A set of rules to develop a behavioral edge – Part 6

Investment psychology

The dogs in the neighborhood don’t bite

We love to interpret the world. We love patterns. We love to find causes. The world of investing is rife with humans drawing intuitive conclusions and over-generalizing based on minimal evidence. Also widespread are fallacious opinions ostensibly reached from statistics but using inadequate samples. Clustered with this family are completely erroneous intuitions of cause based on correlation or patterns.

Putting two and two together

The mistakes I’m referring to are thought of by behavioral psychologists as cognitive errors. They are mistakes where we seem to have a problem putting two and two together. They are mistakes of ordinary reasoning, of faulty inductive reasoning.

Faulty inductive reasoning

A child grows up in a quiet neighborhood. The dogs in the neighborhood are friendly. They don’t bite. The people in the neighborhood are friendly and look out for the kids. The drivers slow down when kids are around. The child might learn from this experience that dogs never bite children, adults are all to be trusted and that cars are not dangerous. This would be faulty inductive reasoning.

The search for the investment guru

Here’s a skill testing question for readers. I have set out below the investment performance of three investors over a six-year period. ‘B’ is bad performance in the stock market – underperforming the average investor. ‘G’ is good performance – outperforming the average investor performance.

Alice                     BBBGGG

Baker                   GGGGGG

Charlie                BGBBGB

Alice’s track record over six years is BBBGGG, that is, she had three bad years followed by three good years. Since our benchmark is the average performance of all investors, in any given year, half will underperform, and half will outperform. Baker had six good years. Charlie was mixed.

Here’s the question. Are all the sequences equally likely? Instinctively we want to say Baker is the guru.

But, on the question of whether the sequences are equally likely Kahneman writes: “The intuitive answer – “of course not!” – is false. Because the events are independent and because the outcomes B and G are (approximately) equally likely, then any possible sequence of six [investment performance results] is a likely as any other. Even now that you know this conclusion is true, it remains counterintuitive, because only the third sequence appears random.” (Kahneman, Thinking, Fast and Slow. 2011) p.115. In his example Kahneman uses the births of boys and girls (B and G) in a hospital.

Smart wags would even try to read something into the different pattern of performance between Alice and Charlie.

To assess whether our GGGGGG investor is a guru we need the help of statistics. We need to ignore our love of patterns. To use statistics, we need enough annual data so that we can say the outperformance is statistically significant. Statistics talks in terms of being 95% sure the results are not due to pure luck, i.e., are truly due to skill. There are millions of investors. If we asked each one to flip a coin twenty times, we can suppose that some would get 20 heads in a row. Undoubtedly amongst the millions of investors there are a large number who have outperformed the market for twenty years purely by luck. The question is how many years of cumulative outperformance is necessary to be satisfied the performance is the result of skill. Someone has apparently figured out that a fund manager would need to beat the stock market for 36 years before we would know that their performance was based on skill rather than luck! I can’t vouch for the conclusion, but I could believe it’s true.

In our hunt for the investment guru, we got hit with a double whammy of cognitive errors. We saw patterns in random data because we, as humans, love to find causes and patterns suggest causes. And we jump to conclusions on the basis of statistically insignificant data, again, because we love to identify causes. So, can we learn much from a few observations? Not really. But there is a high risk that we will try. Forewarned is forearmed. We need to keep our wits about us.

We can make the same error when we are assessing the performance of companies and the performance of management!

Next, let’s turn to the problem of phony statistics.

Phony statistics

Investors are bombarded daily with the opinions of analysts, advisors, pundits, journalists and bloggers purporting to be based on conclusions drawn from statistical analysis. Typical would be an opinion that the stock market is likely to advance next year. The reason given might be the assertion that when the S&P 500 has declined more than 20% in a year, it is likely to advance the following year. The proffered evidence might be that, historically, the stock market has declined 20% or more 17 times and that 14 of those occasions were followed by an advance the following year. (I’m making these numbers up). If we were to read this prognostication, what credence should we give to it? The answer is none. For our S&P 500 decline problem there is no statistical basis to reach any conclusion. Nor is there any valid basis in inductive reasoning. This is phony statistics.

Phony statistics exemplifies a problem in our ordinary reasoning. This is the problem of drawing conclusions from minimal evidence. We have a tendency to overgeneralize.

Investors are not trained statisticians. How can we sort the wheat from the chaff? We get all sorts of comments like this from pundits. The answer is to be alert to the problem and to constantly be on our toes.

Survivorship bias and correlation

A sister problem with inductive reasoning and statistics is survivorship bias. We have ten years of performance stats of ten ETFs sponsored by an investment firm. They don’t include the stats on the two ETFs that performed miserably and were shut down three and five years ago. The performance stats we have suffer from the error of survivorship bias.

A further sister problem with inductive reasoning stems from our tendency to use correlation as a stand in for cause. Even though we all know that correlation does not establish cause, this cognitive error creeps up on us all the time. Overlaying two plots on a single chart which seem to track each other is a classic example of this error.

We exaggerate consistency and coherence

Behind all this is the fact that we are human. As humans we tend to draw conclusions from minimal evidence. We overgeneralize. We also seem to be hard wired to see patterns and trends which are a very frail basis for reaching conclusions. The problem revolves around faulty inductive reasoning and our proclivity to find causes.

As Kahneman puts it: “The strong bias toward believing that small samples closely resemble the population from which they are drawn is also part of a larger story: We are prone to exaggerate the consistency and coherence of what we see.

The exaggerated faith of researchers in what can be learned from a few observations is closely related to the halo effect, the sense we often get that we know and understand a person about whom we actually know very little. System 1 runs ahead of the facts in constructing a rich image on the basis of scraps of evidence.” (Kahneman, 2011) p.114.

Kahneman points out there is an overlay of behavioral biases at play with these inductive reasoning problems.

Gap-to-edge rules

Let me give you my gap-to-edge rules to counter faulty inductive reasoning. They are my version of what Daniel Kahneman (Nobel Prize in economics) calls risk policies. Kahneman describes risk policies as decision rules that are always applied in similar situations. Avoid your behavioral gap and gain an edge over Mr. Market’s foibles.

This post is part of a multi part series titled ‘A set of rules to develop a behavioral edge’.

So far there have been five posts and five sets of gap-to-edge rules:

Part 1 The problem of short-term thinking.

Part 2 The attractive trap of extrapolating the most recent past into the future

Part 3 Control our animal spirits when faced with risky situations.

Part 4 The herd mostly gets it wrong

Part 5 Our minds search for confirming evidence

Gap-to-edge rules for this post

Gap-to-edge rule: Beware of any conclusion based on inductive reasoning

The first step is to develop a clear idea what inductive reasoning is. It’s any time a conclusion is drawn from observations. It’s a generalization.

Any conclusion drawn from an analysis of data is inductive reasoning. It should cause investors to immediately raise their guard. 

Gap-to-edge rule: Beware of any conclusion based on a pattern

This includes all patterns of how the stock market has acted in the past. It includes January effects, sell in May and go away, and many others.

Gap-to-edge rule: Beware of any conclusion based on phony statistics

This includes all those beguiling suggestions of how the stock market has gone up 14 out of 17 years after a decline of 20% or other comparable silliness.

Gap-to-edge rule: Beware of all statistical conclusions, especially from alleged experts

Forewarned is forearmed. Learn to recognize when limited data is being used to infer some general rule. Learn to be questioning of the math and statistics qualifications of the expert.

It’s easy to lie with statistics, even where there is some mathematical grounding for the conclusion. Statistics is a very difficult subject. Even the experts get it wrong. Be alert to sample size, sample period and survivorship bias.

Gap-to-edge rule: Pay no attention to forecasts of the economic outlook or stock market outlook or trends in either of these

As John Templeton says in his Maxim 14: “Too many investors focus on ‘outlook’ and ‘trend’. Therefore, more profit is made by focusing on value.”

Gap-to-edge rule: Keep it simple.

We are constantly exposed to themes presented by analysts and commentators: bull market vs bear market; value stocks vs growth stocks; big stocks vs small companies; cyclicals vs consumer staples; U.S. market vs emerging markets; and so on. These are all generalization based on patterns, trends, limited data and even statistics. Investors can essentially ignore pontifications on these subjects. Worse would be to make important investment decisions based on them. They encourage market timing which is a losers’ game.

So, what does one do to keep it simple?

I keep coming back to the following quote from Warren Buffett: “Should you choose, however, to construct your own portfolio, there are a few thoughts worth remembering. Intelligent investing is not complex, though that is far from saying that it is easy. What an investor needs is the ability to correctly evaluate selected businesses. Note that word ‘selected’: You don’t have to be an expert on every company, or even many. You only have to be able to evaluate companies within your circle of competence. The size of that circle is not very important; knowing its boundaries, however, is vital. To invest successfully, you do not need to understand beta, efficient markets, modern portfolio theory, option pricing, or emerging markets. You may, in fact, be better off knowing nothing of these.” (Buffett W. E., 1998) p.93.


Readers wishing to read deeper into this subject can check out the Motherlode Chapter 16. We Overgeneralize and Find Causes

And particularly Sections:

16.01 A complex example makes the point

16.02 The investment guru

16.03 Even stock market analysts and market strategists can get it wrong

16.04 Phony statistics

16.05 Cause and correlation

16.06 Causal connections based on pure narrative and supposed correlation


Other posts on investment psychology

This post is part of a series. Readers are invited to read Investment psychology explainer for Mr. Market – introduction this will give you a better understanding of some of the terms and ideas and give you links to other posts in the series.


You can reach me by email at rodney@investingmotherlode.com

I’m also on Twitter @rodneylksmith


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