Leveraging Persuasion Science to Nudge Investors in a Better Direction
Making choices can be difficult, but possible alternatives can be arranged in a way that facilitates effective decision-making.
This is the idea behind “choice architecture,” an concept that is explored in the book, “Nudge,” by Richard Thaler and Cass Sunstein. Here is a link to the “Choice Architecture” chapter, written by Thaler, Sunstein and Balz, which appears in “The Behavioral Foundations of Public Policy.”
The chapter highlights some principles that can be applied when designing the context in which decisions will be made. We’ve reordered them to fit the NUDGES acronym.
- Understand Mappings
- Give Feedback
- Expect Error
- Structure Complex Choices
While these principles have important implications for public policy, we felt the framework could also be used by advisors and DIY investors to make better decisions. We’ve outlined some basic finance-related ideas to put some “meat on the bones” of the NUDGES framework.
Informed choice involves an understanding of incentives. Charlie Munger has a great attributed quote that illustrates this point:
I think I’ve been in the top 5% of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it. And never a year passes but I get some surprise that pushes my limit a little farther.
Even Charlie Munger underestimates the power of incentives. Thaler suggests we ask four questions when determining incentives:
- Who uses?
- Who chooses?
- Who pays?
- Who profits?
Financial Market Application: Align Incentives and Keep Fees Low
In investing, different intermediaries have different incentives. We wrote here about bank distribution economics and the conflicts of interest these create. Sometimes managers have incentives to raise assets, even when this may hurt their performance (Wes’s WSJ post on this subject). When one understands the incentives at work, it becomes obvious. Upton Sinclair had a marvelous quote that makes clear why: “It is difficult to get a man to understand something, when his salary depends upon his not understanding it.” Now that’s timeless wisdom!
Some decisions are easy. When picking a perfume, do you prefer Chanel No. 5, or Harley Davidson perfume? Other decisions are much more difficult. How should you treat a newly identified cancer — with surgery or chemotherapy?
In order to understand the tradeoffs of such a decision, which might involve both probabilities and patient preferences, you need to be able to accurately map the relationship between different choices available and how they affect, or map back to your ultimate welfare. What are the risks of each approach? How do outcomes differ? Effective mapping makes abstract information more comprehensible by translating it into concrete concepts.
Financial Market Application: Value Investing > Growth Investing
There are lots of expensive stock investors out there, which is strange, given the poor historical performance of this approach. In investing, it’s critical to understand mappings as they relate to value versus growth investing. Growth stocks tend to be high quality companies, with good prospects. The problem with investing in such high quality companies is that everyone else already knows about their quality, and the price already reflects this. That’s why they’re growth stocks. Any time you invest in a stock that is not cheap, you run the risk that you will get a negative surprise that will hurt you. Thus, an important mapping for investors to understand is that growth stocks are a statistically a bad bet, on average. Understanding why value investing is a good idea and growth investing is a bad idea can inform your investing choices and result in better returns in the long run.
Making choices is cognitively demanding, and any change from a baseline reference point can be perceived as risky. We are wired with a preference for the status quo. Since we tend to gravitate to the path of least resistance, when possible, we should align such default paths with our interests. Ideally, our defaults will be normal practice, and we should set up defaults that protect us.
Financial Market Application: Invest Systematically
Everybody wants to be a stock picking genius. The problem is that it requires constant active decision-making. Should I buy or sell? What can I conclude from evolving market conditions? While we want to be skillful and talented stock pickers, most of us are no good at it. We’re dominated by our emotions, and are predictably biased. Even the so-called “experts” are no good at it. Meanwhile, there’s a lot of evidence that suggests that simple models beat experts across many domains, including investing, as we discuss in this post. A sensible investment “default” setting is to rely on a systematic process. It’s the ultimate “do nothing” default. Minimize your emotions, take your potentially flawed decision-making out of the picture, and default to less discretionary involvement in your own portfolio, since you can be your own worst enemy. If you need to make your own stock picks, establish a “fun bucket,” consisting of no more than 5%-10% of your capital. Can’t figure out how to “time” your allocations? Just go with a dollar-cost-averaging approach as a default.
When you provide feedback to people not only when they are doing a good job, but also when they are making mistakes, this helps them learn and enhance their performance. In particular, you can provide warnings when things are going wrong, or are about to go wrong. An example would be a “Low Battery” notification on a laptop, or the “Low Fuel” indicator in your car.
Financial Market Application: Measure and Benchmark the Performance of Your Investments
It’s surprising how few investors know the true performance of their investments, and perhaps this is for good reason — keeping investors in the dark can hide performance warts. Another reason investors don’t know their true performance is that accurate benchmarking can be difficult. For example, if you are pursuing a concentrated value strategy, you may want to measure shorter-term performance against a similarly constructed value index, as opposed to a broad based passive index. Getting feedback that is both useful, accurate, and not counterproductive is extremely difficult in financial markets. We’d love to hear how folks have solved this problem.
Since human beings are not perfectly rational automatons, we make mistakes. Lots of them. For instance, Thaler describes “postcompletion error,” such as when you leave your gas cap behind after refilling your car with gas, or when you leave a document in the photocopier after making a copy. Once the main task (which taxes working memory and cognition) is finished, people tend to forget to do the follow-on tasks. Given that we can expect to make such mistakes, Thaler discusses a solution. Employ a “forcing function,” which is an operational constraint that prevents errors. An example is ATMs, where you must take your card back in order to get your cash. (Great design, and I wish I’d had this feature on the ATMs where I’ve left my card many times)
Financial Market Application: Automate Your Rebalance or Go Passive
In investing, investors complete the main task of a baseline strategic allocations, but then fail to rebalance their accounts regularly. The rule of thumb on rebalancing is that in general, a more frequent rebalance is better, as we discuss in this post, although commissions and transaction costs can create a point of diminishing returns for higher frequency. Set up a forcing function. In today’s world of ultra-low transaction costs, we believe a monthly rebalance will enhance returns in the long run. Calendar a rebalance for your portfolio every month, rain or shine, and stick to it like grim death.
Investors may want to also consider pure passive investing, even if they believe a more active strategy can outperform over the long term. The downside of a more active strategy is it may trigger us to sell at exactly the wrong time or buy at the wrong time, thus defeating the purpose of owning the active strategy in the first place. If we expect error, and have no way to defend against error, perhaps a passive approach is a solution.
Structure Complex Choices
When people make choices based on a large number of complex options, our usual decision-making strategies can break down. Good choice architects can affect outcomes by providing structure that aids decision making. For instance, when choosing an apartment, one can establish cutoff standards (e.g., commute length, square footage), and practice “elimination by aspects.” Other simplifying strategies can be used to assist people when they face daunting complexity.
Financial Market Application: Simplify With Quantitative Risk Management Rules
Markets are chaotic and complex. Investors often overreact to the financial media, and can get caught up in irrational exuberance or lose their nerve when the market drops. Although a volatile market is pretty much the definition of complex environment, some investors still try to time the market, or invest based on market conditions. Today, many are waiting for the right “pullback” to invest, although the market could continue to get more expensive over many years into the future. In a complex market environment, a simplifying strategy that adds structure to risk management is the use of moving average and time-series momentum rules, as we discussed in this post. Again, remove yourself from the chaos, and restructure complex choices as more simple choices.
While choice architecture concepts apply well to the public policy domain, we hope this post illustrates that they can also work in an investment context. You’re only limited by your imagination in how to apply them. By designing environments that allow for more effective investment decision-making, you give yourself the best chance for investment success. In an uncertain world, where equity, bonds, and other asset classes are expensive, it makes sense to leverage the emerging science of choice and “nudge” ourselves to make better investing decisions.
Please share your thoughts with our community. We have merely scratched the surface with respect to choice architecture and how it might apply in financial services. We’d love to hear your ideas/thoughts.
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