What We Can Learn From Relative Sector Performance

I have begun a large-scale study of the intercorrelations among equity sectors and asset classes; so far, the study appears promising. I will describe the study in greater detail later today when I post to the Trader Performance page. The gist of my findings is that market inefficiencies--and hence trading edges--are greatest when sectors and asset classes deviate from their normal relationships with one another. This can then yield directional trade ideas for particular sectors/asset classes or can generate spread ideas which profit from a normalization of the relationship among the sectors/asset classes.

Readers of this blog will recognize that this line of research is designed to mirror the ways in which I have found highly successful market professionals to operate.

Let's begin with a simple example. Using ETFs, I divided the equity market into seven sectors: energy, finance, technology, consumer, health care, utilities, and raw materials. I then computed monthly correlations between each of the sectors on a moving basis and calculated an average correlation among all the sectors. This overall average correlation since March, 2003 (N = 807 trading days) has been .48.

Since March, 2003, we have had 97 days in which SPY has risen 2% or more on a five-day basis. Five days later, the average gain in SPY has been .25% (58 up, 39 down). This is not different from the average gain in SPY for the entire sample (.28%; 465 up, 342 down).

When, however, SPY has risen 2% or more *and* we have a high intercorrelation among the sectors (N = 48), the next five days in SPY averages a strong gain of .42% (34 up, 14 down). When SPY has risen 2% or more but the intercorrelation is low (N = 49), the next five days in SPY averages a gain of only .08% (24 up, 25 down).

It thus appears that strong rises are most likely to continue when the sectors are moving in concert. Returns are subnormal after strong rises when the sectors diverge relative to one another.

When it comes to declines, however, it's a different story. Since March, 2003, we've had 105 days in which SPY has dropped 1.5% or more in a five-day period. Five days later, SPY has averaged a gain of .99% (72 up, 33 down)--much stronger than the average gain for the entire sample as noted above.

When SPY has dropped 1.5% or more in a five-day period *and* the intercorrelation among sectors is high (N = 53), the next five days in SPY average a very strong gain of 1.44% (40 up, 13 down). When SPY has dropped sharply and the intercorrelation among sectors is low (N = 52), the next five days in SPY average a more modest gain of .54% (32 up, 20 down).

The bottom line is that when sectors are moving in unison, near-term results are bullish. Large gains tend to follow through with further strength (momentum effect), but large declines tend to reverse and produce large gains (reversal effect).

It does, indeed, appear that meaningful market edges occur when sectors deviate from their normal relationships with one another. More studies will follow.