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Do risk-adjusted returns still make sense when risk itself is hard to define?

Ever since the seminal works of Harry Markowitz and William Sharpe in the 1950s and 1960s in the field of portfolio theory academics and practitioners have been trying to incorporate the concepts of efficient frontier and risk within their portfolio management methodologies and performance evaluation analysis.

The truth is that most who are familiar with the subject are aware that very few, if any, portfolio managers look for the efficiency frontier in their asset allocation processes, mainly because it is almost impossible to locate in reality, and base their decisions on a combination of gut feelings and analyst recommendations.

On the other hand, few, if any, portfolio, or its manager, is appraised without one form or another of the performance evaluation models being applied. Be it the Sharpe Ratio [Sharpe (1966)], Treynor’s ratio [Treynor (1966)], Jensen’s alpha [Jensen (1968, 1969)], Information ratio [Treynor and Black (1973)], M2 measure [Modigliani and Modigliani (1997)], or even the highly popular Morningstar rankings for mutual funds, both academics and practitioners use them to evaluate the so-called risk-adjusted returns of portfolio managers and their funds.

The reliance on these methodologies is such that when investors wish to use raw performance data in selecting the funds they wish to invest in their sanity is brought into question. And, those who are familiar with institutional asset management are fully aware that such strict investment parameters are set by the investment consultants, who are acting on behalf of the insurance and pension fund, that the poor asset managers feel like they have been placed inside a straight jacket.

In our latest paper, entitled Economists’ hubris – the case of equity asset management, we examine the reliability of these so-called risk-adjusted performance evaluation models and find that they are significantly more unreliable than many are lead to believe. In fact, none provides anything even remotely scientific or accurate for simple equity funds, let alone for portfolios that have more complex assets. As the complexity and variety of assets within the portfolio increases so does the unreliability of these models.

If one accepts that these risk-adjusted models are unreliable then the implications for the industry can be huge. For one thing, using raw unadjusted returns data for selection of funds should not be view as insane as it currently is. Investors should be allowed to use any methodology/parameter they wish for selecting funds and portfolio managers, since there is no scientific way of proving that they are facing risks that are greater than other investors.

More importantly, once the returns are adjusted for risk many well-performing fund managers might be penalized simply because their portfolio allocation is allocated a higher risk weighting unfairly. The alternative is also true; many not so able managers might find that they are placed higher in the ranking tables simply due to an inaccurate allocation of risk. The implications are that many capable managers might feel that they are unfairly penalized and not adequately compensated for their performance, resulting in many setting up their own hedge funds.

In our paper we suggest that the so-called inertia model suggested by Lord Myners might provide an interesting compliment to the current investment evaluation and compensation models. We are not suggesting it should replace the other models, but feel that given its ease of computation it can be a suitable compliment to the other models.

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