In this series, I will discuss one psychological bias at a time and its ramifications for investors. How does this bias affect our decision making and how could we mitigate its negative consequences? This week we’ll discuss precision bias.
Precision bias is the assumption that more precise results are more accurate as well. In other words, if someone throws darts at a dart board and they all cluster in the left corner, the dart thrower was very precise, but not very accurate. If he would throw all the darts in the bulls eye he would be precise AND accurate. Fallible human beings get misled by exact estimates. They confuse accuracy with preciseness.
In which areas is precision bias prevalent? Policy making comes quickly to mind. Government forecasts are perhaps the most obvious suspects. But precision bias also prevails in financial reporting: both in the (financial) news as in (government) statistics. But even when it comes to investment analysis many widespread habits are shaped by the precision bias. How exact are our stock valuations? When we talk about valuations, do we talk in precise terms or do we offer a range of possible valuations? Do we consider exact economic and/or industry variables or do we assume a range of possible figures?
It is ironic to see how in my country the Central Planning Bureau (CPB) has elevated to the status of an oracle among politicians. During election time, political programs are compared as in how many jobs they will create, as modeled by these macroeconomic forecasters. Such theatrical performance is no less than a sheer disrespect of reality. A macroeconomic bureau could never foretell or has actually succeeded in foretelling the exact employment on a certain future date. What sense does it make to spend time debating hypothetical economic variables that are more often than not entirely mistaken? If these forecasters cannot forecast the, for instance, exact employment, then politicians are not capable of comparing policies. They choose to have a wrong map, say the map of Paris in Amsterdam, instead of no map at all.
Recently, the Dutch Central Planning Bureau (CPB) predicted a 1% growth of GDP in 2014. Note that, even with a track record characterized by years of terrifying misses, they report figures as exact estimates, not as ranges. On the surface, they appear to be as precise as a chemist that determines the quantity of oxygen in a lab.
Nobel Prize winner Friedrich Hayek always criticized this exactness in economic forecasts and estimates. He argues that economists can only make “pattern predictions”, e.g., an increase in the supply of eggs lowers the price of eggs. But even then I highly doubt the scientific value of such statements. Forecasting is an art. This becomes painfully clear when we consider the inherent assumptions that even the above statement about the price of eggs implies: assumptions about what the supply of eggs include (pastured, grass-fed, Omega 3, corn-fed), what buyers consider as eggs, and that the changes are meaningful enough to spur sellers to change their psychologically advantageous retail prices. The innate subjectivity in economic science prevents the economist from any meaningful scientific forecasts that are not permeated with if and ceteris paribus statements. Forecasts remain a tool solely for investors and bureaucrats.
In sum, interpreting statistics or economic forecasts without taking into account their inexact nature is rather silly.
But let’s face it. Sooner or later investors have to pick a number. Economic growth, cash flows, profit margins, costs, debt finance costs, sales figures, whatever.
One of the ways to counter precision bias is by scenario planning. With scenario planning, you use historical data to compare the value of an investment according to a number of different scenarios. But, as Nassim Taleb justly argues, this is a more or less contradictory idea. A worst case scenario by historical standards is a worst case until a worse case emerges. But then, more often than not, you’re already broke.
From 1988 to 2001, the worst trading day in the Dow Jones equaled a loss of 508 points. Until the Dow lost 685 points in 2001. Likewise, from 2001 to 2008 the worst trading day equaled a loss of 685 points. Until the 778 points loss in 2008. Assuming history to be a solid guide for future events can be tricky, dangerous, and costly.
It is interesting to note that Seth Klarman arrived, I suspect, at a similar conclusion. He uses different scenarios when valuing investments, but often resorts to hypothetical assumptions. Assuming extreme conditions, even if they have no historical basis, challenges your own thinking and sharpens the mind. If a potential investment can even endure events without any historical precedent, then you have found gold. Klarman has incorporated, knowingly or unknowingly, the notion of the black swan in scenario planning. If you assume Armageddon, everything less worse will be way better.
Precision bias teaches us three important lessons: take exact estimates with a grain of salt, always use ranges instead of exact figures (aside from temperature measurements perhaps), and use scenario planning with hypothetical and extreme situations. Never forget: more precise is not necessarily more accurate.
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