How to Overcome Anchoring When Investing
Anchoring can lead to poor investment decisions. Here's how to help clients avoid it.
This is the 17th article in the Behavioral Finance and Macroeconomics series exploring the effect behavior has on markets and the economy as a whole and how advisors who understand this relationship can work more effectively with their clients.
When required to make an estimation, people generally begin by envisioning some initial, default number--an "anchor"--which they then adjust up or down to reflect subsequent information and analysis. This is referred to as anchoring bias, an information processing bias.
Numerous studies demonstrate that, regardless of how the initial anchors were chosen, people tend to adjust their anchors insufficiently and produce end approximations that are, consequently, biased. We are generally better at estimating relative comparisons rather than absolute figures.
Suppose you are asked whether the population of Canada is greater than or less than 30 million. Because of the way the question was phrased, you will obviously answer either above 30 million or below 30 million. If you were then asked to guess an absolute population value for Canada, your estimate would probably fall somewhere near 30 million. This is because you are likely subject to anchoring from your previous response.
Similarly, anchoring is used as a negotiating tactic for, say, buying a car. Suppose you go into a dealer and test drive several cars. You are impressed, and are in the mood to actually buy a car. The salesperson has been charming and likable. Now comes the moment of truth--the price negotiation. Most people tend to want to let the other party "declare" a number to start the negotiation. The problem is, people tend to get anchored to a certain number. If the salesperson puts forth a price. you will likely get "anchored" to that number, and as a result, that price sets the standard for the remainder of the negotiation. You may inadvertently pay more than you would like because you became anchored to that initial number.
In the investment realm, it has to do with selling a security. For example, suppose you buy a stock for $100. If you are subject to anchoring bias, the decision to sell or hold the stock will be based on the purchase price you paid--$100--and will have little to do with the actual value of the stock based on a rational analysis. Let's say a stock has gone down from the initial purchase price: You paid $100 for a stock that is trading at $80 today, and the company faces challenging circumstances. The reference price on a decision to sell or hold the stock should be based a rational assessment of the company, its challenges, and its stock price, but those afflicted with anchoring bias will base their buy or sell decision on the $100 initial purchase price.
At the macro level, investors tend to anchor on the current level of a given index as their reference point. And when they make a general market forecast, they use a range that is very close to whatever the current level is rather making an estimate of future returns based on historical standard deviation. (As you likely know, standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A standard deviation close to zero indicates that the data points tend to be very close to the average of the set, while a high standard deviation indicates that the data points are spread out over a wide range.)
Let's say the S&P 500 is at 2700. Depending upon what time period is used, the standard deviation of S&P 500 returns varies. We will assume the standard deviation is 17%. This means that 68% of the time, the S&P 500 return was between the mean plus/minus one standard deviation; 95% of the time, the S&P 500 return was between the mean plus/minus two standard deviations; and 99.7% of the time the S&P 500 return was between the mean plus/minus three standard deviations. At one standard deviation, using a 2700 value for the S&P 500, the range of returns is plus/minus 17% of 2700, or a range of 2241 to 3159. And as you can see that at two standard deviations and three standard deviations, the range of returns gets very wide indeed--much wider than investors who anchor on the current level of the market expect.
The implication of such anchoring is that large numbers of investors may not be ready for a market correction, and when it occurs, they may panic. This is what happened during 2008-2009. Of course, not everyone knows the standard deviation of the stock market and therefore understands the possible range of returns. Nonetheless, even those who do know this information may still be subject to anchoring in estimating future values of the S&P 500.
Anchoring is one of the most common biases that I see in practice. It is very natural for people to use a purchase price or current index level as a reference point to make a future decision. Recognizing when anchoring occurs can help immeasurably in advising clients about investments. The trick, of course, is to be able to discuss anchoring with clients and subsequently do something about it. When making forecasts about the direction or magnitude of markets or individual securities, ask yourself/your clients this question: "Is my estimate rational, or am I anchored to the current index level or price I paid?"
This is a difficult but essential task that can help you and your clients understand the motivations at play. Taking this sort of action can proactively root out any anchoring bias that might take hold during asset sales or asset reallocations.
Michael M. Pompian, CFA, CAIA, CFP, is the founder and chief investment officer of Sunpointe Investments, an investment advisor to family offices based in St. Louis, Missouri. His book, Behavioral Finance and Wealth Management, is helping thousands of financial advisors globally build better relationships with their clients. Contact Michael at email@example.com.
The author is a freelance contributor to Morningstar.com. The views expressed in this article may or may not reflect the views of Morningstar.