For individual investors, knowing the right principles of investing and finance are invaluable, but each person must be open to investing effort in learning them.
I used to live in London, where taxi drivers have to learn The Knowledge, a comprehensive test to gauge their familiarity with London’s streets. It means that as a rider, you can guarantee that your black-cab driver knows where he or she is going.
While The Knowledge was very valuable in a pre-GPS world, it provides far less of an assurance now. A GPS-enabled service knows the streets just as well as, if not better than, a human driver will. But, in addition, a digital navigator knows where there is construction before the driver hits it, knows where a traffic jam is as soon as it happens, and can avoid slower roads as measured by other travelers in real time.
Education? Not So Productive
For decades, investment managers have been trying to provide a form of The Knowledge to investors—white papers, books, webinars, lectures, articles, charts, and so on. But, for as much as information that has been put out there for the public to consume, it still doesn’t appear to be helping much. The average investor still significantly underperforms the S&P 500, according to 2014 data from DALBAR.
“It is now past the time for the investment community and its regulators to understand that the principle of educating uninterested investors about the complexities of investing is unproductive,” said Louis Harvey, DALBAR’s president in a press release accompanying the report.
Bingo. It’s usually unproductive.
That’s why the latest development toward automation and Web-based robo-advising can do for investors what education can not. It’s like driving yourself with a really good smartphone, rather than a (grumpy) driver who is trying to rely on his or her own interpretation of the knowledge.
Why Algorithms Are Productive
Technology and algorithms are great and can be better than humans at some tasks. Here are some examples, popularized by the the Nobel-prize winning psychologist Daniel Kahneman:
- Anything that requires computation, analytics, or data processing
- Outcomes that need to be precise and accurate
- Things that need to be done on a consistent basis
- Considering many variables concurrently in multi-dimensional space
- Availability (i.e., always on, always working, always monitoring your investments)
- Not biased (i.e., won’t give different advice depending on your gender, appearance, race, etc.)
- Inexpensive (i.e., near-zero marginal cost to deploy)
While a well-designed algorithm can’t always make a decision for an investor, an algorithm in tandem with smart design can go a long way towards just that. The algorithm does the complicated data analysis to generate information, which good design can reduce down to the best potential options. It does this by eliminating noise and clutter, asking helpful questions to prompt action, presenting only the relevant information and data, thus permitting investors to only choose between trades-offs they actually have to make.
What about financial education?
Of course, this doesn’t mean investor education doesn’t have its place. For individual investors, knowing the right principles of investing and finance are invaluable, but each person must be open to investing effort in learning them.
Financial education works if the person has the time, educational background (e.g., math, legal, logic), and, critically, motivation to benefit from the education.
Research shows that when an investor is motivated to learn the nuts and bolts of investing, he or she will be better at it for a short period of time.
However, because most people aren’t (and shouldn’t be!) managing their investment all year round, it’s a big hurdle to keep those skills finely honed. And the rules on various things change, with potentially complicated implications.
The reality is that most people don’t have the time or motivation to get into the intricacies of optimizing their investments. That’s where automation is playing an increasingly critical role in helping people meet their core investing needs, such as saving for retirement.
Smart Web design can bridge this gap, providing information at only the right time when needed and in the format required to make a decision.
Based on extensive research on behavioral finance, some of the things I consider to be good Web design in the context of investing include:
- Displaying only the relevant choices and information (like both risk and taxes)
- Eliminating the need for the investor to do math or engage in complicated logic (e.g., yesterday’s return)
- Presenting the outcomes, not the methods or means used to achieve them
- Communicating in dollars, not shares
- Presenting clearly all the impacts of an investment decision, such as the tax cost of making an allocation change
- Using salience, such as color and size, to judiciously prompt the right actions
- Ensuring the default option is usually the best choice based on the information available
- Focusing on the future, not the past
At Betterment, for example, we don’t include the performance of the individual ETFs we hold in our portfolios. While that information is available for any investor who wants to research each fund’s ticker, we believe in looking at the sum of all parts—not each part—in order to correctly understand your performance.
Showing ETF level returns encourages performance chasing, ignores diversification benefits, and doesn’t correctly represent how dividends are reinvested.
This is a perfect example of investor education (e.g., information) hurting more than it helps. Let’s do the opposite as some financial advisors—let’s help people make smarter decisions through well-researched and well-designed technology.
This article originally appeared in Investment News.
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