Many investors at least occasionally worry that they are not spending enough time monitoring their investments and trading to optimize the returns on their portfolio. But according to finance professor Janice Eberly of the Kellogg School of Management, investors could actually be worse off if they pay too much attention to their portfolios. Her research suggests that the costs of monitoring may at times outweigh the benefits.
“In many circumstances, it is optimal for investors to ignore the stock market for long periods of time,” Eberly reports in an interview. “I think of it as the optimality of procrastination.”
Time-Dependent and State-Dependent Rules
Eberly and her colleagues Andrew Abel and Stavros Panageas of the University of Pennsylvania developed a model that, unlike previous microeconomic models, considers both time-dependent and state-dependent rules by which individuals adjust their portfolios. “Time-dependent rules depend only on calendar time and can optimally result from costs of gathering and processing information,” the researchers explain in a working paper. “State-dependent rules depend on the value of some state variable, typically reaching some trigger threshold, and can be the optimal response to a transactions cost.” In essence, a transactions cost is a monetary cost associated with making a change, such as a fee that a mutual fund charges to reallocate an investor’s assets. Other costs typically associated with any kind of economic decision are time and attention costs, such as foregone leisure (i.e., a missed round of golf).
In many circumstances, it is optimal for investors to ignore the stock market for long periods of time.The authors find that the behaviors described by time-dependent and state-dependent rules are not mutually exclusive, because an investor may face both costly information and costly transactions. “In the general case, the chooses a future date to gather information and re-optimize, but that future date may be state-dependent,” they write. “Moreover, conditional on the information observed at that future date, the agent’s action (or lack thereof) may also be state-dependent.”
“It turns out that these two kinds of costs are complementary,” Eberly elaborates in the interview. “If it’s costly to make a decision or to gather information, then you only want to do that if you think you’re actually going to act on the information. In addition, if it’s costly to make a change, then you’re only going to make a change if you really need to—for example, if your current portfolio is out of balance.”
How often an investor should check his or her portfolio depends on how costly it is to do so. Eberly and her colleagues calculate that even when costs are as minute as 0.01 percent of total wealth, the optimal inattention period is almost eight months. If the cost increases, the optimal inattention period increases. For example, if the costs are 0.1 percent of total wealth, the optimal inattention time increases to more than two years.
The rate of return on investment is another factor that can affect the optimal inattention time. It might seem that it would be best for more risk-averse investors to check their portfolios more frequently, but the new model shows that the opposite is true. Eberly explains that investors who are more cautious choose safer portfolios. Because a less aggressive portfolio grows more slowly, it does not need to be optimized as often, and the inattention period can be longer. Conversely, investors who embrace risk and have riskier portfolios will generally experience higher rates of return. As a result, their wealth will grow faster, and they will need to adjust their portfolios more frequently.
Eberly and her colleagues note that changing the assumptions in the mathematical model changes the optimal inattention interval. For example, if you look at an investor who has wealth in risky equity, riskless bonds, and a riskless liquid asset, optimal inattention times may vary depending on whether or not the investor receives a regular paycheck.
“The message is very intuitive and low-tech,” Eberly maintains. “It just takes a fair amount of math to show that sometimes nothing is actually the optimal thing to do.”