October 1, 2014

Peer Review

Autumn the past two weeks splashed brilliantly over the Colorado Front Range. It puts everything into perspective.

I recall as a kid my mother saying in retort to my reason for some dunderheaded act, “You did it because ‘everybody else was doing it?’”

Investor-relations professionals have long tracked what everybody else is doing, comparing the company’s trading with a set of peers. Clustering similar things is a time-tested statistical maxim. We practiced it on the ranch of my youth at the auction yard, sorting groups of our steers on display for potential buyers uniformly by color, weight, shape. One weak link could throw off the average per-pound price.

What makes a peer?  Similar characteristics. Yesterday I sent a screenshot to an IRO (investor relations officer, for you newbies) showing startling comportment between her shares and another stock. One is a home-furnishings retailer, the other a technology high-flyer in cloud architecture.

On the surface, no distinguishing features say these two are peers. But machines calculating probabilities see patterns, not sectors. In human physiology, beneath the skin we’re all the same. We’re peers though we may not look alike. In the stock market, physiology is comprised of rules, prices, supply and demand.

It calls into question comparing how you trade versus peers. Yesterday one of our household-name clients asked, “We’re trailing our peers, so how can the cause be macro?” The data were overwhelming: No movement in rational behavior, massive change for indexes/ETFs and hedging. Our client is the “category killer” in that group, the one every index, every ETF, will own – or sell. Macro selling won’t hit peers the same, and either way, pressure wasn’t fundamental.

That’s no absolute either. Another category-killer in a different industry outperformed its peers because safe-harbor money was buying only the biggest. Plus, algorithms executing the same instructions in an industry group can produce different effects on components.

For example, suppose four in a sector are sold by asset-allocation models (Blackrock, Vanguard). In one, the sell signal prompts intermediaries to short the stock (we measure short volume daily and can see swings of 15-25% in a day). Price drops.  In another, investors are sidelined and share-supply is accumulating. The algorithm sweeps through, selling and triggering inventory-management warnings for brokers carrying shares, which then dump them. The stock plummets.

A third has tight supply and demand in its shares. The selling algorithm dopplers by. Price doesn’t move, staying flat as buyers offset sellers perfectly. In the fourth peer, value algorithms have been programmed to aggressively buy any dip. Here comes the selling algorithm, which trips the buy order and after dipping to start, shares close up 2%.

Moral: To trade like your peers, your physiology must be the same. That’s more often determined by market characteristics than fundamentals, so reviewing peers may be better for operational purposes than as a measure of comparative value.  Either way, canny IROs consistently track behavioral-change in their shares to avoid guesswork (we can help with that!).

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