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Investing Lesson from How Doctors (Don’t) Think

It was a summer afternoon. Sunnybrook Hospital in Canada received an accident case. A young woman driver had a head-on collision with another car. She had suffered broken bones everywhere.

The doctors found multiple fractures in her ankles, feet, hips, and face. Initially, they missed the fracture in her ribs that they later found out.

During her diagnosis, the doctors found something else that was not right with the woman. Her heart was beating unusually. The rhythm of her heartbeat had become wildly irregular. It was either skipping beats or adding extra beats.

The emergency room staff soon diagnosed the heart problem – or thought they had. The woman told them that she had a history of an overactive thyroid. An overactive thyroid can cause an irregular heartbeat. So the staff no longer needed any further investigations for the source of the irregular heartbeat but to treat it.

By this time, they had invited an intern named Don Redelmeier, whose job at the hospital was, in part, to check the understanding of the specialists for mental errors. In other words, Redelmeier’s job was to serve a check on other people’s, especially doctors’, thinking.

As the emergency room staff was about to administer the drugs for hyperthyroidism to the woman patient, Redelmeier asked them to slow down. To wait. Just a moment. Just to check their thinking – and to make sure they were not trying to force the facts into an easy, coherent, but ultimately false story.

Something bothered him. As he later said, “Hyperthyroidism is a classic cause of an irregular heart rhythm, but hyperthyroidism is an infrequent cause of an irregular heart rhythm.”

So the emergency room staff had quickly jumped to the conclusion that the woman’s excess thyroid hormone production was the cause of the dangerous beating of the heart. They did not bother to consider what were statistically far more likely causes of an irregular heartbeat.

But then, doctors, like most of us, do not think statistically. That would involve the reflective part of the brain, which would require incremental effort, and be time-consuming.

Just like 95% of drunk drivers don’t think the statistics that show that you are more likely to be killed if you are driving drunk than if you are driving sober, applies to them. Just like 100% of lottery buyers don’t think the statistics that show that your chance of winning the bumper prize is around one in 14 million, or 0.00001%, applies to them. Just like 99% of people who borrow to trade in stocks during bull markets don’t think the statistics that show that you are more likely to be ruined financially if you are trading with borrowed money than if you are with your own, applies to them.

Anyways, coming back to what was happening in the emergency room of Sunnybrook Hospital, Redelmeier asked the staff to search for other, more statistically likely causes of the woman’s irregular heartbeat. That’s when they found her collapsed lung.

So, like her fractured ribs, her collapsed lung had failed to turn up on the X-ray. But unlike her fractured ribs, her collapsed lung would have killed her in some time.

It is then that the staff ignored the woman’s thyroid and treated her lung. And it is then that the woman’s heartbeat returned to normal.

She was tested for the functioning of her thyroid gland the very next day, and the results were anything but shocking for Redelmeier and his team. The woman’s thyroid hormone production was perfectly normal.

“It was a classic case of the representativeness heuristic,” said Redelmeier. “You need to be so careful when there is one simple diagnosis that instantly pops into your mind that beautifully explains everything all at once. That’s when you need to stop and check your thinking.”

From Emergency Room to Stock Market
I read this above story in Michael Lewis’ wonderful book titled The Undoing Project, which is the story of noted psychologists Daniel Kahneman and Amos Tversky and how their friendship and work has helped us think better.

The representativeness heuristic that Redelmeier talks about above is used when making judgments about the probability of an event under uncertainty – like the probability of irregular heartbeat caused by hyperthyroid versus collapsed lung.

When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely.

We use representativeness because it is easy on our brains (this looks like that, this goes with that). The problem is that people overestimate its ability to accurately predict the likelihood of an event, like the emergency room staff did in the case of the woman patient in the above story and jumped to the easiest conclusion that her irregular heartbeat was caused by hyperthyroidism.

Medical professionals often jump to conclusions. Jerome Groopman, author of How Doctors Think, says that “most incorrect diagnoses are due to physicians’ misconceptions of their patients, not technical mistakes like a faulty lab test.”

Groopman explains that many doctors jump to conclusions in the following ways –

  • They assume the patient will state all relevant symptoms (or are forced to make an assumption due to thinking that seeking further personal information may lead to embarrassment),
  • They assume the patient will not want to undergo any unpleasant (albeit effective) treatment,
  • They assume the patient is a hypochondriac and therefore do not take their complaints seriously, or
  • They make a diagnosis even though they have not heard or understood all of the complaint and for whatever reason do not ask for clarification.

Anyways, let’s move away from doctors and the hospital emergency room and get into the stock market. Even here, representativeness and first conclusions rule the roost. This is because even the stock market involves making judgments under uncertainty.

In fact, ‘jumping to conclusion’ is one of the favourite sports of most people participating in the stock market. And this is what hurts them gravely. Like it hurt me big time when I bought the stock of Hotel Leela just after visiting its beautiful and fully-booked property a few years back, and quickly jumped to the conclusion that it automatically meant a great business. Truth be told, it was gruesome. And I sold out after a 45% loss.

Now, it isn’t that what first comes to our mind (the first conclusion), whether while treating a patient or in stock investing, is always wrong. In fact, we all jump to conclusions by making inferences and assumptions in most things we do in life, and it often helps us.

But what hurts us is that the existence of what first comes to our mind leads us to feel more certain than we should be that it is correct. In fact, mistakes are much more likely when people are unaware that they have jumped to conclusions, and instead think that their assumptions are actually facts.

Consider what Prof. Sanjay Bakshi told me when I interviewed him first in August 2012 –

Our minds jump to conclusions. Humans tend to solve problems by using the first solution that comes to mind. Charlie Munger often says that “to a man with a hammer, everything looks like a nail.”

Let me give you an example of this from my own experience, as to why first conclusions are often wrong.

Let’s go back to the year 2003. This was the time when the steel industry was down in the dumps, and it was about to take off for a very big bull run. At that time, some of my value investor friends and I came to the conclusion that steel prices are going to go up. This was a time when most steel companies in the world were losing money. In fact, there were just a handful of companies that were making any money.

The steel cycle had been down for a very long time. We felt that here was a tipping point coming and things would get better, and steel prices will go up because steel capacity is getting tight and world economy, and in particular, Chinese economy, is growing.

Therefore, we thought there was going to be a shortage of steel, and it would take a long time for the shortage to go away because steel is a long gestation period industry.

We concluded that steel companies would benefit because of the huge rise in steel prices, which was a great insight. So far so good! But apart from concluding that rising steel prices must be good news for steel stocks, we also concluded that the same would be horrible news for auto stocks.

This kept us away from auto stocks based on the pure automatic first conclusion that high steel prices were bad news for auto stocks. That first conclusion turned out to be wrong.

Think about why it went wrong. The value of an auto stock (or any stock) is based on present value of its future cash flows. And rising steel prices may or may not be bad news so far as those cash flows are concerned. A rising input price may be passed on to the customer without suffering any volume decline. Or the rise in volumes caused the industry growth, may more than offset the shrinkage in margins because of a rise in input prices which the company is unable or unwilling to pass on to customers.

So the key factor to think about is not the impact on margins but the impact on cash flows. But the mind doesn’t always do this automatically. It jumps! It jumps to first conclusions, which are often wrong.

So you really have to train yourself out of first conclusion bias. You have to avoid seeking easily available answers to questions that begin with “why”.

Let me explain this with the help of an example.

Let’s look at this hypothetical stock. It has substantial cash on its balance sheet. It has no debt or other liabilities which have a prior claim on that cash. It also has an operating business. But the market value of the company is less than cash assets alone. This is a “cash bargain”.

Many of my students when they look at this thing, they say, “My God, this is not possible! How is it possible that in a market that is supposed to be efficient, you are seeing a stock selling below cash?” They want to buy it based on their first conclusions.

But under what circumstances would that first conclusion be wrong?

You see, the mind does not automatically think in those terms. The mind, instead, latches on to the first conclusion, which, in this case, is that the stock is ridiculously cheap, so it must be bought.

Now, I tell my students, “Let’s force us to think of three reasons why buying such a stock would be a mistake.”

They have to come up with three reasons. Why three? Why not one? Why not four? Well, three is good enough! The idea is to force yourself to come up with multiple reasons that go contrary to your first conclusion and only when you force your mind to come up with three, will it generate three very good reasons.

So what are the three reasons for “not” buying that cash bargain based on your first conclusion that it’s cheap?

Reason 1: Cash burn: Maybe the operating business is losing money and cash will be dissipated away in just a few quarters.

This is what happened to dotcoms after that bubble burst. Many companies had raised cash in the IPO bubble and now that the bubble had burst they were selling below cash. There wasn’t any debt because no sane banker would lend such start-ups any money.

But the operating businesses were burning cash at a rapid pace and it was only a matter of time when the cash would disappear.

Buying such “cash bargains” when they became available in the stock market, would have been a mistake.

Reason 2: Corporate mis-governance: What if the promoters of the company are well-entrenched because they have a 70% stake, and they have no intention of sharing the wealth of the company with the minority investors?

They pay no dividends, and will never liquidate the company. What’s such a company worth?

This company is what Graham once called the “frozen corporation” which will never be liquidated and will never pay a dividend.

Then what the company owns is irrelevant for minority investors, isn’t it? So just because the stock is selling below cash assets alone doesn’t necessarily make it an attractive investment.

Reason 3: Bubble market: When the markets are frothy, people desperately looking for value gravitate towards “cash bargains” because they are evidently cheap.

Well they are almost certainly making a mistake because history shows that when the markets decline, these stocks will also decline, often by much more than the market.

So, now we have three very good reasons for not buying the stock and we can now have a much more balanced debate about whether or not we should buy it.

We have trained ourselves out of first conclusion bias. And you have to do this automatically, like breathing.

To question your first conclusions by thinking forcefully about why they could be wrong – by doing this over and over again – you will become a better thinker, decision maker, and investor.

First Conclusions and Base Rates
While we are indulging in forming quick conclusions, we forget the concept of ‘base rate’, also known as prior probability or the averaged-out experience from the past.

I have already written about the subject of base rate earlier, so I won’t take your time to explain it again here.

But the thought I want to leave you today with is that of being careful when things look too certain in the stock market. When jumping to conclusions come easy. When making money looks very easy. Like now.

Remember that the base rate or prior probability of sustainably doing well in the stock market by –

  • Buying expensive stocks, is low;
  • Buying bad businesses, is low;
  • Buying bad managements, is low;
  • Buying businesses you don’t know much about, is low
  • Buying into IPOs, is low;
  • Acting on tips, is low;
  • Borrowing to trade or invest, is low.

But most people in the stock market ignore base rates completely. They jump to quick conclusions hearing stories like that of an investor who did a few or most of everything mentioned above and still did well.

Such an investor – and you will find a lot of these shouting a lot on television and social media these days – will force many other investors to ignore base rates, and to focus on his story, to fool themselves into believing that such actions can’t be all that bad for them.

Luck, surprise, and uncertainty are the three physical forces that govern the stock market. And investors who deny the existence of these three forces, especially uncertainty, often destroy their savings in the long run.

Our hero of the above story, Don Redelmeier, was never completely certain about anything, and he didn’t see why anybody else should be, either.

In fact, when you are investing in the stock market, uncertainty and not certainty is your best friend.

As Seth Klarman wrote in his letter titled The Value of Not Being Sure

Uncertainty breeds doubt, which can be paralyzing. But uncertainty also motivates diligence, as one pursues the unattainable goal of eliminating all doubt. Unlike premature or false certainty, which induces flawed analysis and failed judgments, a healthy uncertainty drives the quest for justifiable conviction.

Investing is a marathon, not a sprint. And people often get into a great deal of trouble when they are in a rush. When they start to believe in certainties, and when they start to jump to easy conclusions, completely ignoring base rates.

I hope you are not one them too. Check. Check.

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About the Author

Vishal Khandelwal is the founder of Safal Niveshak. He works with small investors to help them become smart and independent in their stock market investing decisions. He is a SEBI registered Research Analyst. Connect with Vishal on Twitter.

Comments

  1. That’s a crap story. I am a doctor and nobody gives you drugs for hyperthyroidism just based on irregular heart beat. Get a better story to prove that doctors do not think.. you picked up junk story to support your preconceived hypothesis.. shame

    • Thanks for sharing your thoughts, Dr. Ravi! I am sorry, but my intention was not paint the entire profession with a single brush. The story I shared isn’t made up but real. And I have also had similar experiences with a few doctors in the past. My ideas was to drive home the point about the first conclusion bias, and the story was just a proof. Sorry if it hurt your sentiments a bit. Regards.

  2. devendra says:

    you are right

  3. Ramaraju Chekuri says:

    Amazing Vishal. Thank you very much for sharing such a wonderful post.

  4. Very informative, thank you.

  5. Good article. It reminds you of a popular term “get a second opinion” when you doubt an illness or diagnosis. Technology in healthcare can certainly fill the gap between doctor judgement and statistical probability to arrive at reasonable conclusions.

  6. It was a wonderful article and very insightful.

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