Part 2 of a 7 part series on Smart Beta.
Many quant managers have started to offer their active strategies on an index chassis. This approach allows exchange-traded funds (ETFs) to be created on active (but rules-based) strategies. The space that has been created is often referred to as strategy indices. Smart Betas can be thought of as a subset of the strategy index space.
Quantitative active strategies generally entail high turnover because they require frequent rebalancing. This is driven by the belief that quant alpha signals can decay quickly as hedge funds and high frequency shops compete to exploit transient price anomalies. Active quant strategies primarily emphasize generating alphas (in excess of standard equity premia) through proprietary return forecasting. Typically, quant active strategies are not designed for high capacity, low turnover, transparency or an economic representation of the targeted geographical exposure. These traits and the alpha focus make them a poor fit for the equity core, which tends to emphasize the construction of a high capacity portfolio that provides intuitive economic exposures while extracting well-known sources of return premia.
Given this characterization, it is convenient to divide strategy indexing into an alpha and a beta camp. Smart Beta indices fall in the beta camp, offering investors relatively low cost, transparent, easy-to-implement strategies that reflect broad market exposure and capture the standard sources of equity premia. Active quant indices cast their lot with the alpha side, seeking to provide investors with the potential for exploiting more transitory price anomalies driven by investors’ behavioral quirks that are yet to be fully understood.
Differentiating alpha and beta helps investors make sense of the various strategy indices available in the financial services marketplace. If investors embrace modern finance theory, then Smart Beta is simply a natural progression from a world with a single source of equity premium to a world with multiple wellsprings of equity premia. Investors need not question whether the standard sources of equity premia, such as value or low volatility, are data-mined or otherwise illusory. Instead, they need to formulate a view on the forward-looking premia associated with market beta, value beta, and low volatility beta. Once these return assumptions are made, they then need to think about the appropriate blend of the different equity premia for their equity core given their risk/return preferences and the cost of capturing these premia.
When conducting due diligence on alpha-seeking quant indices, investors must apply the same analyses that they have traditionally applied in selecting active managers. Is the back-test believable? What are the theoretical supports for the anomaly being captured? Is there robust out-of-sample evidence that the result is not an artifact of a unique time period or data set? Is the anomaly sufficiently persistent and high capacity that it can be exploited at size and generate alpha for an extended period of time? Back-tests associated with alpha production are not more credible simply because the investment chassis is now an index.
Ultimately, I hope the success of strategy indices will lead to lower expenses for their beta oriented equity core or the alpha-seeking satellite. The greater transparency associated with the index chassis should also serve to reduce agency conflicts, which show up as performance reducing activities such as window dressing and style drift. This will also reduce the governance costs incurred by investors.