Nick Silver examines the perceived risk of an economic crash based on recent memory:
I decided to set up a rudimentary statistical experiment. Suppose it’s January 2008 and you’re an investor — or an economist — trying to forecast the probability of a major downturn in the United States economy. We’ll define such a downturn as occurring any time real GDP falls at an annualized rate of 4 percent in any one quarter, which is about equal to the decline experienced in the fourth quarter of 2008. Between 1947 and 2007, quarterly GDP fell by this amount on eleven separate occasions.About the simplest economic model one can build is to assume that fluctuations in GDP occur randomly and follow a normal bell-curve distribution. From there, it is reasonably straightforward to calculate the percentage chance of a “crash” — a 4 percent decline in GDP — in any given quarter.
If you had done this calculation in January 2008, using data from the past sixty years — roughly since the end of World War II — you would have estimated the chance of a crash in the upcoming quarter to be 3.17 percent, implying about one crash for every eight years of economic activity. Not exactly an everyday occurrence, but certainly something you would need to be prepared for.
But suppose instead that your time horizon is shorter. You decide to look only at data from the past twenty years — or essentially since Alan Greenspan took over as chairman of the Federal Reserve in 1987. With that time horizon, the risk of a crash appears to be very low indeed. In fact, you would assess the probability to be just 0.04 percent — meaning one crash for every 624 years. This deceptively simple choice of assumptions, then, turns out to make a huge amount of difference. If you’d looked at data from the mid-eighties onward, instead of from World War II onward, you’d have underestimated the risk of a crash by a factor of about 80.
Moreover, if you were to conduct this “crash-risk” calculation retroactively and graph it over time, you’d identify a couple of inflection points at which the potential impact of recency bias increases. Using a twenty-year time horizon, your perceived risk of a crash would fall dramatically as of about 1995, once you started to “forget” about the oil crisis of the mid-1970s — this happens to coincide with the beginnings of the dot-com bubble. Then a few years later, as of 2001 or 2002, the risk of a crash would fall almost to zero, as the economic turmoil of the early 1980s begins to be written off by investors. This happens to coincide with the start of the housing bubble.
It doesn’t necessarily take an unforeseeable combination of events, then, to precipitate a market crash like the one we’re now experiencing; a short memory span would suffice.