Good Behaviour – Daniel Egan

Relevant NYTimes article on varying needs for financial advice.

There are tens of thousands of advisers out there, with varying levels of expertise, who charge varying fees for their services. And professional advice doesn’t guarantee good returns. You need look no further for evidence than the market collapse of 2008-9, when most people lost money, even those with the supposedly ideal mix of investments, hand-picked by their financial planners.

First, the idea that financial advisors help you escape risk is wrong. Even an all-cash or AAA bonds investment is subject to risk, as inflation means the outcome is still uncertain. Second, the purposed of that “ideal mix” of investments is meant to get the best expected return, given the amount of risk you are comfortable taking on. Risk is never removed, and you should expect to experience losses at some point during your investment.

The big question is why and when is it worth paying for financial advice? The crux is the different types of “cost” you need to avoid depending on your circumstances. Going it alone incurs the cost of beginners’ mistakes- bad tax choices, low diversification, high-cost products, and most crucially, churning your investments over the market cycle. Paying a financial advisor incurs their fees and the possibility they guide you to investments which make money for them, but aren’t perfect for you.

Generally, the higher the stakes (your wealth level), the more important it is to use an FA. The potential long-term costs of beginners mistakes became very large as you get serious about investing, and you will learn the qualities of a good financial advisor quickly. They are focussed on controlling costs, the long-term view, asset allocation, and most critically, understanding the nuances of who you are. And, having a second opinion helps make less irrational decisions over the market cycle.

Unless you wake up and see Warren Buffett in the mirror, it might be worth it to pay someone to stay the course.

The final point is quite right – Individual investors tend to be bad at choosing when to invest in, and when to pull out of, the market. In fact, they lose seriously playing this game.

Relate research:

• We tend to mistake cost for quality when we get financial advice. If we’ve paid for poor advice, we’re still likely to use it.
• Two heads are less bubbly than one – a second decision-maker reduces likelihood of big mis-pricing mistakes.

I’m reading John Elster’s work on inter-temporal consistency and rationality, and was struck by the fact that there is a parallel in how we use the word “siren”.

In Elster’s work, and greek mythology, a “siren” is a woman who calls (attracts) sailors specifically for the purpose of harming them.

In modern parlance, a “siren” is something which yells at (repulses) you specifically for the purpose of helping you avoid harm.

Thus the word has come to mean exactly the opposite of what it once did. I wonder if there is any research on this, or specifically what allows it to happen.

With banker compensation limitations all the rage, I was struck by the ex-post nature of most of the claims. The statement “they got greedy” is as much a moral hindsight explanation as anything else – but is there any actual lesson we can learn from it. Can we diagnose ourselves being “greedy”, and avoid it?

Most investors I’ve spoken with who cite “greed” regret not cashing out of an investment before it declined. The usual situation is that someone saw an investment make a good return, but waited too long to cash out, resulting in a lower return. The desire for a great return caused someone to miss a good return.

So if the definition of “greed” is putting to much value on a marginally better outcome, when the possibility of far less is prevalent, how does that help us?

My problem is figuring out a rule to avoid being greedy, but not to endorse a strategy which results in the disposition effect – when you’ve made a good paper return, that isn’t a reason to sell (just to crystallize the gain).

As always the key is to be future-minded. Both exit strategies formed at the time of purchase are  critical, as are as strategies based on fundamentals. They give you a reason to keep yourself anchored, and sell unless the views which have driven you to buy in the first place change. The only reason to sell is because you think the investment will go down in absolute terms, or you have a better alternative investment. If you think an investment will continue to go up, stay with it.

I will say that concurrent (non-hindsight) assessments of greed is often when someone’s self-interest (bankers) is blatantly harmful to someone else (the economy), but not themselves. I can handle this definition, but it doesn’t really work in personal investing.

I was recently struck by the similarity between two economic phenomena – health care insurance and credit default swaps. Or more accurately, the similarity in how they don’t work how they’re designed to.

From the New York Mag on Goldman Sachs and CDS’s:

As it happened, Goldman Sachs was AIG’s biggest banking client, having bought $20 billion in credit-default swaps from the insurer back in 2005. The swaps were meant to offset some real-estate investments Goldman had made, specifically a bunch of mortgage bonds it had on its books. The idea was simple: If the value of the mortgage bonds went down, the value of Goldman’s AIG swaps went up, assuring Goldman was safe from all-out losses on what it feared was an upcoming collapse in real estate. In reality, this was nothing like insurance and much more like an old-fashioned hedge.

By that weekend in September, Goldman Sachs had collected $7.5 billion from its AIG credit-default swaps but had an additional $13 billion at risk—money AIG could no longer pay. In an age in which we’ve become numb to such astronomical figures, it’s easy to forget that $13 billion was a loss that could have destroyed Goldman at that moment.

From the LA Times:

Blue Cross of California encouraged employees through performance evaluations to cancel the health insurance policies of individuals with expensive illnesses, Rep. Bart Stupak (D-Mich.) charged at the start of a congressional hearing today on the controversial practice known as rescission.

MR already made key points, but I think one thing is worth pointing out.

In financial engineering – because of agency, liquidity, contractual, or statistical self-deception, a device which is supposed to protect you from disaster ceases to operate correctly in disastrous environments.

In comparison, imagine if seat-belts worked fine at low speeds, but ceased to work at high speeds. What point is there then?

Tyler Cowen linked to Gravity and Levity, who have an interesting post on Gompertz Law of mortality.

Fortunately, I was also exploring Understanding Uncertainty today as well, and came across this wonderful interactive illustration of exactly these laws. Great for seeing mortality rates in action (not often one says that…).

This ties in with my earlier post on how graphics, especially interactive ones, will help make statistical and probablistic analysis more mainstream and prevalent as more people are exposed to it. However, it does still require a sceptical and trained mind to understand the implications of sampling procedures, tests against the null etc. I do wonder if more easily available statistical methods (Google Analytics, SurveyMonkey’s graphing tools) will create a low hurdle for those with no statistical training to present “analysis” as if they do.

Historically, the mere fact you were able to put together a chart would identify you as a nerd. Now that statistics is the next sexy profession, everyone will try to jump on it. But just because you can make a chart doesn’t mean your analysis is meaningful or correct. I’ve seen more and more presentations by individuals who had zero training in statistics, but still presented “statistical evidence” by bar charts or pie charts (the hallmark of the statistically naive). My favorite was a presentation where a comparison of responses between two groups was done on count data – the fact that 200 (out of 2,000) respondents in Group A responded “yes”, while 40 (out of 200) in Group B responded “yes” was taken as evidence that Group A were more amenable to the change. Yikes.

Varian is correct in that:

The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades…  Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.

But don’t underestimate the importance of that last sentence. In fact, that might be the most important sentence. The ability of a decision-maker to discern good, trustworthy statistical analysis from amateur hour will both increase the quality of decisions, and dissuade amateurs from attempting to use simplistic analysis to make their point. If the weakest link runs the show, the fact you’ve used propensity scores doesn’t matter.

Full disclosure: I work for a firm that provides financial advice. I wouldn’t do it if I didn’t think we were helping our clients, but I still cannot pretend to be an impartial party. But, I hope this also makes my opinion a little more informed.

---

A recent paper on how we use expert financial advice has been grabbing a bit of attention lately – Dan Ariely called the results “troublesome, perhaps even frightening” . In a Wired article title “Given ‘Expert’ Advice, Brains Shut Down” the paper’s author states:

When the expert’s advice made the least sense, that’s where we could see the behavioral effect…

In this world, you take advice, integrate it with your own information, and come to a decision. If that were true, we’d have seen activity in regions that track decisions. But what we found is that when someone receives advice, those relationships went away.

Yikes! We stop thinking when someone gives us advice! I need to read the article.

So I did, and you can too (bless PLoS). Reaction? Don’t believe the hype. What follows is about the choices people made. Criticism about neuroimaging analysis are beyond my skillset.

1. The “expert advice” was a single word – “Accept” or “Reject”, detailing what “the expert would do”. The expert was an economist who explicitly made conservative recommendations¹.
2. Given that you tell me someone is an expert and their opinion I’d believe you too – and that would affect my behaviour. Questioning the expertise of someone is almost secondary to how you integrate definitely expert advice into your decisions.
3. The graph below depicts the two estimated probability weighting functions with/without expert advice. There is a statistically significant difference between them – we can reliably tell one from the other. But the change appears pretty small the maximum difference in the function (at an objective probability of 0.8) appears to be 0.05, or a maximum effect of 5%. Quoting the paper –“the expert’s advice led to a significant change … in the direction of the expert’s advice.” So the respondents listened to the expert a bit, but didn’t do anything really stupid. Ok, is that supposed to be surprising or interesting?
4. The effect on choices was as follows – when not told what the expert would do, 64% did what he would have recommended. When told his recommendation, 72% did what he recommended. That’s right – a difference of 8%, when the baseline agreement was 64%.

Thoughts…

These aren’t as impressive results as thought they’d be. The comparison of the “harm” the advice did was benchmarking responses to expected utility theory, so it’s questionable if subjects were “harmed” by it. And the “expert” explicitly states he’s giving conservative advice. This is interesting because in actual financial advisory settings, you are never sued for advising taking on too little risk. To my knowledge every article you will read is about financial advisors advising too much risk. This article actually defines harm by not taking on enough risk, in fact!

I do think there is a great study in this ala Stanford prison experiment - how much will we follows experts advice, including when it’s obviously harmful or wrong? But this experiment doesn’t really ask those questions.

Experts provide advice about things we supposedly know less than them about, so that we don’t have to know everything they know to come to as-informed a conclusion. While I do think everyone should assess expertise critically², I think this paper should have been titled “Expert advice influences choices and decreases cognitive load” – which is pretty much what we go to experts for.

---

1. The quote I found was:

Though the recommendations were delivered under his imprimatur, Noussair himself wouldn’t necessarily follow it. The advice was extremely conservative, often urging students to accept tiny guaranteed payouts rather than playing a lottery with great odds and a high payout.

2. And is exactly why I read the articles myself.

Engelmann JB, Capra CM, Noussair C, Berns GS. Expert Financial Advice Neurobiologically “Offloads” Financial Decision-Making under Risk. PLoS ONE. 2009 ;4(3):e4957.

Dan Goldstein notes the intuitiveness of using graphical representations of probability, especially in Bayesian settings.

High-quality , sometimes even interactive, graphics and charts, have increased greatly in recent years as news and other information have migrated to the web. The New York Times has a dedicated staff of graphical artists well versed in information design, and many digital graphical artists are becoming better versed in statistics and displaying ideas and results. Even the NHS in in on the act, reporting my surgery’s performance with graphs and relevant comparison groups. (Kudos!)

Enabling factors:

• Digital graphics are inherently tinkerable for the designer, reducing turn-around times on trying new displays and ensuring the message is correct.
• Probabilistic reasoning has become more important with the internet, more developed financial sectors, and more information. We thus have the need, and the circumstance, to use them more than our predecessors.
• Our eyes and brain are much quicker at judging sizes and distances than translating numbers to an equivalent internal representation, so we can read (good) graphs quicker. With more exposure, we read them even quicker.
• Many individuals unceasing and constructive criticism and leadership in creating better graphical standards.
• More people trained in statistics and related fields are moving into the public and private sector from academia, and trying to figure out how to communicate their ideas or conclusions to those without training.

Questions:

• Will this affect existing foundations of behavioural economics? Much of the extant literature is derived from numerical representations of probability. Both the papers in Dan’s link are from within the past 10 years, whereas core papers in the decision science field (Kahneman and Tversky etc) are from the 1970′s. Gerd Gigerenzer’s work actively works at finding when such biases are solely based on presentation effects, in effect eliminating them with graphical representations.
• If  graphical representations are consistenly found to enable superior probablistic reasoning, why aren’t they more widespread? I see far more cases where the producer (private business) loses from clear intuitive representation of charges, competition, and risk than benefits. A clear graph showing a specific business is the best only benefits one business.
• The example used it clear for bayesian categorical reasoning – but what about probability distributions and raw numbers? We ran an experiment a while ago with discrete probability distributions which did pretty well, but I’m sure there are futher applications.
• What’s next? Interactive versions such as Dan’s distribution builder is just one possibility.

For some truly fantastic graphics, which aren’t necessarily probablistic but do increase readability, see http://www.style.org/

This is the first (brief) official post of this blog. And perhaps a good intro to talk about learning curves.

In March, I went snowboarding for the first time (I was 27 at the time). It was painful and humbling. Nothing prepares you for having to balance and manoeuvre on a completely new surface. By the end of the third day, I was ready to quit – every fall was like feeling all previous falls again at once. On the fourth day there was heavy snowfall – bad news for my skier friends, but at least the falls would hurt less. So I said (literally) “I’ll give it one last try”.

Low and behold, the heavy snow made all the difference. I could now turn and balance. I went down the mountain faster than my skier friend. At one point I simply was tired and so went off a jump into a snowbank. HEY! THIS IS FUN!

Revelation: I often think of myself as “learning” new things, mostly because I read them or think about them. However, this isn’t learning, at least not in terms of motor skills or concepts – it’s just being informed. Actual learning, let’s call it creating a new pattern of ability in your brain, be it snowboarding, surfing, mathematical or statistical concepts is often uncomfortable and frustrating. It involves doing something poorly (at least at first), and knowing you aren’t doing it well. But you have to remember that learning in any domain is accumulatory  – the first achievements are always the hardest, but subsequent achievements leverage previous ones.

I see three basic types of learning curves– increasing marginal gains, decreasing marginal gains, and mixed. Figuring out what drives each of these allows you to understand which one you are on, and what you can expect in the future. This makes sticking out the curve easier or harder, because you know if you just hold out a little longer, you can reach so much further.

1.	Anders K. Attaining excellence through deliberate practice: Insights from the study of expert performance. The pursuit of excellence through education. 2002 ;21.