Now, let’s show how this framework could be used for a longer-term value trade. Say that you believe that the insurance company AIG stock price will likely rise by 50% over the next year and a half. Like all large financial stocks, it is suffering both from wariness over the 2008 shock and its perceived opacity. AIG is especially tarred by its government bailout. It is trading at about half of tangible book value, and operating profits are strong. Because this is a value play, we do not want the spreadsheet to set a stop too close to the market; in fact, we want to scale down. (Of course, if AIG goes down, at some point we will have to admit we were wrong and get out.) “AIG assumptions” (below) shows the input spreadsheet for this trade on a monthly basis.
In this case, the trade is positive, although still below the price path. Because of this, the decision point is slowly dropping. The slowness was forced by the assumption that prices will rise by 50%. If you experiment with the spreadsheet, you will see that even prices of $25 would not stop us out this early in the trade. However, as time passes, the stop becomes more sensitive to lower prices.
Bayesian inference currently is used by a number of hedge funds and prop desks as a forecasting tool. Here, we learned how the principles can be used in an intuitive way. It is hoped this will encourage readers to incorporate these tools into their own thinking. Using the spreadsheet will provide a better feel for how the stop levels work and how much risk various decision rules let you take.
Burton Rothberg is a professional trader and a consultant to hedge funds. He can be reached at firstname.lastname@example.org.