Here’s a trading strategy that I think has a good “story” (no link this time – it’s one of my own ideas and not a replication of an academic paper). This is the motivation: Between smart-beta ETF's, hedge funds, and other asset managers, a huge amount of money has been invested in factors that have shown some historical ability to outperform the market: value, momentum, low volatility, high dividend yield, quality, etc. Consider the low volatility factor. There are now 25 ETF's, with $35 billion in assets, dedicated to this one factor alone (see here). But smart-beta ETF’s may just be the tip of the iceberg. At a Deutsche Bank Quant conference in 2012, there was a panel discussion on low volatility strategies, and one panelist estimated that there was $160bn devoted to this strategy among all types of money managers. And this factor has only grown in popularity in the four years since that conference.

Quants typically spend a great deal of time searching for new factors. But here’s another approach: for these standard factors, trade stocks as soon as they migrate into or out of an extreme quantile in anticipation of fund flows from rule-based smart beta ETF’s and other similar money managers. For example, as soon as a stock migrates into a low volatility quantile, buy the stock with the expectation that many other funds tracking this factor will have to eventually buy it too. And the same idea applies to shorting stocks that move out of an extreme quantile.

Everyone trades these factors differently, which makes it harder to anticipate the crowd’s flows. For the low volatility factor, for example, some choose stocks with the minimum historical volatility (the $6bn SPLV ETF does this), while others choose stocks that make up the minimum variance portfolio from a mean-variance optimization (the $12bn USMV ETF does this). Some instead choose low idiosyncratic risk stocks or low beta stocks. The rebalancing schedules differ, and the historical window for estimating volatility may differ. Some pick deciles vs. quintiles. Some use a large cap universe vs. a small cap universe. And some combine low volatility with other factors, like high dividend yield.

In the attached backtesting algo, I focus on the momentum factor. Although there is no standard way of computing momentum, the method that is most commonly cited in the literature is the “12-1” momentum factor, which is a stock’s 12 month return excluding the last month’s return (the last month is often skipped to avoid the short-term, one-month reversal in stocks). There are many variables to choose from with a strategy like this, which leads to huge potential data mining. I went short stocks that migrated out of the top decile (decile 10 to decile <=6) and went long stocks that migrated into the top decile (decile <=6 to decile 10). Similarly, I went long stocks that migrated out of the bottom decile (decile 1 to decile >=5), and shorted stocks that that migrated into the bottom decile (decile >=5 to decile 1). For the stocks that migrated to the extreme decile, I hedged the momentum exposure by selling an equal dollar amount of momentum stocks (stocks that haven’t migrated from the extreme deciles). Momentum is a volatile factor with periodic crashes, so I wanted to hedge out the momentum exposure. I measure a change in decile by comparing each stock’s momentum decile one month ago with what would theoretically be the momentum decile one month from now (the 11-0 stocks today will be 12-1 stocks in one month). I chose a fixed two-month holding period for stocks that migrated.

There are two enhancements I tried to improve performance. First, you get much better results by just trading migrations to and from decile 10 and ignoring migrations to and from decile 1, which could be data mining but also is consistent with the notion that there are more long-only funds that would only be trading the top decile of stocks (there is a flag in the code to choose this option). I also looked at screening out M&A names, since some of these stocks may migrate into decile 10 on the announcement of a merger but flatline after that. The M&A screen modestly improves results.

This idea could be applied to any number of other popular factors. I briefly tested a few of them, but with very mixed results. Keep in mind, though, that there are nuances with each factor. Although momentum stocks are more volatile and you see a fair amount of large migrations over a few months, some of the other factors, like the low volatility factor and factors based on accounting data, are more stable and do not migrate as much or as quickly, so the threshold for choosing migrations may have to be smaller (for example, for these factors, it may make more sense to look at migrations to adjacent quantiles, although stocks near the border can fluctuate back and forth). And there is less consensus on what is a “standard” quality factor (pardon my shameless self-promotion, but quality factors are described in my last post), so the assets under management may be more dispersed around the various measures of quality. But it might be interesting to look at some of these other factors in greater detail.