Over at TheStreet’s OptionsProfits, I write a daily column looking at trade ideas and volatility and all the other sorts of things you’d expect from an options trader. One kind of trade we like to look at is when you sell options, especially puts, after a major news event has caused an otherwise healthy stock to take a dip. We looked recently at the implied volatility in options on stocks like Wal-Mart (WMT) and OpenTable (OPEN) and, ultimately, decided there wasn’t an attractive trade there after all. A reader wrote in with a great question about these sorts of articles:
“It seems like a lot of trouble to work out all of that analysis, only to decide that you’re not going to make any trade at all. … How often does this happen?”
He’s asking how often the conclusion of a bit of analysis is that there’s no edge to be had. For me, this sort of thing happens constantly. It is a bit of a bummer, since life goes quickly and analysis does not. Granted, it takes longer to write up your process as a publishable article, but a scan of a stock, its fundamentals, the options market and relevant factors (IV, HV, skew, order flow, etc.) still takes some time. I’m a big fan of technology because it makes that process faster and easier: If I’m only interested in momentum stocks, or in beaten-down value names, or in assets with rich implied volatility, those can all be scanned for. Even so, you can never know where an attractive trade will be until you look.
There’s a problem right now in scientific research, which is that no academic journal wants to publish a bunch of studies that have “no result” as their conclusion. Here’s a good article from Jonah Lehrer on this problem. It’s not just a problem in publishing, either: If a lab spends a lot of time and money on some project, of course they’d prefer some breakthrough or at least an interesting result rather than any old null hypothesis (no correlation, the drug has no effect, etc.).
The same challenge exists in finance. We’d all rather discover a worthwhile investment or attractive trade at the end of an analytic process. But if you’re honest with the data and with yourself, more often than not, you’ll conclude that it’s all just noise. That’s not a bad thing: Finding yourself confronted with a lot of “no trade” conclusions can be confirmation that your methodology is right, that your process is working properly. An investor who finds great investments wherever she looks is like a scientist who constantly uncovers new “cures” for cancer. Despite the cost in time and effort, negative results can be a wonderful thing.