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The Conceptual Link Behind the “LTR” Ranking

May 24, 2010

Does positive feedback trading, indicated by an adjusted measure of return autocorrelation, enhance momentum profitability? In the February 2010 version of their paper entitled “Positive Feedback Trading Activities and Momentum Profits”, Thomas Chiang, Xiaoli Liang and Jian Shi examine the relationship between positive feedback trading and profitability of momentum strategies.”

Readers are encouraged to look at  the following link on CXO Advisory as important background reading: http://www.cxoadvisory.com/volatility-effects/amplifying-momentum-returns-with-idiosyncratic-volatility/

I have often been asked whether the Livermore Index is not simply an index of “high-beta” stocks that do well in up markets and poorly in down markets. The  hedged backtests show a sharpe ratio of 2 across bull and bear markets, so therefore it would be mathematically impossible for this to be the case. So what is driving the alpha above and beyond standard relative strength excess returns? The real reason the Livermore Index has excess alpha is due to is “LTR” ranking, which is a proprietary measurement of autocorrelation—and not the standard variety. https://cssanalytics.wordpress.com/2010/04/06/performance-of-dv2-on-the-bottom-50-ltr-dv-index-versus-the-top-50-ltr-trend-index/ The key is that stocks with high LTR rankings are much more likely to trend smoothly and have performance that is less connected to the index. In fact, such stocks exhibit high idiosyncratic volatility versus beta volatility–which is a fancy way of saying that their volatility is due to individual business factors versus index movements. Idiosyncratic volatility provides two distinct benefits 1) it improves the diversification benefit dramatically since correlations are lower between stocks with high idiosyncratic risk versus low idiosyncratic risk 2) it permits a stock to detach from the index.

This means that stocks that have powerful fundamentals and a strong business model like an Apple Computer or a Baidu.com will be able to march to the beat of their own drummer at times, rising even when the market may be falling. This detached behavior is especially evident on the shorter time frames. Since high LTR stocks must exhibit a smooth price history, the detachment often manifests itself in a persistent/consistent manner. This behavior helps to improve the risk-adjusted returns of holding such stocks since they tend to have strong momentum versus volatility.

But what about the reverse? I introduced the low-LTR index many posts back as a “mean-reversion” index. The differences in the performance between the high and low LTR indices was remarkable– high LTR stocks often lost money trading using mean-reversion strategies while low LTR stocks made a significant profit. So what is driving this relationship? For one, low LTR stocks are very choppy and tend to have less momentum. This means that they often have more support and resistance barriers, and thus tend to stay range-bound. Low LTR stocks are also more volatile proportionately, and as we know volatility tends to be cyclic.  But finally, and perhaps most importantly, low LTR stocks are more likely to exhibit low idiosyncratic volatility and therefore by extension tend to have a higher beta to the index than high LTR stocks. A higher beta implies that low LTR stocks will be more in sync with the index. Thus if they become over or under-valued versus the index by way of being overbought or oversold, they will “snap back” due to their higher cointegration relationship.

The bottom line is that despite many of the catchy monikers or acronyms, there is almost always a logical reason behind many of the ideas put forth on this blog. Many have their roots in academic finance theory. Initially it was my intention to spare readers from much of the logic to avoid making things sound overly technical or beyond reach. However, I think it is worthwhile to consider such things so that we all do not get trapped in the semantics and the fluff. The reality is that indicator and strategy names can almost make it seem as if there are more different/distinct ideas than actually exist. The truth is oscillators, trend indicators, relative strength, breadth all share a very common lineage—it is up to the smart researcher to think in terms of what they are trying to find out—or what they are trying to take advantage of versus attempting mass optimization and data mining. Often it is just easier and more tangible to proceed in such a manner–but I implore you to try to think and understand the market because that is where you will find true enduring anomalies versus spurious or unstable results.

4 Comments leave one →
  1. BMB permalink
    May 24, 2010 10:46 am

    Great post DV. I am interested not only in the potential performance of your strategies, but how that performance is produced.

  2. May 25, 2010 6:36 am

    David
    Attributing the LTR’s ability to “trend smoothly” to the component stocks idiosyncratic volatility seems to conflict a bit with the “Positive Feedback” article’s research conclusions. They find high momentum better related to “medium” idiosyncratic volatility (although the high IV stocks did better with mo than the low). Do you think this is due to their measurement tools or perhaps there are other sources of LTR’s alpha?

    In addition, you made a reference re the LTR a few weeks back to the fact that while the stocks in the LTR were more trendy, inclusion in the index did not mean they were trending up at the time. Your weekly DV spreadsheets of the top ranked LTR stocks is supplemented with trend indicators as well. If that is how LTR should be used should the weekly comparison in this blog of the weekly returns of the top ten LTR stocks versus the QQQQ be made? I know that led me to believe at first that this was a positively trending basket of stocks, while instead it could be a very mixed bag of issues albeit united by their trendiness.

    Jerry

  3. Chad permalink
    February 12, 2014 5:48 am

    Hi David, any update on how to LTR rankings have been performing since you last posted about them? Also, If the top half of LTR stocks tend to follow through and the bottom half tend to mean revert, do you find that during periods where follow through is strong in the top half, that mean reversion profits are also higher in the bottom half (and when profits are low for momentum they’re also low for mean reversion) or is it more of the case that when the top LTR stocks show more follow through the entire sample also shows relatively more follow through and thus the mean reversion profits are weaker and vice versa?

    btw if you decided to use the nasdaq 100 due to their propensity to trend more, wouldn’t it also mean that if you wanted to trade mean reversion you’ll get even higher profits trading the bottom LTR stocks in the S&P 500 index instead of the nasdaq 100?

    Thanks for sharing, really eye-opening stuff on this blog,
    Chad

Trackbacks

  1. Pairs Trading with CSSA’s LTR Stock Ranks

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