This week I had the privilege to attend the Sante Fe Institute’s conference in conjunction with Morgan Stanley entitled Risk: The Human Factor. There was quite the lineup of speakers, on topics ranging from Federal Reserve policy to prospect theory to fMRI’s of the brain’s mechanics behind prediction. The topics flowed together nicely and I believe helped cohesively construct an important lesson—rules-based systems are an outstanding, albeit imperfect way for people and institutions alike to increase the capacity for successful prediction and controlling risk. In the past on this blog, I have spoken about the essence of financial markets as a means through which to raise capital. However, in many key respects, financial markets have become a living being in their own right, and as presently orchestrated are vehicles where humans engage in continuous prediction and risk management, thus making the lessons learned from the SFI speakers amazingly important ones.
This notion of financial markets as living beings in SFI’s parlance can be described as a “complex adaptive system” and is precisely what SFI is geared towards learning about. While financial markets (and human beings) are complex adaptive systems, SFI is a multi-disciplinary organization that seeks to understand such systems in many contexts, including financial markets, but also in biology, anthropology, social structures, genetics, chemistry, drug discovery and all else where the concepts can be applied.
To highlight the multi-disciplinary nature of the event, John Rundle, one of the co-organizers of the event and a physics professor at the University of California Davis, with a special background in earthquake simulation and prediction introduced the theme for the day. Dr. Rundle presented results for his trading strategy founded upon his theories for earthquake prediction. The strategy was built upon asking the following question: can models for market risk be constructed that implicitly or explicitly account for human risk? Seems like things are off to a great start.
Some of the coolest, most interesting moments came during the Q&A sessions, where this year’s presenters, some past presenters, and many brilliant minds from finance including Michael Mauboussin, Bill Miller and Marty Whitman had the opportunity to engage each other on their theses, refining and expounding upon each other’s ideas. Sitting in the room and absorbing conversations like John Rundle speaking with Ed Thorp during an intermission about their own risk management perspectives and how to maximize the Kelly Criterion in investments was a surreal experience that I sadly cannot impart in this blog post, but I hope to channel the spirit in sharing some of the important ideas I learned. Further, I'd like to invite any of you readers out there to add your own thoughts in the comments below.
Let’s start with the first presentation and walk through the day together. In each subsection, I will give the presenter and their lecture title, followed by some notes from the lecture that I felt were relevant to my practical needs (this is not meant to be a thorough overview of each and all presentations). I will type up my notes from Ed Thorp’s presentation in its own blog post, for there seemed to be considerable interest from fellow Twitterers on that one lecture in particular.
David Laibson, Harvard University
Can We Control Ourselves?
Does society have the capacity to prepare for demographic change? Experiments consistently show that people want the right thing, particularly when the question is presented as one of future choice. However, when faced with the very same choice in the present, we fail to make the right decision; the very same decision we would make for longer-term planning purposes. There is a behavioral reason for this: we want the right thing, but right now gets the full brunt of the emotional psychological weight, while planning is not nearly as influenced by the emotional element. As a result, humans have a knack for making terrific plans, with no follow-through.
There is a neural foundation for this, as we have 2 systems (this is derivative of the idea presented in Daniel Kahneman’s Thinking Fast, and Slow).
How can we help people follow-through on their goals in planning as it pertains to saving for retirement?
Which is most effective:
To that end, we were presented with information that showed people recognize self-control problems and opt for less liquid savings options if given the choice, EVEN IF the returns are exactly the same. That is, people acknowledge their inability to control the itch to break their well-made plans.
Vincent Reinhart, Managing Director and Chief U.S. Economist at Morgan Stanley
FED Behavior and Its Implications
I had the opportunity to ask a question, so I asked whether NGDP targeting would be such an optimal rules-based system, and if QE3 was something akin to NGDP. Reinhart answered that while QE3 does get us closer to a rules-based system it is not like NGDP. He further asserted that he wouldn’t necessarily be in favor of NGDP targeting, and that a system of NGDP targeting would be an implicit, under-the-radar way for the Fed to let the market know it will slacken on the inflation coefficient of its dual mandate.
Philip Tetlock, University of Pennsylvania
The IARPA Forecasting Tournament: How Good (Bad) Can Expert Political Judgment Become Under Favorable (Unfavorable) Conditions?
In the 1980s, the government funded a study looking into how well experts predict global events, called the IARPA Forecasting Tournament. Today, this experiment is being recreated, with a focus on forecasting global events of interest to the US government. The experiment uses the Brier Score, first developed for weather forecasters, in order to gauge accuracy. The best Brier score is 0, a dart-throwing chimp registers a 0.5 and the worst possible score is 2.
Types of prediction ceilings:
In the first year of the tournament, the average score in the baseline was 0.37, better than the chimp, but not quite perfect. The best algorithms score 0.17 and sit 0.29 units away from the truth.
In the top performing groups of participants had the following traits in common (note: collaboration was welcomed and fostered by the moderators). I’m injecting my opinion here, but I find these to be very important goals for any organization in attempting to participate in an arena where prediction is important (in this case, for investors the lessons can be particularly apt).
Two lessons/observations:
Elke Weber, Columbia University
Individual and Cultural Differences in Perceptions of Risk
In finance we think of risk as volatility. Culturally however, risk is a parameter, not a model. Risk is therefore subjective and intuitive on an individual level. Further, when faced with extreme outcomes, emotion becomes an increasingly more powerful force on perceptions of risk. It is the perceptions of risk that drive behavior, and these perceptions exist on a relative, not absolute scale. Humans are biologically wired to that end.
Weber’s Law (not Elke Weber, an earlier Weber): the differences in the magnitude required to perceive two stimuli is proportional to the starting point. i.e. all differences are measured by a relating the new position to the original.
Familiarity actually works to reduce perceptions of risk, but not risk itself. Experts in a certain field tend to underestimate risks due to familiarity. Return expectations and perceived riskiness predict choice, NOT the expectation of volatility (i.e. risk is perceived on a relative scale, not through the formulaic calculation of volatility).
Cultural differences—Shanghai vs. US MBA students:
In the Animal Kingdom, the most base way to perceive risk is through experience. Small probability events tend to be underweighted by experience, but overweighted by perception.
I had the opportunity to ask Dr. Weber a question. I asked her about the point that familiarity tends to lead people to overlook risk, and how that can be reconciled with the value investing concept of sticking to a core competency? If through focusing on a core competency, rather than mitigating risk, investors were increasing it. Dr. Weber rightly observed that focusing on a core competency does have some distinctions with familiarity in that the idea is to work in areas where one has the most skill, but that there could very well be such a connection. In fact, she thought my question to be “very interesting” and worth further observation.
Nicholas Barberis, Yale University
Prospect Theory Applications in Finance
Can we do better in financial markets replacing expected utility with prospect theory?
Some core elements of prospect theory in finance:
There is little support for beta as a predictor of returns. Prospect theory instead focuses on the idea that a security’s (or indices) own skewness will be priced based on the scale of the left or right tail.
The Disposition Effect – people sell stocks that have gone up far quicker than stocks that have gone down.
Gregory Berns, Emory University
When Brains are Better than People: Using fMRI to Predict Markets
Dr. Berns started with a history of using blood pressure in order to ascertain where/how/why certain stimuli impact the brain. Today we can use fMRI in order to clearly see ventricular activity and this provides a nice window into how the brain works. Blood flow to regions of the brain change based on which part of the brain is active/engaged at any given point in time. Animals in the wild that are most adept at prediction can survive far better in changing environments than those who cannot.
Contrary to conventional wisdom, dopamine is not directly correlated to pleasure. Dopamine in fact is correlated to the anticipation (i.e. the delta) of pleasure. It is the changes in dopamine levels which lead to decisions. Dr. Berns showed a fascinating slide using the corking and drinking of a fine wine to illustrate this point. It is in the moment of opening the bottle of wine that people experience the dopamine release, rather than during the pouring of the glass or taking the first sip.
Dr. Barberis had mentioned fMRI and its application to measuring the disposition effect and here Dr. Berns confirmed and illustrated. There are three explanations for why the disposition effect happens:
Using fMRI, we can see that there are different approaches to the disposition effect depending on how and where the brain reacts (note: boy do I wish I had these slides, because the images are amazing in highlighting the effects). People tend to fall into 2 camps—those who are influenced by the disposition effect, and those who are not. fMRI shows that in those who ARE influenced by the effect, the blood flow is most active in the stem of the brain, the area where dopamine is released. In those who are NOT impacted by the disposition effect, there is brain activity in a much broader portion of the cerebrum (the bigger part of the brain).
This effect was studied using fMRI in 2 contexts involved in understanding prediction.
Please note: I apologize for any formatting errors. This post was drafted in Word and did not transfer very cleanly at all into the Squarespace format. In the interest of sharing the ideas in a timely mannger, I will go ahead and publish before I have the chance to clean up all the spacing, tabbing, etc. Please enjoy the content and try to look past the messy spacing.