Good News and Bad News,  John Bagnold Burgess, 1876, courtesy Russell-Cotes Art Gallery & Museum, Bournemouth
Ideas for Leaders #459

Overreacting to Bad Financial News Can Lead to Poor Investment Decisions

This is one of our free-to-access content pieces. To gain access to all Ideas for Leaders content please Log In Here or if you are not already a Subscriber then Subscribe Here.

Key Concept

A University of North Carolina experiment involving investment choices confirms the neuroscience research that reveals how people learn differently from good vs. bad outcomes and when being exposed to positive vs. negative news. The result, the experiment shows, is a bias to too much pessimism when investors experience negative outcomes.

Idea Summary

Neuroscientific research reveals that the brain reacts differently to negative vs. positive outcomes; recent research in finance shows that market participants (including investors and investment professionals) draw different lessons from market conditions depending on whether conditions are good or poor. Professor Camelia Kuhnen of University of North Carolina’s Kenan-Flagler Business School designed an experiment involving investment choices to test whether people do indeed learn differently depending on whether they are in a negative or a positive domain. Kuhnen shows that people will overreact to a bad outcome or to bad news and draw overly pessimistic conclusions about the future — in this case, about the future value of the stocks in question. 

"People will overreact to a bad outcome or to bad news and draw overly pessimistic conclusions about the future..."

In the experiment, participants were shown a series of dividend payments for each of two stocks. For some participants, the dividend payments were negative; others saw positive dividend payments. Their mission: to give an estimate, based on the dividend payments they were seeing, on the likelihood of stocks coming from an overall good distribution or a bad distribution (that is, in the long run, an estimate that the stock would pay off better or worse). The figures were manipulated so that the positive or negative pay-outs were not correlated to whether the distribution was good or bad.

The results of the experiment show that that the probability estimates that the stock was paying from a good distribution were 3% to 5% lower for participants who saw only negative dividends — that is, those who were considered in the “loss condition” —than those who were in the gain condition.

As for the margin of error, loss condition participants were 2% - 4% more wrong (statistically) on the probability that the stock came from a good distribution. In short, people draw overly pessimistic lessons about investment options if they witness bad outcomes or read about bad news.

Expanding the number of participants and moving the experiment from the U.S. to another country (Romania) did not change the results.

Business Application

People learn differently from financial news depending on whether the news is good or bad. In the investment context, investors are going to be more pessimistic than warranted about stocks when they are learning from negative outcomes rather than positive outcomes, or when they are hearing bad news about the economy.

The fact that in a negative context we are biologically inclined to be more pessimistic than we should be is important when considering our decisions and actions during economic downturns or when facing setbacks. For example, business leaders may be underinvesting in productive activities or in human capital during downturns not because they are being wisely cautious but because they are hardwired to overreact to bad news.

Caution may be called for when faced with certain bad outcomes or poor conditions. However, reacting more pessimistically than warranted may only serve to hamper the potential recovery of the enterprise. Business leaders must watch out for the pessimism bias confirmed in this research.

Contact Us

Authors

Institutions

Source

Idea conceived

  • November 2014

Idea posted

  • November 2014

DOI number

10.13007/459

Subject

Real Time Analytics