Not All Markets Are the Same
The search for robust systems is the focus of serious research for system developers and academics. Defining robustness is a dicey semantic discussion, but for this post we will define it as a measure the ability of a variable or system to perform well across either a spectrum of uncorrelated markets and/or across time through different regimes. There is a tremendous amount of intellectual appeal and academic support for this concept. If forced to make a choice, I would say that this is the easiest way to ensure that a system will function well out of sample.
BUT……the truth is, not all markets are the same. After a great deal of research across multiple markets and stocks, I can honestly say that there are truly a great deal of idiosyncracies in how different vehicles behave. Gold does not behave like the S&P500—it never has, and the differences are substantial. Oil does not behave like Gold or the S&P500, again substantial differences. This brings the interesting conclusion that if such large markets can behave so differently despite sharing common sources of investment flows, it stands to reason that smaller markets or individual stocks may have even more divergent behavior. In fact this is actually true—the stock market is like a great rainforest of different species in this respect. Smaller cap stocks have such unique and bizarre behavior that it is unfair to even place them in a sector or category.
This has some interesting implications– 1) a lot of the “noise” we see when searching for robust effects across stocks or markets is actually caused by real systematic differences between markets, and therefore many ideas that we discard would in fact show significant promise after controlling for it. In fact, I am told by a friend in the drug business that this is a serious issue with clinical trials because many different types of people respond poorly to drugs that work on most of the population. Thus if representative samples are not chosen for trials, a valuable drug can be rejected because of patient side effects that were not systematic but rather idiosyncratic. 2) Instead of searching for something that works on everything all the time, you can find a style that suits you and simply trade that on stocks that you find predictable–or use a systematic method that can identify such stocks/markets.
The Livermore Index is but one simple example—it is NOT just a list of high momentum stocks, it is also a list of stocks that have a historical tendency to trend in a meaningful way. That is the secret sauce–not the relative strength algorithm which is elegant but ultimately fairly simple. In fact if you were to test DV2 or RSI2 on the top 10 Livermore stocks, you will find that you lose money over a 10 year period! Surprisingly this same factor works across global markets and stocks—–it is a universal factor used to identify a very specific idiosyncracy. This means that if you wanted to trade these stocks using a 5-day or 10-day breakout, that would be a winning system. If you have a trend trader mentality then this is your dream situation in an increasing mean-reversion dominated marketplace. Heck even a 1-day breakout on these stocks is highly profitable with an exit on the first down day.
Now here is where it gets interesting and a little bit like a puzzle in astrophysics: If in a large index like the Nasdaq 100 we can identify the stocks most likely to follow through then we should be able to identify the stocks most likely to mean-revert. Therefore, given knowledge of the the current relative proportion of stocks that follow through that have risen/fallen today and the relative proportion of mean-reverting stocks that have risen/fallen today—we can predict more accurately whether the index itself is likely to follow through or mean revert. If we can do this for the Nasdaq 100, we can do this for any aggregate index whether it is the S&P500 or ANY sector or country ETF.
This my friends, is the next generation of breadth research—the old generation is indirect and often multicollinear with measures of the index movement in isolation. That is not to say that breadth measures are not reliable—because they actually do a better job of predicting market turns at extremes. It is to say that on a day to day basis—breadth is not that accurate after controlling for index effects, and it has nothing to do with the concept or variable itself. It has to do with the noise embedded in the effects I just mentioned. It has to do with the fact that not all stocks or markets are the same.