Optimal Behavior

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.

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.

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.

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.

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?

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

A problem I occasionally encounter when trying to improve behaviour in the real world is the fairness of control groups. Imagine you have a potential cure for a problem, but you have no proof that it actually works. A standard experiment would randomly allocate individuals to treatment and control conditions, run the experiment for a set period of time, and compare outcome variables. You’d then know if there was a strong effect.

However, suppose the treatment turns out to be effective. Those individuals who did not receive it- control group – could claim that they had been treated unfairly, as others received a potential benefit which they did not. The fact that the treatment was suspected to improve outcomes is sufficient to indicate that an unfair advantage was being conferred, despite the fact that it was not yet known to be effective.

Sometimes, not always, this fear works against our ability to create new knowledge, and definitively improve outcomes. A business confronted by such a prospect might have the following thoughts:

-          This might improve our clients’ outcomes, but we have no proof.

-          If we run an experiment, those people in the control might feel they have been treated unfairly, and either stop being customers, or even worse, sue us.

-          We’d therefore have to give it to everyone.

-          But then we’d have no proof that it helped.

-          Let’s just not do it.

Note that in this case, no-one benefits from the potential improvement.

I’ve been surprised how often this chain of thought is worked through, and the odds seem 50/50 that they come out this way. It takes more courage to find the truth than to continue with the status quo.

Related: Subjecting fewer patients to ineffective treatments

A problem I occasionally encounter when trying to improve behaviour in the real world is the fairness issues around control groups. Imagine you have a potential cure for a problem, but you have no proof that it actually works. A standard experiment would randomly allocate individuals to treatment and control conditions, run the experiment for a set period of time, and compare outcome variables. You’d then know if there was a strong effect.

However, suppose the treatment turns out to be effective. Those individuals who did not receive it- control group – could claim that they had been treated unfairly, as others received a potential benefit which they did not. The fact that the treatment was suspected to improve outcomes is sufficient to indicate that an unfair advantage was being conferred, despite the fact that it was not yet known to be effective.

Sometimes, not always, this fear works against our ability to create new knowledge, and definitively improve outcomes. A business confronted by such a prospect might have the following thoughts:

-          This might improve our clients’ outcomes, but we have no proof.

-          If we run an experiment, those people in the control might feel they have been treated unfairly, and either stop being customers, or even worse, sue us.

-          We’d therefore have to give it to everyone.

-          But then we’d have no proof that it helped.

-          Let’s just not do it.

Note that in this case, no-one benefits from the potential improvement.

I’ve been surprised how often this chain of thought is worked through, and the odds seem 50/50 that they come out this way. It takes more courage to find the truth than to continue with the status quo.

Related: Subjecting fewer patients to ineffective treatments

I’m occasionally participating in an on-going forecasting competition (Forecast World Events), and the financial forecasts in particular are interesting. Today I received a request for a forecast relating to the S&P500:

What will be the largest single-day GAIN or LOSS for any trading day between 2012-2-06 and 2012-02-10?

Allocate 100% probability across these categories:

1. <1%
2. 1% to 1.99%
3. 2% to 2.99%
4. 3% or more

The status right now is below:

Unless you have very precise estimates of what will happen over the next 5 trading day, a good estimate would come from history. So using the incredibly convenient R program (code below), I pulled historical data on the S&P500. We can check this question in history a couple of ways:

1. Using all week periods since 1980
2. Using all week periods in February since 1980

The results are surprisingly consistent with history. As of Feb 4, 2012, the consensus was expecting the upcoming week to be more volatile than history, with double the odds of seeing a 3%+ movement up or down, and double odds of a movement between 2 and 3%. The modal historical outcome of between 1% and 2% is underweight by about 13%, not a massive difference.

What would be interesting is seeing how “common” this view is, i.e. is the consistency with history because the majority of people are close to history, or are a majority of extreme positions cancelling each other out?

R Code:

rm(list=ls())
install.packages(quantmod)
library(quantmod)<
sp500<- getSymbols("^GSPC",auto.assign=FALSE,from="1980-01-01")
rets<-periodReturn(sp500,period="daily")
cat.rets<-as.data.frame(rets)
str(cat.rets)
cat.rets$ret.cat=NA
cat.rets$ret.cat[abs(cat.rets$daily.returns)<0.01]=1
cat.rets$ret.cat[abs(cat.rets$daily.returns)>=0.01]=2
cat.rets$ret.cat[abs(cat.rets$daily.returns)>=0.02]=3
cat.rets$ret.cat[abs(cat.rets$daily.returns)>=0.03]=4
cat.rets$year<-as.POSIXlt(as.POSIXct(strptime(as.character(row.names(cat.rets)),format="%Y-%m-%d")))$year+1900
table(cat.rets$year,cat.rets$ret.cat)

cat.rets$dow<-cat.rets$dow<-as.POSIXlt(as.POSIXct(strptime(as.character(row.names(cat.rets)),format="%Y-%m-%d")))$wday
#Using Monday as anchor, define greatest return over the next 5 days

cat.rets$max.week.ret<-NA
for (i in 1:nrow(cat.rets)) {
  if (cat.rets$dow[i]==1) {
    j=i+5
    cat.rets$max.week.ret[i]<-max(cat.rets$ret.cat[i:j],na.rm=TRUE)
  }
  else next
}

table(cat.rets$max.week.ret)
prop.table(table(cat.rets$max.week.ret))

################################
#Just Februaries
nrow(cat.rets)
cat.rets$month<-as.POSIXlt(as.POSIXct(strptime(as.character(row.names(cat.rets)),format="%Y-%m-%d")))$mon
table(cat.rets$month)

cat.rets$max.feb.week.ret<-NA
for (i in 1:nrow(cat.rets)) {
  if (cat.rets$month[i]!=1) {
  if (cat.rets$dow[i]==1) {
    j=i+5
    cat.rets$max.feb.week.ret[i]<-max(cat.rets$ret.cat[i:j],na.rm=TRUE)
  }
  }
  else next
}
row.names(probabilities)<-c("<1%","1-2%","2-3%",">=3%")
prop.table(table(cat.rets$max.feb.week.ret))
probabilities<- cbind(c(.13,.37,.32,.18),prop.table(table(cat.rets$max.week.ret)),prop.table(table(cat.rets$max.feb.week.ret)))

An article in the most recent JDM makes two valuable points – sometimes winning is losing, and we’re ok with that. Some individuals are more likely to over-bid, purchasing a lottery for more than it’s best possible value. Why might they do this? Because to some people, very competitive people, it seems that “winning” is as (more?)  important than making a genuine profit.

This effect is probably reinforced in circumstances that meet the Winners Curse, in which optimism about ones incomplete information is compounded by a desire to “win”. The very word winning has such positive connotations, how could we not want to win an auction?

For some people, the joy of winning is worth over-paying. And these are the people you DEFINITELY want to invite to your poker game.

The second half of last year was not kind to my waistline. Between getting married and leaving the UK there were many causes for dinners and drinks out, which always seem to be less healthy than those at home. So when I got settled in the US, I was very disappointed to see I was about 20lbs (1.5 stones, or 9kg)  overweight.

However, unlike many problems in life, that problem is simple, very solvable and controllable. If you want to lose weight, you must expend more calories than you consume. In many ways, it is like a financial budget – if you want to save (lose weight), you must spend (eat) less than you earn (expend). This is not to say it is always easy, but is is straightforward and doable.

However, how do we track how many calories we consume, and how many we expend? This has historically been the biggest problem with calorie counting diets – they tended to be very imprecise in both foods, and exercise. Many diet programs were based on eating specific foods so that you knew exactly how many calories you consumed. But now, we have a wide variety of applications which can help us to keep track of our calories, both consumed and expended. As an android phone user, I use MyFitnessPal to keep track of calories. I’ve rarely not been able to find the food I was looking for in it.

And these programs lead us to understand exactly the trade-offs we need to make. To lose 1.5 lbs/week, I need to consume, net of exercise, 1520 calories. This doesn’t mean I only eat 1520 calories though – it means I can earn more by exercising. It makes explicit the fact that if I want to drink another beer I need to run for at least 10 minutes. So a night out on the town will cost me about an hour of exercise, which seems like a good deal in both ways. The fact that the approach of equating food to exercise has beneficial effects therefore doesn’t surprise me.

In this way, calorie counting is actually quite useful and allows me to know precisely how much I can eat, or how much I need to exercise at the end of the day. The fact that these trade-offs are made explicit what makes it difficult for us. I have to decide between both drinking a beer and doing 10 min. more exercise, or not doing either.

Because trade-offs are uncomfortable, people seek to trick themselves into this behavior without explicitly counting calories.  They use diets such as only eating meat, eating only during certain hours or the day, or only eat certain foods so that they can achieve exactly the same thing. Almost all forms of diets seek to restrict caloric consumption. Some of them do seek to change your metabolism as well, however at the end of the day if you are losing weight you are doing it by expending more calories than you consume.

This is a clear case the difficulty lying not in knowing what to do, but simply doing it.

However, contrast those budget trade-offs with alcohol consumption. I have no way to earn more alcohol units. I cannot exercise or do any other virtuous behavior the results in being able to drink more and maintain my health. Simply put there is a natural limit which I have no ability to increase. Now being able to make those calorie trade-offs seems great. I have the ability to earn more.