into portfolio optimization.

Richard Michaud is president and chief investment officer at Boston-based New Frontier Advisors LLC, whose patented technology investment advisers and institutional investors use for portfolio optimization, asset allocation and portfolio re-balancing.

0
Comments

What do you think?

View comments

Recommended for you

Upcoming Event

Jul 09

Conference

Boston Women Adviser Summit

The InvestmentNews Women Adviser Summit, a one-day workshop now held in six cities due to popular demand, is uniquely designed for the sophisticated female adviser who wants to take her personal and professional self to the next level.... Learn more

Featured video

INTV

Regulators' gloves are coming off with cybersecurity. Put up your dukes with these tips

Updated guidelines and some of the first-ever rule enforcements signal that regulators are getting serious about holding firms accountable for data breaches, according to special projects editor Liz Skinner and technology reporter Ryan Neal.

Recommended Video

Keys to a successful deal

Latest news & opinion

Advisers throw cold water on FIRE movement

Millennials love it, advisers don't: Turns out, extreme early retirement is a suitable goal for almost nobody.

10 universities with the most billionaire alumni

These 10 American schools have the greatest number of alumni who are billionaires.

Top-performing ETFs of 2018

The markets took a beating last year, but these exchange-traded funds bucked the trend

Morningstar says investors rushed the exits in 2018

Net flows into mutual funds and ETFs were the lowest since the 2008 financial crisis, while money-market funds captured inflows.

Widow awarded $4.2 million by Finra panel for theft by ex-Royal Alliance broker

The former broker, Gary Basralian, earlier pleaded guilty to theft and is facing up to 20 years in prison.

X

Hi! Glad you're here and we hope you like all the great work we do here at InvestmentNews. But what we do is expensive and is funded in part by our sponsors. So won't you show our sponsors a little love by whitelisting investmentnews.com? It'll help us continue to serve you.

Yes, show me how to whitelist investmentnews.com

Ad blocker detected. Please whitelist us or give premium a try.

X

Subscribe and Save 60%

Premium Access
Print + Digital

Learn more
Subscribe to Print