Ronald N. Kahn

The Asset Manager’s Dilemma: How Smart Beta Is Disrupting the Investment Management Industry

Smart beta products are a disruptive financial innovation with the potential to significantly affect the business of traditional active management. They provide an important component of active management via simple, transparent, rules-based portfolios delivered at lower fees. They clarify that what investors need from their active managers is pure alpha—returns beyond those from static exposures to smart beta factors. To effectively position themselves for this evolution in active management, asset managers need to understand the mix of smart beta and pure alpha in their products, as well as their comparative advantages relative to competitors in delivering these important components. Editor’s note: This article was reviewed and accepted by Executive Editor Robert Litterman. Authors’ note: This article reflects the opinions of the authors and not necessarily those of their employer.

Smart Beta: The Owner’s Manual

Smart-beta products have captured the interest of investors. But where do they fit in their portfolios? The typical investor, who currently owns active and index products, should own active, index, and smart-beta products. This article introduces a framework that decomposes any strategy’s return over time into a broad capitalization-weighted index return, the return to static exposures to smart-beta factors, the return to timing smart-beta factors, and the return above and beyond smart beta. Smart-beta risk constitutes roughly one-third of the active risk of an average active equity manager, and roughly two-thirds of the active risk of an average fixed-income manager. Diversifying across active managers increases the fraction in smart beta. Most investors will want all of these return sources in their portfolio, and this framework facilitates optimizing the blend.

Breadth, Skill, and Time

The information ratio determines the potential of an investment process to add value, and according to the fundamental law of active management, adding value depends on a combination of skill and breadth. Grinold and Kahn use an equilibrium dynamic model to provide insight into the concept of breadth, as well as a refined notion of skill. In equilibrium, the arrival rate of new information exactly balances the decay rate of old information. Grinold and Kahn denote the information turnover rate g. It is relatively easy to measure for any investment process. If the investment process forecasts returns on N assets, the breadth of the strategy i is g · N. Skill—the correlation of forecasts and returns—increases with the return horizon for small horizons, but then asymptotically decays to zero for very long horizons. The authors’main result is that the ex ante information ratio is ,where ? is a measure of skill.

Multi-Period Trading via Convex Optimization

We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and holding cost such as the borrowing cost for shorting assets. We then describe a multi-period version of the trading method, where optimization is used to plan a sequence of trades, with only the first one executed, using estimates of future quantities that are unknown when the trades are chosen. The single-period method traces back to Markowitz; the multi-period methods trace back to model predictive control. Our contribution is to describe the single-period and multi-period methods in one simple framework, giving a clear description of the development and the approximations made. In this paper we do not address a critical component in a trading algorithm, the predictions or forecasts of future quantities. The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made. We have also developed a companion open-source software library that implements many of the ideas and methods described in the paper.

The Dangers of Diversification: Managing Multiple Manager Portfolios

Investors have long built portfolios diversified across managers and have long applied mean–variance analysis to allocate to managers. This classic approach has at least three challenges. First, and most important, it concentrates risk in generic ideas correlated across managers. This can lead to unexpected tail risk. Second, it provides a temptation to overlever the resulting portfolio, which often appears to have very low risk but a high information ratio. Third, it typically combines underlying funds managed for standalone performance rather than performance of the portfolio, often leading to overdiversification. The authors analyze these three challenges and propose solutions. Unfortunately, the solutions are not easy to achieve.

Practical Applications of Smart Beta: The Owner’s Manual

In an interview with Institutional Investor Journals , Ron Kahn and Mike Lemmon , senior executives at BlackRock , describe a new marketplace in which investors need to understand and optimize the sources of their returns and managers need to deliver active returns beyond static exposures to smart-beta factors. Though not a new concept, smart beta has become a disruptive innovation with important ramifications for investors and active managers.