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Knowledge or Patience?

One of the most provocative questions in behavioral finance is what causes some people to end up with lots of money, and some with very little. Yawn! you say! How is that provocative? It’s provocative causes lead to policy and private market solutions, if you want to treat problems. And the traditionally presumed causes are very different from the ones put forth by behavioral finance. So a recent paper by Justine Hastings and Olivia Mitchell looking at why certain people end up with more or less wealth is provocative because it goes against the common wisdom that the most important lever for improving saving and investment behavior is for financial literacy to be increased. While classic economics says it’s about rationality, behavioral economics says it’s about patience. In their own words:

Two competing explanations for why consumers have trouble with financial decisions are gaining momentum. One is that people are financially illiterate since they lack understanding of simple economic concepts and cannot carry out computations such as computing compound interest, which could cause them to make suboptimal financial decisions. A second is that impatience or present-bias might explain suboptimal financial decisions. That is, some people persistently choose immediate gratification instead of taking advantage of larger long-term payoffs. We use experimental evidence from Chile to explore how these factors appear related to poor financial decisions. Our results show that our measure of impatience is a strong predictor of wealth and investment in health. Financial literacy is also correlated with wealth though it appears to be a weaker predictor of sensitivity to framing in investment decisions. Policymakers interested in enhancing retirement wellbeing would do well to consider the importance of these factors.

To reiterate, patience is a better predictor of health and wealth outcomes than financial literacy. This implies that just teaching people about compound interest and diversification may not improve financial outcomes much. You need to change peoples ability to defer gratification, to be more patient. The earliest evidence I’m aware of about this was in the 1960s. You can read more about delayed gratification and life-long outcomes in this great New Yorker piece.

But they have now replicated the Mischel Marshmallow experiment, so we get to see what patience and self-control look like in action very early on in life (and very cute).

Training for more (or less) than the “main event”

A friend of mine is training for the New York Marathon, and while I’m not planning on running it, I am training with him. This in general has involved increasing the distance we’re running by about 2 miles every other week, and last week we finally hit 12 miles, nearly half a marathon.

Two things strike me about most training plans for marathons:

  • Your training doesn’t usually include distances more than the main event. This is unusual – usually it makes sense to train at levels worse than the main event. if I was training for a 5k, I would definitely run more than 5k, along with time trials over the actual distance. Marathon training plans generally max-out at training runs of about 22 miles however. I guess marathons take so much time and are so hard, you actually want them to be single events, otherwise you’d wear yourself out.
  • I can see the “train for less than the event” effect in my running times. I’ve been running about 10 miles every weekend for the past month. Each time I’ve finished, I’ve felt pretty tired, but good. This Sunday when I ran 12 miles, I could see that I wasn’t used to running more than 10: after 10 miles, my speed dropped off significantly, even though I was on a slight downhill. Likewise, my wife (who usually runs 4 miles) ran 6 miles, and slowed down right at about the 4.5 mile marker.

* Tracked using google MyTracks

This is tough – when training for an extreme event, you might not want to train as much, or even more than the main event because it can be so wearing. But it’s almost guaranteed that when go past your training distance, you’ll find the remainder of your run much more challenging.

When do investors feel regret?

Investor regret: The role of expectation in comparing what is to what might have been

– Wen-Hsien Huang — Marcel Zeelenberg

Investors, like any decision maker, feel regret when they compare the outcome of an investment with what the outcome would have been had they invested differently. We argue and show that this counterfactual comparison process is most likely to take place when the decision maker’s expectations are violated.

We found that decision makers were influenced only by forgone investment outcomes when the realized investment fell short of the expected result. However, when their investments exceeded prior expectations, the effect of foregone investment on regret disappeared.

In addition, Experiment 4 found that individual differences in the need to maximize further moderated the effects of their expectations, such that maximizers always take into account the forgone investment.

What does this tell us, and how can we use it?

  1. We’re  most likely to look for comparisons feel it in falling or sideways markets, when our expectations aren’t being met.  And unfortunately, cash will always be a willing accomplice to our regrets, providing a “what could have been” investment.
  2. Conversely, we’re unlikely to regret underperforming in rising markets, even if the market does better than us.
  3. Some of us (“maximizers”) need to look out for this tendency more than others – and you know this ahead of time by taking a maximizing/satisficing test.

The limits of diversification

Diversification is always good. It’s just limited in how much good it can do.

Diversification is achieved by adding assets into a portfolio which have correlations less than 1 with the portfolio. At its purest level, it reduce risk because not all assets will have the same gain or loss at the same time. By investing in different assets (which all have the same risk and return), we reduce the extreme movements of the portfolio, often in a way which doesn’t reduce the overall return quite as much.

To highlight how this works, let’s take an investment with an expected return of 6, and volatility of 12 (all example assets have these values in this post). We’ll mix the this asset equally with a clone of it, which is completely uncorrelated with the first asset. This reduces the volatility to 8.6 from 11 – a reduction in risk of 22%. The graph below illustrates what happens when we continue to add in more uncorrelated assets exactly like the first. The return stays constant, but the volatility continues to go down. But each time it goes down, it goes down a bit less.

We can look at this directly, and note how the decrease in volatility per increase in assets behaves. The ability to reduce risk falls off quickly, and we appear to hit a limit at a volatility of about 2.7.


The examples above give a very simple example of how diversification works, but they are unrealistic in a few ways. First, the assets are completely uncorrelated. It is almost impossible to find assets which are completely uncorrelated in the real world. Let’s run the same analysis again, but this time with a correlation of 0.5 across the assets.

Now we see that the benefits of diversification are strongly related to the (lack of) correlation between assets. If we run this analysis across all levels of correlation, and approximate the minimum level of volatility we can achieve, we get a graph like the one below. From a set of assets which all have an individual volatility of 12, we can reduce portfolio level volatility down to 2.7, but only if we have 20 uncorrelated assets. If the correlations rises to 0.5, our minimum volatility is much higher, at about 8.

So pure diversification – including uncorrelated assets with the same volatility level- always does help reduce volatility, but it the degree to which it helps depends on the correlations. And even completely uncorrelated assets have their limits.

Given that the correlations amongst equity markets worldwide tend to be quite high – about 0.7 – 0.8 – we need to set our expectations about what diversification can achieve realistically.

Illustrating the long-term vs short-term

A while ago the great graphics gurus (sorry) at the NYTimes created a very cool graphic showing the annualized returns of the S&P500 over a long time period:

This was one of the best graphics I’d seen in a while, but there are a number of things I thought could be improved, or used to illustrate another point.

  1. Red doesn’t mean loss. The light red in the picture means a return slightly above inflation. I think it’s misleading to color a real gain as a red loss.
  2. The grey is likewise misleading a bit. Grey is the 20 year median, which equates to 4.1% higher than inflation. That’s not boring and grey – that’s happy and green! It’s possible it’s grey because of how it compares with cash, but cash was not yielding 4% in real terms through much of that period.
  3. Everything is observed in years. But in the short-term, the first few months can really drive a years return.

What is great is that it’s actually fairly easy (ok, it did take me a few hours) to replicate this graph using completely free software. Using the statistical package R, you can literally run this file, and get the graphs below.

First up is just attempting to recreate the graph, which took a bit more manipulation than I expected, but was doable. Things to note:

  1. My data are not exactly the same. The NYTimes graphic adjusts for dividends, average taxes and fees, and inflation. In that respect, their data is superior to mine.
  2. Each cell on my graph represents an average annualized monthly return, even if it represents just one month. Annualizing a monthly return can produce some extreme values.
  3. I inserted a black line at the 1-year holding period.
[Standard disclaimer – this is not investment advice, just for illustrative purposes of what you can do, does not reflect the views of my employer, etc]
What this brings out a bit to me (not discounting point (2) above, is that most of the bright red occurs within the 1 year time period, along the diagonal. 


I wanted to focus the graph a bit more on the influence of holding periods on returns, so I rejigged the graph slightly to focus on holding periods. I really like the result:

  1. You can now see how much more volatile 1 year holding periods are.
  2. You can likewise see how there are very few cases when a nominal loss lasts for more than 10 years – almost never. Taxes, inflation and fees will make this graph look worse, but dividends will make it look better.
  3. It focuses me on the long-run. It shows that even if I had invested right before the  worst months of the 1970s, in about 7 years of holding tight I’d be in the same place as someone who’d invested right before the boom years.
  4. I look at this graph, especially every line longer than 15 years (and I’m definitely investing for more than 15 years), and see that it doesn’t matter if I invest now, or a year from now. Over that time frame, there is little risk around choosing when to invest.
  5. We are currently going through quite a tough patch, but the last time we had such a tough patch, it didn’t really affect returns above 12 years. I don’t know what the bottom half of this graph will look like in the future, but the top gives me comfort that it can’t be that bad.



What do you think?




Financial advice: (potential) market failure edition

The issue of what financial advisers are paid for comes up often, mostly when there is a question about whether they have fulfilled their fiduciary duty in giving advice. There are a variety of different models for how advisers get paid, and all have at least the appearance of some problem with them. There are two main issues:

  1. Agency: Are the advisers incentives aligned with yours – will they be a trustworthy agent? To my knowledge, no firm charges fees which are perfectly incentive aligned. Clients would have to agree to pay based upon how much value advisers have added compared to what the client would have done on their own – a hard counter-factual to assess.
  2. Quality: How do you know you the advice you are getting is worth paying for?

In the UK, the Retail Distribution Review is changing how financial advisers can charge for their advice, and many advisers will be moved to an up-front fee basis, or having to give “on-going” advice, i.e. initiating interactions with the client depending (usually) on what the market does.

To focus on the second issue, many advisers are quite rightly scared that this might hurt their business significantly, but mostly for a psychological reason – people have a real problem paying for up-front financial advice. They aren’t sure of the quality of it (the adviser often knows better than them), and they will not know if the advice was of high quality until a point far in the future.

This opens up a possibility of market failure to (among other things) asymmetric information. A client cannot directly assess an advisers’ quality ahead of time, and thus will discount the value of advice to incorporate the risk it is low quality. Advisers who give high quality advice will know it, and not want to accept low-quality prices for their high-quality advice. As a result, fewer people will pay (up-front) for financial services, because it seems too expensive relative to what they expect from it.

Recently my parents went to a financial adviser to help them plan their transition into retirement. They were a bit shocked by the $800 price tag for about 2 hours of time with the financial adviser – yet this is incredibly cheap by many standards. Wealth management and financial advisers often charge in the region of 0.60% up to 2.5% of assets under management per year, and clients have historically accepted those charges. My parents charge would have amounted to less than 0.005%, yet they were still floored by it.

Independent Financial Advisers already are a well-established market, but it will be interesting to see how people react if they have to pay up front.


The perverse role of debt in feeling wealthy

I had the pleasure of meeting up with Abby Sussman of Princeton last night, who investigates the psychology of wealth – assets and liabilities. Her recent piece in Psychological Science sums it up well:

We studied the perception of wealth as a function of varying levels of assets and debt. We found that with total gross wealth held constant, people with positive net worth feel and are seen as wealthier when they have lower debt (despite having fewer assets). In contrast, people with equal but negative net worth feel and are considered wealthier when they have greater assets (despite having larger debt). This pattern persists in the perception of both the self and others. We explore consequences for the willingness to borrow and lend and briefly discuss the policy implications of these finding

Here is a presentation laying it out. Get this:

Comparing two people in the black, 93% would rather have low debt and low assets. For people in red, 66% would rather have high debt and high assets.

In playing the role of a loan officer, comparing the two profiles in the black, 75% would grant a loan to the person with low debt and low assets. For profiles in the red, 74% would grant the loan to the person with high debt and high assets.

So if we have positive net wealth, we are motivated to pay-down our debt, to feel and be considered wealthier. If we have negative net wealth (underwater home owners, ahem), we are motivated to take on more debt.

And as outsiders, we apparently agree.



Government disclaimers and ineffective communication

From a working paper by Kesten Green and Scott Armstrong

We were unable to find evidence that consumers have benefitted from government-mandated disclaimers in advertising. Indeed, experiments and common experience show that admonishments to change or avoid behaviors often have effects opposite to those intended. We found 18 experimental studies that provided evidence relevant to mandatory disclaimers. Mandated messages increased confusion in all, and were ineffective or harmful in the 15 studies that examined perceptions, attitudes, or decisions.

Be careful in jumping to the conclusion that the reason is that the government mandated it, rather than that they are often poorly executed. Lobby groups may in fact spend a lot of time making sure the messages are ineffective. Note that when private enterprise has low incentives to communicate clearly, they don’t do any better. Credit card agreements are a great example, via Planet Money. The before-and-after examples are striking.

Edward Tufte has noted that smoking warnings are usually formatted as if they are meant to be ineffective, with an over-reliance on bold and underlining which actually overwhelms the message itself. I think this derives from non-experts in communication being responsible for what is, in essence, a marketing effort. Typing in bold caps FEELS LIKE YELLING, but that doesn’t mean yelling is effective in changing behavior.




“Fooled by Compounding”

A new paper in the Journal of Portfolio Management (non-gated copy available here). Abstract:

Compounding can make things appear to be larger than they really are. This confusion can arise when the return from an event is compounded over a long holding period, and the return from compounding is described as the return from the event. In this article, McLean reviews several examples of this common mistake, which are found in a popular book on rare events, newspaper articles, investment advisors’ research reports, and finance journal articles. He also shows how compounding can distort inference in event studies and in the measurement of mutual fund performance. McLean describes alternative methods of return measurement that are not affected by compounding and shows that these methods can lead to different inferences than do measures that include compounding.

While the examples and reasoning in the paper aren’t novel or groundbreaking, it’s yet another example of how easy it is to be mislead by compound or exponential growth.

Related: McKenzie, C. R. M., & Liersch, M. J. (in press). Misunderstanding savings growth: Implications for retirement savings behavior. Journal of Marketing Research. [pdf]

People systematically underestimate exponential growth. The current studies illustrate this phenomenon, its implications, and potential interventions in the context of saving for retirement, where savings grow exponentially over long periods of time. Experiment 1 showed that the majority of participants expected savings over 40 years to grow linearly rather than exponentially, leading them to grossly underestimate their account balance at retirement. Experiment 2 demonstrated that this misunderstanding of savings growth led to underestimating the cost of waiting to save, which makes putting off saving more attractive than it should be. Finally, Experiments 3-5 showed that highlighting the exponential growth of savings motivated both college students and real employees to save more for retirement. Making clear to employees the exponential growth of savings — just before they make crucial decisions about how much to save — may be a simple and effective means of increasing retirement savings.

Unexpected Utility

I believe unexpected utility* is one of the most under-researched ideas in behavioral finance and economics. I, for one, experience it occasionally, and it is the best kind of utility.

What, exactly is “unexpected utility”? It’s an experience, usually and hopefully positive, that you completely didn’t remotely see coming. The expectation is key here. Daniel Kahneman, in his recent book, uses the example that the same meal, when made by someone else, often tastes better. When you are making a meal, you are at some level pre-experiencing it – you think about what the ingredients will taste like, you are smelling them as you cook. By the time you actually eat you’ve actually experienced a reasonable amount of the meal.  Because we care much more about changes than we do levels, going from no-meal-experience to full-meal-experience, as you do in a restaurant, makes a much bigger impression.

It is one thing, for example, to enter into a prize draw for $100, and then win. At some level, you knew it was possible, and you’ve already thought about it, and at some level, experientially discounted it. Contrast that with finding $100 in the pocket of pants you haven’t worn for a while (yes, hard for me to imagine too). That would be much more happiness generating.

Because unexpected utility is so powerful, I really wish it was a bigger, more frequent part of our lives. If we all helped out someone who didn’t see it coming, for example, we’d probably make all of us quite a bit happier. If we set our lives up to have more unexpectedly good experiences (perhaps by doing safe, but novel things), we might end up surprisingly content.

Any ideas about how to encourage unexpected utility?


* The blog Unexpected Utility is good as well… And what a great name!