Factor-Based Ranking Systems

I spend a lot of time creating and refining ranking systems for stocks. My entire system of investing/trading is based on systems that take a universe of stocks and rank them from lowest to highest based on various factors. Using these systems to buy and sell microcaps, I've been able to grow my own personal portfolio by leaps and bounds, making profits of 45% in 2016, 58% in 2017, and 14% in 2018. To do this, I use Portfolio123, a versatile subscription-based service that allows me to backtest ranking systems and measure their performance.

One way of measuring ranking system performance is to separate the stocks at each rebalancing point into ten or twenty groups based on their rank and seeing how those groups would have performed. So, for example, here is a performance chart of the ranking system I use for my own personal stock buys, with a wide universe of close to 4,000 stocks and rebalancing every four weeks between January 1999 and January 2019. It’s important to note that a lot of the stocks I buy are not very liquid and that the performance chart doesn’t reflect slippage/transaction costs.

Looking at this performance chart, it’s easy to realize that this kind of ranking system is very efficient at choosing outperforming stocks, but doesn’t differentiate very well between stocks in middling percentile groups (for instance, between the 35th and 70th percentiles). In order to evaluate a stock’s chances of success, a different approach is needed. Rather than optimizing a ranking system for the top quantile or the top 50 or 100 or 500 stocks, it would be better to optimize for the slope of the performance chart. A ranking system that maximized slope would be truly evaluative (as long as one assumes that past performance is somewhat predictive of future performance).

So I developed the Stock Evaluator, a ranking system that evaluates the chances that a stock will outperform. It’s quite different in many ways from the system I use to buy and sell stocks. Here’s the performance chart, this time on a wider universe of stocks, with a quarterly rebalance, over the same time period.

One huge difference between these ranking systems is that the long-only ranking system favors small caps, microcaps, and nanocaps, and the evaluative system doesn’t take size into account. Why?

Size works as an augmentative factor. The smaller the stock, the more it’s going to be affected by other quantifiable factors. Small-caps that are overvalued, for example, will do far worse than overvalued large-caps.

To illustrate this, here’s the performance of the Stock Evaluator in two different universes of stocks: the top image is its performance in the Russell 1000 (large stocks) and the bottom is in the Russell 2000 (small stocks). You’ll note that the spread between high-ranking and low-rankings stocks is a lot wider for small stocks than for large ones. This is true of almost all ranking systems I’ve seen.

Let’s take my evaluative system and add a size factor (market cap) equal to 17% weight. You’ll see the difference in the performance below. It’s become much more like my long-only ranking system, and much less evaluative.

One can also create a ranking system for going short, whose performance chart would look something like this:

This ranking system consists of only ten equally weighted factors, rebalanced monthly. No doubt I could create an even more extreme one using more factors and different weights.

In order to perform an experiment, after creating this system I created another system of ten equally weighted factors (almost all of them different from the short-only system), rebalanced monthly on the same relatively liquid universe, designed for going long. The performance chart looks like this.

Now here is the performance of the two ranking systems combined, so that each of the twenty factors gets a weight of 5%.

The top ventile (5%) here isn’t quite as high as the top ventile of the second system, and the bottom ventile isn’t as low as the bottom ventile of the first system, so possibly this “compromise” system wouldn’t be quite as useful for choosing stocks to buy or short. However, as a “stock evaluator,” it’s far better than either of the first two systems. What this ranking system is really useful for is looking up the percentile rank of a particular stock, which will give you a good estimation of its chances of success over the next rebalancing period (in this case, four weeks).

To sum up, there are three different uses for a ranking system, and these three systems are good examples. One use is to choose stocks to buy; another is to choose stocks to go short; and a third is to evaluate stocks. For the first, you want to have the top-ranked stocks do really well; for the second, you want to have the bottom-ranked stocks do really poorly; and for the third, you want both. For the first and second, you want to use size factors wisely, emphasizing small caps; for the third, the size factors cancel each other out, and you end up with a size-neutral system.

I'd like to explain this last point in detail. One good short-only system, just for example, might look for stocks with high accruals, low asset turnover, low margins, high share turnover, high volatility, negative momentum, high value ratios, and small size; a good long-only system might look for stocks with strong growth in EPS and sales, low accruals, increasing volume, exceptionally low value ratios, and small size. So if the evaluative system would combine the two, you’d rank stocks as follows: accruals (the lower, the better), asset turnover (the higher, the better), margins (the higher, the better), share turnover (the lower, the better), volatility (the lower, the better), momentum (the higher, the better), EPS and sales growth (the higher, the better), recent volume change (the higher, the better), and, most importantly, value ratios (the lower, the better). All these factors work oppositely for long and short systems. But since both the long and short ranking systems include a size factor that’s exactly the same instead of the exact opposite, a combination of the two would cancel the size factor out.

There has been a long tradition of looking at factors based on rank performance. The usual study takes the top-ranked stocks long and the bottom-ranked stocks short. A good example of a fund that works in this way is AGFiQ's US Market Neutral Value Fund (CHEP). This fund goes long equities with low valuations and shorts equities with high valuations. Its performance has been absolutely abysmal. A much more profitable strategy would be to choose different sets of factors for the long and the short holdings. And if overlap is a problem, you can combine the best-performing factors for each.

I hope I’ve provided some fodder for thought in how to use factors to rank stocks. There’s no one-size-fits-all way to do so, and thinking about factors and ranks in simplistic terms may be counterproductive. Buying the top-ranked stocks in a long-only ranking system may give you higher returns than buying the top-ranked stocks in an evaluative ranking system, but it’ll also give you a rockier ride. Some stocks may rank in the 99th percentile in the first, making them appear to be terrific bets, but only the 55th in the second, making them appear to be no better than average, because you’re using different factors with different weights.

In addition, you have to keep in mind that these systems have been optimized to some degree over very specific time periods with very specific sets of stocks, and out-of-sample performance over future time periods with different stocks may turn out to be very different. For example, small-cap stocks that ranked very highly using conventional value ratios performed extremely well between 1999 and 2006, and rather poorly since.

Despite the complexity of looking at factor performance through ranking, though, I believe that it offers a more comprehensive look at stock market opportunities than any other approach. The sophistication of ranking systems is far beyond the capabilities of ordinary screeners or, for that matter, of conventional discounted-cash-flow analysis. Factor-based ranking systems are truly the most versatile and comprehensive way to analyze stocks, and offer many extraordinary opportunities to beat the market.

CAGR since 1/1/2016: 40%.

My ten biggest holdings right now: ARC, OSIR, HALL, CTEK, GSB, PFSW, NTWK, SIGA, KTCC, PERI.

Comments

[Jerry]

hi,

May I know whether you buy stocks or leaps for most of your investment?

Br,
Jerry

[Yuval Taylor]

Stocks only.