Setting an Inhuman Baseline

The introduction of studying behaviour in finance started with philosopher John Stuart Mill in his 1830 essay, On the Definition of Political Economy; and on the Method of Investigation Proper To It, but is most popularly attributed to Scottish economist Adam Smith’s creation of the fictional “Rational Economic Man”, expanding on the concept in his book, The Wealth of Nations.

The fictional entity of the “Rational Economic Man” was used to explore theory around how an entity with perfect rational behaviour, and thus no emotions, would make economic decisions.  These were the beginning assumptions built into (eventually) well-known economic theories of interactions between supply-and-demand, and ultimately grew into the use of measuring the “utility” of decision making.

For example:

“In this formulation, an economic man does not have to act morally or responsibly; they don't even need to act rationally from the perspective of an outside observer. They only need to act in a way that allows them to attain pre-determined, narrow goals at the lowest possible cost. For example, if an angler in the Pacific Ocean can catch the same amount of fish with a disposable plastic net that they could with a more expensive hand-woven natural fiber net, they will choose the plastic net–even if that means they will eventually and unintentionally poison the fish that he depends on for their livelihood.”[i]

This study of utility and decision-making became the field of Behavioural Economics.  Throughout the mid-1900s, economists, philosophers, mathematicians, and psychologists continued to explore the field, leading to developments in statistical theory and game theory, such as Nash’s Equilibrium Theory, the well-known Prisoners’ Dilemma (Flood and Drescher), Decision Analysis Framework (Howard), and the Expected Utility Theory (von Neumann and Morgenstern).

Outward progress took a breather there for the better part of 30 years.  All of the statistical analysis and study of decision making mentioned above continued to use the Rational Economic Man as its basis, ignoring the very-obvious fact that humankind rarely, if ever, makes perfectly unemotional decisions.

Von Neumann and Morgenstern’s expected utility theorem is one you’re probably familiar with, because you’ve seen a very simple example in my Risk Questionnaire:

If you had the option between taking $25,000 now, or flipping a coin with the chances to either win $60,000, or receive $0, which option would you take?

The “correct” answer according to the Rational Economic Man and the expected value of the choice is to take the 50/50 coin flip to win $60,000.  Why?  Let’s do some quick probability math.

Expected Value = (50% x $60,000) + (50% x $0)

                                  = $30,000 + $0

                                  = $30,000

$30,000 > $25,000, therefore the “correct” answer is to take the coin flip.

However, and this is clear from the Risk Questionnaire results, lots of people don’t want to take the coin flip!  I won’t get into a lot more math here, but Von Neumann and Morgenstern’s Expected Utility Theory helps to explain why some people don’t want to take the choice by adding their “utility function” which takes into consideration factors outside of the equation, in particular, the bias of risk aversion.  Consider this simplified situation: for someone with only $5,000 in the bank, receiving $25,000 holds far too much utility to risk it all on a coin flip!  The guaranteed utility of $25,000 is far more attractive than the 50% possibility of $60,000.

Did you know you were doing some very complex statistical analysis during that one question?

Adding Emotion to Economics

The study of decision making and expected utility continued throughout the 1950s, 60s, and 70s, but results kept coming back skewed from what was expected in either the classical Decision Making structures, or the new Expected Utility Theory.  Results leaned in the anticipated direction, supporting the research, but was often far from the mark.  What was going on?

Enter the godfathers of modern Behavioural Economics: Daniel Kahneman and Amos Tversky.

In 1969, Kahneman proposed to Tversky that the skewed results being achieved in these studies were relying on two things that were not entirely correct:

1)         People would be doing quick statistical math every time they were making a utility decision

2)         People came to the table with no “baggage” that could influence their decisions

Running many experiments over the following years, they presented their conclusions in the 1973 paper, On the Psychology of Prediction:

“In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction.  They rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and systematic errors. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction.”[ii]

Heuristics comes from the Ancient Greek εὑρίσκω (heurískō,) and is related to “finding” and “discovering”.  Interestingly, the same root word forms the modern day “Eureka!

In simplified terms, Kahneman and Tversky labelled heuristics as “rules of thumb”; common decision-making shortcuts that most people use in day-to-day life, because having to analyze every decision, every time, would be a horrible misallocation of resources.  Of course, sometimes the rules of thumb work, and sometimes they don’t.  Kahneman and Tversky’s seminal book, Thinking, Fast and Slow, separates our thought processes into two systems: System 1 uses heuristics and biases to form fast opinions.  System 2 uses deliberation and testing to arrive at slower conclusions.

System 1 – operates quickly and intuitively; your gut-reactions and snap judgments.  These include judging distances, providing direction when you hear a sudden noise, detecting anger in someone’s face or voice, answering 2 x 2 = ?, and (concerningly) driving your car down an empty highway.  These are “instinctive” processes, where learned heuristics and biases can give you a quick solution; your brain “saves” you from expending the energy.

System 2 – labelled the “lazy system”, these are your slow thought processes, where intentional effort is being made.  Counter-intuitively, any time you’re focusing your attention on something, you’re engaging the lazy system so that you can get an accurate or measured response.

To demonstrate, this puzzle was presented in Thinking: Fast and Slow.

“For an example, here is a simple puzzle.  Do not try to solve it, but listen to your intuition:

A bat and a ball cost $1.10.

The bat costs one dollar more than the ball.

How much does the ball cost

A number came to your mind.  The number, of course, is 10 cents.  The distinctive mark of this easy puzzle is that it evokes an answer that is simple, intuitive, and wrong.”[iii]

The researchers go on to point out that if the ball costs 10 cents, the total cost would be $1.10 + $0.10 = $1.20.  It is only when you engage your System 2 thinking that the correct answer becomes obvious; the ball costs 5 cents ($1.05 + $0.05 = $1.10).

Your System 1 tricked you!  Since your brain will almost always try to take shortcuts to save energy, answering quickly bypassed your System 2, which would have taken a few extra seconds to double-check the math.

Shane Frederick’s Cognitive Reflection Test[iv] provides two more examples of how your brain will process information fast, and then slow.  Give it a try!

If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?

In a lake, there is a patch of lily pads.  Every day, the patch doubles in size.  If it takes 48 days for the lily pads to cover the entire lake, how long would it take for the patch to cover half of the lake?

Identifying Heuristics and Biases

Through the subsequent decades, research on cognitive reflection, heuristics, and biases continued, further refining and analyzing how humans would approach solving problems.  However, issues continued to confound the desire of economists and statisticians to wrap mathematics around inherently socially learned biases.  Most problematic – humans kept acting differently when presented with similar choices!

Morgan Housel’s excellent book Same as Ever contains a discussion of how personal histories can form current biases.  As an abbreviated example, compare two people, both 50 years old, both engineers, both “equally successful” at face value.  However, one engineer came from a struggling immigrant family with no history of higher education, and the second came from a highly educated, wealthy household.  Despite their current equivalence, who do you think feels wealthier?

In practice, identifying these biases that may impact investor behaviours is incredibly important.  In the above example, the first engineer’s lived experience could mean that they are more likely to save or less likely to take risks (risk aversion / loss aversion biases).  Meanwhile, the second engineer, having come from a higher socio-economic background, may see their social circles spending more, influencing them to save less (anchoring / availability bias), or having not ever dealt with money issues could be convinced that stock-picking and portfolio management aren’t that hard (overconfidence bias).

Its clear to see that despite their current equivalent status, these are two very different investors.

Another example is showing itself very clearly these days between generations with different life experiences.  Though this is true for every generation, right now it is most obvious between “Boomers” and “Millennials”.

“Boomers” have experienced most of their career during a time of reasonable “aspirational education”, where getting a Masters or Ph.D would nearly guarantee higher employment and salaries, whereas “Millennials" have lived through a time when Starbucks baristas are being asked for a Bachelors degree, and a Masters is nearly the price of admission for many careers.

“Boomers” came into their adult years during a period of inflation and exceptionally high interest rates.  “Millennials” came into maturity watching the 2000 Tech Bubble burst and the 2008 Great Financial Crisis, leading to a “lost decade” for market returns.

This leads to effects like:

Boomers 

• More likely to consider fixed-rate mortgages, having purchased their first home during sky-high interest rates

• Less likely to want to stray from a “typical” Balanced portfolio, having had their investment careers during a period where reasonable returns came from both stocks and bonds

Millennials 

• More likely to consider variable-rate mortgages, having purchased their first home during rock-bottom interest rates

• More likely to be reluctant to take on an adequate amount of risk, having begun their investment career during a period of flat markets and multiple crashes

Stop for a moment here and consider what beliefs about money and investing you have, where those came from, and how that might influence your own heuristics and biases.  Did your parents pay for your education?  You are much more likely to want to pay for your kids’ educations.  Did you live through a period of severe financial instability?  It is much more likely that you are highly sensitive to the change of values in your investment accounts.

Its crucial that we talk about your “money history”, so that we can understand together the unique view you have on life, and what success means to you.  Armed with this information and understanding, I can better help you achieve your “money future”.

Where Exactly Are The Blind Spots?

To put names to the heuristics and biases that form our “financial emotions” and lead to investor blind spots, this is a list of the top 5 investor biases affecting investment performance: 

Status Quo Bias – the reluctance to change from your current position

Risk Aversion – the unwillingness to take on risks

Loss Aversion – the unwillingness to realize a loss on an investment

Anchoring Effect – the effect where the presented information biases how you process the information that follows

Confirmation Bias – the tendency to look for information that supports your decision

 You can see how problematic these can be to investors!  Take the following scenario:

 An investor has held shares of their company stock for years, having been given annual grants by their employer.  Their exposure to the company stock has been successful, but now forms the bulk of their investment assets.  With retirement on the horizon, their Advisor suggests selling a good portion of their company stock to invest into a diversified portfolio, but the client has not had much other investment experience.  The company stock has just hit a recent high of $90/share, but has now declined to $81/share in the past month.  The broader stock market has just had a very successful year, but the newspapers have expressed worry about a coming recession.

What effects is this investor feeling?

Status Quo Bias / Risk Aversion – the investor is worried about investing in a different way, because they are aware of “risks”, but they don’t know enough about stocks or bonds to know what the risks are.  They are fighting both the status quo bias against the recommended change, and the risk aversion bias because they are familiar with the risks of the company stock, but not familiar with a traditional diversified portfolio.

Anchoring Effect / Loss Aversion – even if the investor was familiar with the markets, they have more battles to face!  The anchoring effect has locked that recent $90/share value in their head, and now loss aversion kicks in because selling at $81/share would mean “losing” those additional $9/share that was recently available.

Confirmation Bias – since the investor is already concerned, those newspaper stories about a coming recession hit home, and the investor points to this concern as a reason not to change investments.

Sound familiar? It should!  If seasoned professionals have difficulty making these decisions with certainty (and we do!), how is it reasonable to believe someone who’s primary occupation lies elsewhere would know what to do?  It would be like asking a teacher to come help finish the pipefitting in the local refinery – there’s no way for someone to be reliably successful under those conditions.

Measuring Bias Effects and Protecting Against Them

So, you’ve made it this far, you’ve taken this crash course in Behavioural Economics, and hopefully you’re thinking about the little bit of yourself reflected in the preceding pages.  Great!  In the classic words of the Greek philosophers, “To know thyself is the beginning of wisdom” 

Recent studies from Inalytics and the CFA Institute have analyzed tens of thousands of trades across thousands of portfolios in order to isolate the effect of bias on portfolios.  Knowing the largest effects, we can start building a strategy to do the best thing any investor can do: avoid mistakes.

#3 - Disposition Effect

Related to loss aversion and regret avoidance, the disposition effect is the bias to sell the “winners” in your portfolio, rather than turf the “losers” and realize a loss.  This was the widest-spread bias, with over 95% of portfolios showing this behaviour, and this practice affected portfolios with an annual average performance loss between -1.5% and -1.75%.

#2 - Anchoring Effect

When you own a large position, or if you have owned a certain thing for a very long time, its likely that your opinion of the investment remains positive even after a significant drop in share price.  You have become “anchored” to the higher marker price previously available, or in the case of holding something for a long time, become “married to your position”.  This was the biggest “trading effect” bias, with an annual average performance loss of approximately -2.25%.

#1 Effect – Status Quo Bias

The most overwhelming effect is the Status Quo Bias.  With a trading effect bias of well over -5%, and possibly much worse because of the difficulty of measuring the lack of choice, this is the result of your brain’s natural reluctance to make decisions at all!

The most common example of this bias are investors hitting decision paralysis when investing available cash.  Should it be invested in stages, or all at once?  What should it be invested in?  What if the market drops right away?  Without the experience of a seasoned Advisor that can strip all of the emotions away and provide logical decisions, most people would rather choose to make no decision, and subsequently lose out on future investment returns.

The second most common example of this bias was a study of employees’ decisions when choosing how to invest their pension or group savings plan.  It was found the vast majority left their pensions in the default selection, most often cash, and had lost out on years of potential investment returns.  This was such a harmful effect that most pension and group plan administrators shifted to providing a “Balanced” allocation as the default option, instead of cash, or more recently forcing a decision via “Investor Questionnaire” before allowing the employee to participate at all.

How do we protect against these biases?

• Take the guesswork of which stocks to own and when to own them straight out of the equation by owning broad market indexes.

• Hold a variety of types of investments that naturally work with one another.  If one type of investment isn’t working at a particular time, that’s fine, something else in the portfolio will be working.

• Admitting Mistakes – can you, or your Advisor, remain humble and admit when something went wrong, and sell a loser?

• Know your limits!  If you don’t have the experience or don’t understand the choices you have available, then don’t do it yourself!  You may struggle through one round of decisions, but can you do it constantly?  How about when the market panics?  Most people experience decision paralysis exactly when they need to act.

• Separate yourself from your decisions!  Find a good Advisor who will help you make these decisions, and then ensure that you follow through on implementing recommendations.

When the facts change, I change my mind.  What do you do, sir?

John Maynard Keynes

Onwards and Forwards

In closing, first I’ll say thanks for sticking with me this long!  As I’ve mentioned to many of you during our planning and investment conversations, I truly do love this topic, and I hope it showed.  The more we discuss your money history and your financial emotions, and identify your biases, the better I can serve you in helping to make the optimal financial decisions…especially in the face of uncertainty!

I’ll ask you now to give a moment’s thought:

Do you have cash that you “don’t know what to do with” in the bank right now, earning nothing in a savings account?

Have you gone through your pension or group plan options in the past 5 years…or ever?

Do you or someone you know have a major financial decision that needs to be made, such as commuting a pension, or investing a windfall such as a property sale or inheritance?

Are you currently going through a “high impact” life event, such as a new baby, a career change, or a divorce?

If any of these situations sound familiar to you, or are affecting someone you know, please reach out!  Let’s crush those biases together and get you on the right track!

[i] “What is an Economic Man? Definition, Use in Analysis, and History”

https://www.investopedia.com/terms/e/economic-man.asp

[ii] “On the Psychology of Prediction” – Kahneman, Tversky

https://psycnet.apa.org/record/1974-02325-001

[iii] Thinking: Fast and Slow, p. 44 – Kahneman, Tversky

[iv] “Cognitive Reflection Test”

https://en.wikipedia.org/wiki/Cognitive_reflection_test