Courtesy of Paul Amery
Remember to check the assumptions made for the cost of trading when examining a new index concept. ( Editor note: read between the lines, even though the phrase “best execution” is missing from this piece, the article should inspire thoughtful consideration re what true best execution entails).
Vanguard’s warning of the perils of index data mining is timely. As the number of “smart beta” index concepts increases, each promising superior performance than old-fashioned, capitalisation-weighted benchmarks, the possibility of investors getting hoodwinked also grows.
Just about anything can be used to “predict” something else if you use historical data series creatively enough. According to fund manager David Leinweber, the Wall Street Journal reports, annual butter production in Bangladesh “explained” 75% of the annual returns of the S&P 500 over a 13-year period. If you throw in data for US cheese production and the combined sheep population of the US and Bangladesh, Leinweber says, you get to “forecast” US stock prices with 99% accuracy.
Not everyone got the joke. A number of firms asked Leinweber to share his data on Bangladeshi butter production so that they could build a trading strategy around them, the WSJ tells us. Were any index and ETF providers looking for a new smart beta concept among them, by any chance?
Vanguard has its own axe to grind in all this, we shouldn’t forget. The firm sticks religiously to using traditional, cap-weighted indices as the basis for its passive funds, arguing that anything else is an active bet on market behaviour and should be recognised as such. I’ve argued before that this is as much as a commercial strategy as anything else—Vanguard’s huge size precludes it from even considering index concepts that are in any way capacity-constrained, as many non-cap-weighted approaches are.
But a good first step in assessing an index promising “smart beta” and outperformance is surely to ask oneself if the underlying investment concept makes sense. Does Research Affiliates’ “fundamental indexation” approach in equity markets, which uses companies’ revenues, profits, book values and dividends as a way of determining index weights, hold water as a strategy? It does for me.
Does the same firm’s alternative weighting scheme for sovereign bond markets (which is based on countries’ GDP, energy consumption, population and rescaled land area) work as a stand-alone investment concept? I’m not so sure. Does a smart beta strategy focused on historical stock volatilities work as a predictor of future risks? For me, not at all.
There’s also one topic Vanguard didn’t touch on in its review of the pre- and post-launch performance of newer indices—trading costs. Even if you like a new index idea, how do you know that the costs of buying and selling index constituents have been reflected accurately and fairly in the back-test?
There’s an obvious incentive for the promoters of a new index to flatter its historical “performance” by taking an optimistic view of how much it would have cost to buy and sell the index constituents over time. And while cap-weighted benchmarks are largely self-rebalancing, typically generating only a few percentage points of turnover a year, newer index concepts can easily involve annual internal index turnover of hundreds, even thousands of percent.
Historically, it appears that many index providers have dealt with the thorny problem of internal trading costs very simply—by disregarding them completely.