Mind Over Money

Welcome back to the Mind Over Money podcast. I’m Kevin Cook, your field guide and storyteller for the fascinating arena of behavioral economics.

I’m excited about today’s topics because we are going to talk about how our brains use stories to make decisions. In fact, after I tell you a few stories about brains and stories, you’ll start to wonder which came first – brains or stories!

Please pick a time stamped topic below:

(0:30) - How Storytelling helps us make decisions: Peter Guber Article

(4:15) - How do our perceptions effect our decision making

(7:15) - Insights from neuroscientist, Michael Gazzaniga

(15:00) - Experiments done by neuroeconomist, Paul Zak

(21:50) - Molly Crockett: "Beware neruo-bunk"

The Evolution of Risk-Taking

Trading is one of the hardest day jobs in the world – what drives those who succeed at it?

Welcome back to Mind Over Money, I’m Kevin Cook, your field guide and story teller for the fascinating arena of behavioral economics.

Today my guest is a PhD candidate from the University of Greenwich in London who is studying the risk-taking behavior of traders from the perspective of evolutionary psychology.

Belinda Vigors wants to know if successful traders are doing anything different when making decisions under uncertainty and stress than us mere mortals who wrestle with our cognitive biases and unknown depths of neurochemistry and neuro-circuitry that drive our emotional habits.

Before we meet Belinda, I want to give you some background for our discussion that is actually causing a rift in the field of behavioral economics, at least for those of us who apply it to markets and trading.

I have been a long-time fan of Daniel Kahneman -- who is the only psychologist ever to win a Nobel Prize -- for his work on decision making under uncertainty and risk. He practically invented the discipline of behavioral finance.

And the cognitive biases that he has discovered and named over 4 decades have given us a rich vocabulary with which to talk about people (and traders) acting stupid and irrational with money and risk.

Since I am also an eager student of neuroscience, I simply look at the two fields as different but complementary ways of describing and understanding decision making. As I like to say, the behavioral gang studies us from the OUTSIDE-IN, while the brain gang studies us from the INSIDE-OUT.

An Affinity for Randomness and Ambiguity

But Kahneman’s 2011 book Thinking Fast and Slow has caused some extra controversy in the twilight of his long career because he may be not only oversimplifying how our decision making works, but also limiting any believer’s choice of solutions or therapies.

In other words, his System #1 and System #2 for thinking -- the fast and emotional vs. the slow and rational, respectively -- don't appear to integrate with the findings of brain science very well, which would argue for much more complexity especially as emotion and neurochemistry are concerned.

Kahneman also no longer likes describing humans as “irrational.” But then how do we explain all sorts of self-sabotage regarding money, risk and long-term goals? Or, how about truly self-destructive behavior like crime and violence?

And Kahneman’s theories are not based on any clinical work in helping people or traders change their thinking and decision-making. Since I did not read Thinking Fast and Slow, in today’s podcast I share a quick review by a thoughtful investor who explains quite well why the theories may be very limiting.

Ravee Mehta, a portfolio manager and author of The Emotionally Intelligent Investor: How Self-Awareness, Empathy and Intuition Drive Performance, says that Kahneman’s systems miss much of the significant skill, discipline, and what I call an “affinity for randomness” that traders can develop when they become more aware of their emotions and intuition.

Here’s an excerpt from Mehta’s 2012 blog, “Daniel Kahneman is Wrong (at least when it comes to investing)”...

I humbly disagree with Kahneman, a Nobel laureate, and some other prominent psychologists when it comes to “fast-thinking” and investing. They argue that the stock market is too complex and random for intuition to be developed. They believe that the randomness causes people to build incorrect association biases. This argument is flawed. While the overall market may seem complex and random, there are many patterns within it that recur frequently. The best money managers recognize patterns developing ahead of most. They also can develop useful intuition because they are honest with themselves about the role luck had in their success. The intuitive decision-making that is involved with investing is much more complicated than other types of decision-making. However, that does not mean that we should abandon trying to better develop and use gut instincts when we invest.

The Evolutionary Path of Trader Brains

Cognitive biases, emotional habits, and intuition are definitely interesting. But what really gets me excited is talking about how our brains work. And that invariably leads to discussions about our evolution.

So I was really excited to bring in a young student of this complex collision between behavioral science and of one of the hardest jobs on the planet, trading.

Belinda Vigors explains how traders may be dealing with risk in terms of “need thresholds” and survival goals. She bases part of her theories about emotion on the work of psychologist Jennifer Lerner of Harvard whose research examines the distinct effects of fear, anger, and happiness o...

In today's Mind Over Money podcast, I took a closer look at decision-making. Specifically, I wanted to explore what often gets in the way of good decision-making, especially when the financial landscape is shifting like it is now.

And that means we have to focus on the cognitive biases, those mental short-cuts, filters, and processes that help us make decisions faster.

Because those same short-cuts just as often short-change us from the best outcomes in everything from stock-picking -- and its twin challenges of risk and profit management -- to car shopping and job hunting.

Remember that this podcast wants to come at our “brains on risk” from 3 distinct angles:

Angle #1 is behavioral finance. This is the field of cognitive biases and heuristics that Daniel Kahneman and Amos Tversky broke big ground for in the late 1960s and 1970s. I call this way of knowing about our decision-making the OUTSIDE-IN approach because the behavioral researchers and social scientists are conducting problem-based experiments and questionnaires with thousands of people. They examine behavior in different, repeatable “decision situations” and then draw conclusions from the data patterns about what we humans tend to do and why.

Angle #2 is neuroscience. What science has discovered about our brains through advanced imaging techniques in the past two decades could fill lifetimes of research projects for the next few generations of curiosity seekers. I call this way of knowing about our decision-making the INSIDE-OUT approach because the neuroscience researchers are focused on the brain structures, functions, and biochemistry that cause our behavior.

And this reminds me to remind you about episode #2 of Mind Over Money recorded on Nov 8 where I spoke with Denise Shull of The ReThink Group. Shull earned her Masters in the Neuroscience of Emotion at the University of Chicago and then went on to become a trader and a trader’s coach, working for many top banks and funds. We had a great discussion about understanding how emotion is involved in all our decisions.

Shull’s consulting work really took off in 2003 when she combined her research with that of Antonio Damasio, Professor of Neuroscience at the University of Southern California. Damasio and his colleagues found that if emotional centers of the brain were damaged or in some way disabled, we wouldn’t be able to make decisions at all, or at least not with the ease and effectiveness we do hundreds of times during the day.

So Denise Shull trains her clients to become more aware of their emotions during trading, not to shun them. And she also started doing this in 2016 for the US Olympic Snowboarding Snowcross team. Be sure to catch episode #2 of Mind Over Money with the title “Train Your Brain for Better Trading” to hear all about her work.

How Smart Traders Re-Wire Their Brains

The third angle that I approach the collision of psychology and markets through is the investors and traders themselves, from the so-called Market Wizards, the books by Jack Schwager that interview the highly successful, to the rogues and gamblers who lose it all. This 3rd angle of knowing about our “brains on risk” was actually where I started my intellectual journey here 20-some years ago, observing traders in the commodity and futures pits of Chicago.

That’s because smart traders who’ve learned to survive in the pits or behind the screen share many common traits that the scientists would admire in terms of being aware of their biases and emotions so that they can make better decisions, more consistently, for long-run success. Short-term trading is arguably one of the most mentally-challenging occupations there is because, as I used to argue, our brains are hard-wired to make quick decisions that break the golden rule of trading.

That rule is: cut your losses short, and let your winners run. Most new traders seem to instinctively do the opposite because they are loss averse and eager to capture gains. This is the mathematical recipe for failure in trading because it guarantees that even if you are right 60% of the time, you will lose everything as you let losses pile up bigger than winners.

Ray Dalio on the Big Shift

Speaking of Market Wizards, Ray Dalio, the head of the largest hedge fund in the world, Bridgewater with nearly $200 billion AUM, put out a new investment letter today, November 15, to opine on how the global-macro investing landscape will change over the next few years after last week’s GOP election sweep. Obviously we are seeing many of these new trends already emerging in full force, like the bond bubble popping, and the US dollar rallying as inflation expectations rise, and the flood of money into domestic small-cap companies, especially Financial and Industrial/Manufacturing stocks, at the expense of big-cap Tech

Dalio suggests that these big shifts could rival the ...

While Renaissance Technologies makes most other hedge funds look foolish, even the independent trader can copy their discipline

NFLX, PANW

Long-time followers of mine in this bull market know that every quarter I go over the holdings of hedge fund Renaissance Technologies because they were one of the early quant houses that made algo trading so successful and popular.

The founder, Jim Simons, was a mathematics professor in the 1970s who never thought about the markets much. He sort of stumbled into testing some theories on stocks, and the rest is history as he was pulled headlong into the markets and created a powerhouse with over $50 billion AUM (assets under management).

And Simons made a point of not hiring MBAs, traders, or anyone with a background in finance. He only wanted physicists, engineers and other quantitative problem-solvers to come work for him and mine the data of markets to find unique correlations, patterns and new edges.

What kind of data patterns and correlations are they after? Well, with 90 Ph.Ds. on staff, mostly math, science, and engineering types, we can only guess that they are sifting through mountains of fundamental, price, economic, weather, and consumer patterns, looking for those small anomalies between individual stocks and industries and other asset classes.

Mining Data Others Ignore

If it’s a popular, well-known correlation, they don't want anything to do with it. They hunt in the noise of tons of data for things that others can't see, or are not even looking for.

This week, Matt Levine at Bloomberg View wrote briefly about Renaissance after colleague Katherine Burton published a full story on the company and its funds and practices for the December/January issue of Bloomberg Markets magazine. Here's how Levin opens his piece, quoting data from Burton's story...

The big problem with Renaissance Technologies, the Long Island-based "pinnacle of quant investing" founded by Jim Simons, is that its Medallion fund makes too much money.

Medallion was up 21 percent for the first six months of 2016. It was up 35.6 percent last year, 39.2 percent the year before, 46.9 percent the year before that. This keeps going. The last down year was 1989. The fund had a rough few days in August 2007, but ended the year up 85.9 percent. It has returned about 40 percent per year, on average, net of fees, since it started in 1988.

Of course it can't keep compounding returns that way because of the size factor. What you can do well with a 5 billion dollars you can't necessarily as well, much less better, with 50 billion. And that's why they are forced to simply return profits to investors, who are primarily employees now since the fund was closed to new investors in 2005.

Matt Levine's piece on Bloomberg View can be found here and the full Burton story is linked above.

Can a Human Trader Copy Black Box Success?

While the computer programs that work for Renaissance are still a big mystery -- like we don't know how many strategies just trade intra-day to make money -- it's safe to say that they create a lot of turnover in stocks, exploiting new patterns or "edges" in thousands of stocks.

But doesn't that mean a lot of extra risk?

What most people miss about the success of the algos and black box trading systems is that they run through markets with big size in thousands of stocks and instruments because the risk control is automated too.

It’s not like you or I trading 100 stocks at once and going crazy trying to keep track of the risk and profits. They program the computer models to seek and destroy profit opportunities and to manage the risks in real-time too so that they are never destroyed.

So speed and continuous, instant access make the difference too, especially if the model is wrong about an opportunity. In that way, they take human emotion completely out of the decision-making equation.

With that I want to introduce you to our guest today, who is like David to the Goliath Renaissance. Jeremy Mullin is a colleague of mine at Zacks where he starts with a simple quant model built on earnings momentum – the Zacks Rank -- and then overlays his own suite of technical trading filters and what I will broadly call “behavioral analysis” because he pays attention to extreme moves in stocks that are often driven by algo trading that is exploiting investor fear and greed in the markets, which therefore sets up new opportunities for him.

Jeremy has spent the last 13 years as an equity, futures and options trader. His main focus when trading stocks is high beta equities and earnings moves. He ...

The greatest accidental experiment in social media has a lot to teach us about taming the AI beast

(2:00) - Facebook Flopped and It’s Great!

(5:45) - Max Tegmark: Life 3.0

(10:35) - Prometheus Achieves World Domination

(16:00) - Taming the Beast that AI Could Become

(21:10) - 5 Proofs We Are Naturally Irrational

(24:20) - Driving Money Away, Keeping Bad Habits

(29:25) - Repelling Helpful People, Sabotaging Our Health

(34:15) - An Elephant in Your Brain

(42:30) - Episode Roundup: [email protected]