the psychology of successful trading

Omission Bias and Financial Markets

What Is Omission Bias?

Omission Bias is the tendency to think it is better to do nothing than to do something and make a mistake. We all have this tendency because it is a fundamental part of our psychology. It can be particularly strong when thinking about how much we will regret something in future if we make a mistake. We think we will regret doing nothing less than we will regret doing something.

This matters in financial markets, because it is not necessarily better to do nothing than to do something. The tendency to inaction creates inertia. We hold on to stocks longer than we should. We fail to buy new stocks when we should also do that. Formally, this is because is no practical difference between the following two scenarios. I could invest $100 in a stock that declines 10% the next day. I could also fail to buy one stock that appreciates 10% the next day. In both scenarios, I have lost $10. But Omission Bias will make me think that scenario two is better than scenario one. This is because in scenario two I have done nothing while in scenario one I have done something.

In this way, our psychology makes us prefer to do nothing. But the only important metric to judge the quality of my trading is the financial result. And that was the same in both scenarios: I do not have $10 that I would have had.

Adverse Effects of Omission Bias

A major adverse effect of Omission Bias is that it impairs your ability to assess performance. You will only look at what you did as opposed to what you did not do. This is because you are only worried about things you did that did not work. You don’t worry about things you didn’t do that would have worked because Omission Bias takes out the regret in that second case.

But the only measure of importance is how much money you made as compared to how much you could have made. So there should be equal attention paid to opportunities missed as there is to opportunities taken which did not work out.

This adverse effect is of crucial importance.  Many investors do not have clear enough data of what has worked for them and what has not.  It is essential to have a good focus on this for a number of reasons.  

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One benefit is that you can only manage your portfolio appropriately if you have been examining its performance precisely.  A second benefit is that you might be able to identify some specific sorts of trade that you are particularly good at.  You can then seek to identify relevantly similar situations and exploit them.  Also — you might have a chance of avoiding disasters from the past occurring again!

Our ability to look at our failures and learn from them is also impeded by our natural distaste for thinking about the unpleasant — but failures are always more instructive than successes.  One might almost say that any fool can succeed — but only an expert can fail well…

Practical Consequences

A major practical impediment to any attempts to correct for Omission Bias is due to the sheer scale of the problem.  The number of shares you did not buy yesterday is absolutely huge.  There is no way you can think about all of those.  Nor should you.  The more useful comparison is to think about the shares you could have bought or the ones you almost did buy.  So that tells us that you should be looking at several buy options at a time.  Look at what factors led you to choose the one you did choose.  

Maybe you were looking at three oil companies.  You compared them on price/earnings ratios, dividends and price/book value.  You made a choice.  Did that work out?  (Don’t do this next day.  Wait for a reasonable period.  Otherwise you will just be looking at noise.)

Omission Bias is a sort of Agency Effect

What fundamentally is going on with Omission Bias is a sort of agency effect.  If something bad happens and you could have prevented it but did not, this is seen as morally less culpable than if you did something which caused a bad outcome.  After all, “you didn’t do anything.”  I think this perception might be strengthened by the fact that the law says a lot about what we cannot do but rarely says anything about what you must do.  You are at liberty to walk past a baby drowning in a pond.  You are not at liberty to throw a baby in a pond.

This might be fine morally.  But stock markets are not outlets for moral action.  They are locations where you can profit.  Or not.  Bear in mind the possibilities of Omission Bias affecting your judgements of your own decision-making and your decisions will get better and more profitable.

Learn more in the video below:

the psychology of successful trading

Stock Performance: Correlation To GDP

What Drives Stock Performance?

Everyone wants to know what drives stock performance.  It seems intuitive that over the long-term, stocks should correlate with GDP — but some have questioned this.  In this article, I will aim to show that if you look over a long enough time period and don’t cut the data, that stocks are indeed correlated with GDP.

The first issue when seeking to examine the correlation between two variables is determining the optimal dataset to examine.  This is highly contentious since it is quite likely that spurious correlations can appear in datasets that have been excessively manipulated either by cherry picking the date range or by eliminating outliers.  For that reason, I will select the largest reasonable dataset and do nothing to it.

I think the earliest reasonable starting point is somewhere around 1950.  Much before then, and one is looking at periods where the economy is so different to that of the current day, that it would be inappropriate to try to draw any conclusions.  In addition, it is not that helpful to produce a model going further back and have to say, when asked about a prediction, that “this is what happens to stock performance when Hitler invades Poland…”

GDP Development

Here is a plot showing US GDP since 1950:

This immediately looks like some kind of power law growth with a kink for the global financial crisis.  (We don’t really have enough data to look at COVID properly yet, but I expect it will produce another kink and not really disturb the trend line.)

This second plot illustrates stocks performance — it is the S&P 500 index over the same timescale.

This also shows some sort of power law rise, though it is clearly much noisier than the GDP curve.  This does not however obscure the direction of travel.


Here’s a brief aside about polynomials.  This is skippable if you don’t care what curves I am going to fit to the above two lines.  Polynomials are curves of the form y = ax^3 + bx^2 + cx + d.  The order of the polynomial is how many of the a, b, c, d coefficients are non-zero.  So a first order polynomial is just a straight line of the usual form y = mx +c.  The reason this matters is that we do not want to overfit or underfit.

If we underfit, we fail to capture information in the curve we are modelling.  If we overfit, we get all the information in the curve, but we may add some features that are not really there.  The best way to check for that is to see what happens if you extrapolate the curves beyond your training data.  If you have overfitted, it will often be the case that your predictions go insane as soon as you are off-piste — i.e. outside the training dataset.

Here is an illustration of what under and overfitting might look like.

The blue circles are some arbitrary test data we want to fit.  The other curves are the various orders of polynomial returned by a fitting package.  As you can see, the first order polynomial — just a straight line as mentioned above — is totally inadequate and is a radical underfit of the data.  The second order is not bad, but the third order is very good.

Fitting a Polynomial to US GDP

Fitting a third order polynomial to US GDP gives me the following plot.

 I have slightly adjusted the data to make it fittable.  The x-axis is now the number of days since 03-Jan-1950.  I also zeroed out the US GDP for 1950 which I don’t think matters too much.  You can see that the curve is a good fit to the data apart from a rogue rise at low day counts which again I don’t think matters because we aren’t going to try to predict GDP in 1950 — we already know it.

If you want to know the equation of that curve, it is :-5.265e-11 x + 4.351e-05 x – 0.2643 x + 414.1

If you think that the very small cubic coefficient means I don’t need third order, you are basically right but again I don’t think it matters as long as the curve extrapolates reasonably.

Fitting a Polynomial to Stock Performance

Let’s return to stock performance.  Conducting similar manipulations on the S&P500 (recalibrating to days since the start of 1950 and setting the initial value to zero) gives me a different fitted curve, as below.

Note that in both cases, there are around 25000 days between the start of 1950 and today.  (70 * 365 = 25,550).

Here, the equation of the fitted line is: 2.726e-10 x – 3.87e-06 x + 0.02366 x – 10.68

Again, the cubic coefficient is very small.  The notable point about this curve is that it is very noisy.  But it still follows a clear trend line.

Be Careful With Extrapolations

Now we get to the dangerous part.  To look at stock performance long-term, we need to see if these curves behave once we extrapolate them.  Let’s look at what they do if we double the day count to 50,000.  (This is equivalent to making the date range double — so we are currently 70 years on from 1950; 70 further years added on takes us to 2090.)

There are two reasons this is dangerous.  As I said, if the curves blow up, we have achieved nothing.  The other issue is that you can’t extrapolate power laws forever.  I will now discuss a brief example of that not working.

You will recall that in the early stages of COVID, people were plotting case counts and fitting exponentials to them.  That looked like it was panning out to begin with.  But the plot below shows what happens if you fit an exponential to the Florida case count as of yesterday.*

COVID as an Example of a Failed Extrapolation

You will recall that in the early stages of COVID, people were plotting case counts and fitting exponentials to them.  That looked like it was panning out to begin with.  But the plot below shows what happens if you fit an exponential to the Florida case count as of yesterday.*

It can be seen that the case count profile is in no way fitted by the exponential.  So is it the case that the US economy can continue growing on an exponential basis indefinitely?  Probably not.  When will it stop doing so?  No-one knows, but it might be beyond your investing lifetime.  Also, bear in mind that it will have looked like it couldn’t continue growing exponentially at most points since 1950.  We don’t know what technology or quantum computing or unimaginable developments are going to do.

I used the data science programming language Python to generate these plots. You can find put more about how that works, with a specific application to COVID, at the the link below:

Extrapolating GDP and the S&P 500

So what happens if we extrapolate the curves we fitted?  This is shown in the plot below.

This shows firstly and importantly that the curves continue to behave out to 50,000 days.

Conclusions on Stock Performance

Firstly, we can conclude that if the S&P500 and US GDP continue to develop as they have done, then by the year 2090, US GDP will have reached USD $90tn and the S&P 500 will have reached 26,000.  That would be some great stock performance!

Secondly, I have not shown what happens if you try to fit curves to only more recent data.  They are very noisy and that explains why a lot of analysis does not show any correlation.

Thirdly, this does not really backup Portnoy when he says “stocks always go up.”  Partly that is the case because of the huge noise in the S&P 500 curve.  Partly it is the case because you might have to wait a long time.  Partly it is the case because the curve only shows that the S&P 500 always goes up.  But the trend is your friend here if you wait long enough.


There are various conclusions that I have not argued for.  I have said nothing about other countries.  The above analysis would definitely not work for the FTSE-100 because that does not grow exponentially.  It seems rather to exhibit a large saw tooth oscillation between 4000 and 7000 with a period of about a decade.  That won’t correlate with anything.  Similar points apply in Japan.

the psychology of successful trading

Maybe Lockdown Kills More People Than It Saves

Adverse Consequences of Lockdown

The FT today runs the argument that there are serious adverse effects of the lockdown which should be considered when deciding when to lift it.  It could be that eventually lockdown kills more people than it saves.

These include the exacerbation of poor mental health and the way that people who are victims of domestic abuse have no escape.

Not a bad argument as far as it goes, but it is open to the objection that lives and cash are incommensurable.  I wouldn’t make that argument, but many would.  This objection points the way to a stronger argument in the vicinity.

Asking the Right Question about Whether Lockdown Kills

Lockdown will kill more people than it saves.  The choice is not “accept some economic damage in order to save lives or not.”  It is “kill group X or group Y.”

We kind of get there already with the estimate by the oncologist Sikora that excess cancer mortality caused by lack of screening will be 50,000.  But beyond that, the economic damage caused by the lockdown will be so immense that it is likely that the second round effects on future NHS spending will also be huge.  That will again cause many more deaths.  

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Moreover, those deaths are more damaging.  We may think we are killing Group Y in order to give Group X a few extra months.  Most of Group X are over 80 and with existing health conditions.  Indeed, 50% of COVID mortality takes place in a care home setting, where the median length of stay is in any case 12 months.  Group Y is younger people who would be economically active for a lifetime. If they are killed by the lockdown, they will not be.  Note that a 1% increase in unemployment causes a 0.79% increase in suicide.

However, against this it should be noted that men who smoke and are aged 80 with a morbidity factor such as diabetes still have remaining life expectancy of 5 years. This rather extraordinary fact suggests that some men who have made it that far are very resilient.

Further Problems

My second reason for scepticism about the lockdown is that we are not really doing it (in the UK). There is massive non-compliance.  Many people cannot be bothered to maintain a 2m separation even when it is easy to do so.  There are signs on Battersea Bridge stating that one should cross on the leftward pavement. This is too difficult for about 20% of people.  25% of people admit that they are non-compliant, which makes me believe that in fact, maybe 75% of people are actually non-compliant.

Also note that subject 31 caused 81% of cases in S Korea. This implies you need a 100.0% compliance rate to do anything useful. That also makes me question the point of lockdown. Though of course I am complying with it because we cannot be sure whether it works.

The primary argument for lockdown derived from the Imperial model.  This is a questionable piece of work. It is pure mathematical modelling, not a result of data analysis.  The model is not experimental observational science.  It is also “5000 lines of unaudited C from 13 years ago.”

I discuss one serious problem with the Imperial model here:

Lockdown appears more reasonable based on the latest data, however. One powerful argument against lockdown was that India was clearly incapable of doing any kind of stringent and comprehensive lockdown. Indian mortality rates were mild however. That is no longer true. It may be the case that we have to do lockdown even though lockdown kills.

the psychology of successful trading

Plan Continuation Bias In Financial Markets

What is Plan Continuation Bias?

Plan Continuation Bias is a major factor driving investor losses in stock and other financial markets.  For example, many investors tend to hold on to losers for too long when they should cut their losses.  In this article, I will outline how this bias permeates our psychology by looking at how it works in air crashes, and then go on to examine its effects in financial markets. Investors will learn how to address this bias and improve trading performance.

Plan Continuation Bias, simply put, is the tendency we all have to continue on the path we have already chosen or fallen into without rigorously checking whether that is still the best idea or even advisable at all. Operating with this bias, as with the other 180+ biases that are an unavoidable feature of our psychology, is generally a good idea. We simply don’t have the time to constantly re-analyse our decisions.

Plan Continuation Bias in Plane Crashes

Berman and Dismukes wrote a NASA report on this problem, which they describe in a brief article. They define Plan Continuation Bias as follows:

a deep-rooted tendency of individuals to continue their original plan of action even when changing circumstances require a new plan

Berman and Dismukes “Pressing the Approach” Aviation Safety World, December 2006, pp. 28–33

The authors describe two air crashes which were in their view caused by the operation of Plan Continuation Bias. Flight 1420 into Little Rock, Arkansas crashed in June 1999 because the pilots ignored alarms and persisted with an approach in difficult weather conditions. Similarly, Flight 1455 crashed in March 2000 in Burbank, California because the pilots continued with an approach even though they knew that they were flying at 182 knots which they knew was 40 knots above the target touchdown speed.

It is very easy for us to sit here on the ground and do armchair flying. We would not have made these errors we say to ourselves, wrongly. If we saw that we were flying too fast or that there were multiple alarms sounding, we would abort the landing and go around. This is not difficult to do. This quick and wrong simulation of the pilots misses out many germane factors. The pilots are under some pressure to land planes quickly and efficiently for cost reasons. There are no guarantees that going around will improve weather conditions. But ultimately, the major factor in these crashes in human cognitive bias.

Plan Continuation Bias has significant effects on the psychology of all of us. As the authors observe,

Our analysis suggests that almost all experienced pilots operating in the same environment in which the accident crews were operating, and knowing only what the accident crews knew at each moment of the flight, would be vulnerable to making similar decisions and errors

Berman and Dismukes “Pressing the Approach” Aviation Safety World, December 2006, pp. 28–33

Effects in Financial Markets

Plan Continuation Bias is just as relevant a factor in making decisions in financial markets. We can be just as liable as the pilots described above to sticking to the plan. We bought a stock, it was a good idea at the time, and we continue to hold it even though the original reasons for it being a buy have dissipated or not transpired.

In trading, while no one is going to be killed, it is still an environment in which decisions need to be made on an inadequate data set and sometimes under time pressure. It is also going to be a highly charged situation emotionally. The inadequate data set could result from factors such as the impossibility of predicting the future or the sheer scale of the operations of a listed company. Time pressure is particularly prevalent in day trading, but even more long-term investors are susceptible to effects such as feeling that “money is burning a hole in their pocket” and they need to put a trade on right now. The emotional charge comes from losing money. We are all highly averse to losses — in fact, we seem to be 2.5x more averse to losing money than we favour gaining the same amount. It hurts to lose. It challenges our self-perception.

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How To Prevent Plan Continuation Bias From Impeding Your Stock Market Performance

  • Try to minimise the effects of an inadequate data set by either doing more research or not trading unless you are certain or can set downside limits. Don’t take trades where it looks like you need to know everything about a company or where you think other market participants can easily know more than you. Avoid trading assets you don’t understand like Bitcoin.
  • Don’t do anything under time pressure. You will need to get used to FOMO because “just getting one more trade on” will kill you quite quickly. It’s fine to miss things. It is much more important to get a small number of decisions right than to try to catch every opportunity
  • Don’t trade when feeling strong emotions and try to trade emotionlessly. This is hard to do. It is particularly hard to learn this from practice/dummy accounts. It simply doesn’t hurt very much to lose play money. You should still start here, but be prepared for real life to be much harder. Get more Zen about it. It doesn’t matter if a trade loses as long as you are up over the year.

See also:

the psychology of successful trading

Women Traders Are Better: More Data


We now have more data showing the women traders are better.

Warwick University Business School (“WUBS”) have conducted a fascinating study on the investment performance of men and women.  See: WUBS:

They show that women perform significantly better with a good sample size and temporal range.  They make some interesting remarks on why this might be.  I think I can add some extra psychological depth to this — so we can see that female traders appear to have some quite deep natural advantages and they should feel encouraged about managing their own investments.

What WUBS did was collaborate with the share dealing service offered by Barclays Bank.  They looked at 2800 investors over three years.  There are various ways of measuring stock market performance, but one of the most common is to compare the performance of a portfolio with a relevant stock market index.  (I explain what a stock market index is here: What Is A #Bear #Market?)

Data on Women Traders

It is quite hard to outperform an index consistently.  This fact is what lies behind the recent strong growth of tracker funds.  You may as well buy the index if you can’t beat it.  The results from the WUBS study showed that women consistently outperformed the FTSE-100 index and men did not.  The male investors returned 0.14% above the index which is basically statistically consistent with having performed equivalently to it.  However, I suspect that these investors would have been better off just buying the index rather than paying a lot of trading fees to obtain the same performance.

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The female investors outperformed the FTSE-100 by a massive 1.80%.  This may not sound much, but it is actually huge.  Done over a lengthy period, it would lead to significantly improved results.  Let us assume that the FTSE-100 returns 5% a year.  If you started with £10,000 and performed as the male investors do, you would end up with £45,000 after 30 years.  (It is always important to think long term in the stock market; to prefigure part of the answers I will discuss below, the women seem to understand this.)  

The female investors would turn £10,000 into £72,000 over the same 30 year period.  That is a huge improvement over £45,000 and bear in mind that the female investors have taken the same risk, making it even more impressive.  (One caveat is in order here: no one performs this consistently over the long-term–if they say they do, it is a huge red flag.  Remember Madoff?  But the point stands.)

How are female investors outperforming?

WUBS and Barclays set out a few reasons which could explain the outperformance.  One of them is the one we already know about.  Women are less over-confident than men.  I explain how that works here: Women Are Better Traders Than Men.  In summary, women tend less often to think that their new idea is brilliant and then abandon their previous idea before it has had time to work.  Men on the other hand just get extremely convinced about their new sure-fire idea and go with it.  Interestingly, women’s lack of over-confidence is not manifested in what they say about their beliefs.  They just don’t act on them as often.  We could discuss philosophically what that means about our account of belief — but the key point is that women are less likely to trade in deleterious ways!

What Mistakes do Women Traders Avoid?

Several reasons are suggested.  There are three that I think are especially interesting.

  • Women stay away from terrible ideas like #Bitcoin
    • I have not seen any data on how many women bought into Bitcoin, but is is certainly consistent with my claim in the second post above that female investors have stayed away — we know that women did not vote for Trump very often and much less so if they had college degrees.  In addition all of the online hysteria (!) from Bitcoin boosters appeared to be from deluded male market participants.
  • Women avoid “lottery style” trading
    • It has always struck me as insanity to own a lot of penny stocks which are supposed to return ten times the amount you invest in a year because this almost never happens. A far better approach is just to sit still in major stocks for a long time, with maybe some spicy options for fun in a minor section of the portfolio.  The problem with picking the next Amazon (or Bitcoin, for that matter) is that you can’t.  You would have to own a million penny stocks for each Amazon or Apple.  So this strategy is exciting but completely unsuccessful.
  • Men hold on to their losers
    • It seems that women are better at getting out of something which hasn’t worked.  This came very close to home for me.  Infamously, I am still holding Deutsche Bank stock, partly because I recommended it in my book as a contrarian trade.  Banks are supposed to trade at at least book value (in fact, 2.0x before the crisis).  Because it is buying something for a quarter of its value.  That hasn’t worked for me yet — maybe a female trader would have got out of this position a long time ago.
the psychology of successful trading

Trading Psychology: Optimise Your Performance

Why is Trading Psychology Important?

Understanding trading psychology is one of the most important but also less often done tasks for investors.  Of course, everyone realises that they need to analyse the investments they are considering buying.  But many traders do not realise that winning in investment is mostly about successfully predicting what other market players will do.  Before they do it. And that is a psychological task.

Most of the advice on the internet is not really psychology.  It is quasi-psychology.  You might get famous traders telling you things like “I always played tennis in the morning before my best trades to make sure I felt good.”  This is useless.  By all means, study what these guys do. You may get insights into how they look at opportunities and maybe any tricks they have for bouncing back from a loss.  But famous traders don’t have any specific training in psychology. So, if you are specifically wanting to improve your own trading psychology, adopting their tips (such as the tennis one above) won’t really help you in achieving that goal.

What you need to look at is the psychological literature. This is published in the academic journals. You also need to do that having spent a decade on the trading floor. Fortunately, you don’t need to do that yourself since I have done it for you. So all you need to do is read the book.

The other sort of person who cannot help you is someone merely described as a psychologist. That’s better than nothing. But they need to be the right sort of psychologist, as I will set out in the next section.

Is that the right sort of psychology?

There are some actual psychologists who write on the topic and are experts in the field.  But be careful about their specialisms.  Someone who is a clinical psychologist may be an expert in schizophrenia. They may however not necessarily know any other aspects of human psychology.  And of course these experts do not have any serious trading experience. So they definitely can’t help you improve your trading psychology. Unless you have schizophrenia. But in that case, you have more pressing issues to attend to than the performance of your portfolio.

There are also a lot of completely unqualified people who write on these topics. They don’t know any psychology and they don’t know anything about trading. These individuals are all over the internet. But you should not waste any of your time on them.

The right sort of psychology is actually called Theory of Mind. This is the label for the way we predict and explain the behaviour of others. This is exactly the area in which I specialise. You can check that out in my first book:

To identify the right sort of person, you need to ask two questions. Does this person have significant trading experience? Are they qualified in a related field?  I am one of these people.

How To Optimise Your Psychology

To convince you of this, I will outline my ideas on how to optimise your trading psychology.  The first thing to know about is that we have a lot of cognitive biases. (Here’s a list on Wikipedia: there are hundreds! )

These are mental shortcuts that are often useful when we want a quick and dirty answer. However, they are often very unhelpful when we are trying to get something right.  One example is Confirmation Bias. This is where people look only for evidence that supports what they already believe.  There have been many robust psychology experiments published, that show time and time again that we do this often.

This isn’t something which is optional. Cognitive biases are a fundamental part of our wiring. More intelligence does not make you immune to biases. At least if you know about them though you have a chance to counteract them.

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The first thing to note here is that if you use this bias when making your own trading decisions, you will make bad decisions.  Every time!  So you will definitely not be optimising your trading psychology.  But here’s the key point: everyone else in the markets will be doing it too.

So what does that mean?  It means you need to know about Confirmation Bias. You need to think about it in a market context.  Look out for it in yourself and be careful.  Expect it in other market players and trade accordingly.  

That’s how you stand the best chance of optimising your trading psychology. 

See Also: The Illusory Truth Effect And Financial Markets

the psychology of successful trading

Smart People Still Have Biases


Some people deny that smart people still have biases. Here is a description of one example of that:

They are wrong however. The psychological literature makes this clear. I will outline the data showing that in this article.

What are Cognitive Biases?

There are a large number of cognitive biases operative in our psychology. There were over 180 at the last count, and that is just the ones we know about so far.  All of these biases are largely invisible to us in their operation. They are extremely hard to eradicate.  Significant financial incentives do not cause reduction of the effects of some of these biases.  I have discussed myself at length (Short, 2017) the way biases can cause highly suboptimal decision-making. That happens even when there are very serious financial consequences.

The types of bias I mean would be exemplified by Confirmation Bias. This occurs when people look for evidence which confirms hypotheses they already believe.  I think we should also consider Gender Bias in this same arena. However, the claim we should not represents a potential objection to my position.  I will address that below. But first I will show that intelligence offers no protection against implicit biases.

Smart People Still Have Biases: Three Examples

Here are three types of bias where it was not the case that more intelligent subjects exhibited less bias.

  1. Myside Bias — this is related to Confirmation Bias.  It occurs when people evaluate and generate evidence or test hypotheses in a way that conforms to their prior opinions and attitudes.  Stanovich, West and Toplak (2013, p. 259) found that the “magnitude of the myside bias shows very little relation to intelligence.”
  2. Dunning-Kruger Effect: unskilled persons also lack insight into their relatively poor abilities in an area.  However, similar bias effects operate at the other end of the spectrum.   Schlösser et al. (2013, p. 85) report that their model “partially explained why top performers underestimate their performances.”  (I am assuming a correlation here between high intelligence and an ability to be a top performer in the fields of endeavour examined by the authors.)  But here we see that intelligent subjects are also not immune from a variant of the Dunning-Kruger Effect.
  3. The Gambler’s Fallacy — this is the tendency to think that fixed probabilities are altered by past events.  For example, the odds of getting heads on throwing a fair coin are always 50%, irrespective of what has happened previously.  If someone sees heads ten times in a row and then says either “it must be heads again next” or the opposite, they are exhibiting this apparently maladaptive heuristic.  Xue et al. (2012) found that “individuals’ use of the [Gambler’s Fallacy] strategy was positively correlated with their general intelligence.’’
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Smart People Still Have Biases: A Potential Objection

I will close by considering one potential objection to my account.  This is that Gender Bias is not a cognitive bias and should not be considered in the group above where intelligence is not a protective factor.  I will counter this objection in a number of ways.

  1. If Gender Bias is not a cognitive bias, what is it?  It results in a systematic slanting of judgements away from what would be strictly rational, and that accords precisely with my working definition of a bias (Short, 2015).
  2. I do not need to assume a narrow and precise definition for Gender Bias.  I am including within it all of what people refer to by the terms Sex Discrimination, Sexual Discrimination, Homophobia, Anti-LGBTQ+ prejudice etc.  These discriminations often take place via stereotyping — assuming that everyone in group X has certain characteristics which may in fact be possessed by only some or indeed none of the members of group X.  Stereotyping appears on the standard list of cognitive biases.
  3. Krieger (1995) explicitly considers racial bias within a cognitive bias framework and includes also discussion of Gender Bias. 

So we can see that the claim that intelligence protects against implicit bias is false. 

For more on a bias in action, see:

the psychology of successful trading

What Is A Bear Market?

People often ask what the common stock market terminology of bullish or bearish means.  While these have standard meanings in normal speech — bullish being positive or optimistic, and bearish being the opposite — at least the term “bear market” has a precise technical definition in the arena of stocks.  I will explain this here.

The formal definition of a bear market is a market that has declined 20%.

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How to Understand the Definition of a Bear Market

The first item to clear up on the way to understanding the definition is “what do we mean by a market?”  Normally people will be talking about a particular stock market index, such as for example the Dow Jones Industrial Average (“DJIA”), the S&P 500 or the Nikkei-225 (“N-225”).  So now we want to know what a stock market index is.

Individual shares go up and down all the time.  One cannot say what is happening in more broad terms to “the market” by looking at single shares because of this volatility.  So instead, one looks at a basket of shares.  That is what an index is: a basket of shares listed in a specific location.  There are thousand of these, and they can be selected in many different ways.  

Illustrating a Bear Market Using the Dow Jones Index

To illustrate this, the DJIA is a basket of 30 major US shares that are selected so that they represent a good spread of major US stocks in different sectors such as computers, aircraft manufacture and banking.  The S&P 500 is a broader basket of shares issued by the 500 largest public companies listed in the US.  The N-225 is somewhat different as it is made up of the 225 largest stocks listed in Tokyo.  It is price weighted, meaning that more expensive stocks will be more heavily influential in the movement of the index.

So, put simply, if all of the component stocks in the DJIA go down 20% in a period, the whole index will also go down 20% over that time.  Since this index and the others are a broader measure of market sentiment than any single stock, if the DJIA goes down 20% in a period, we can say that it was a bearish episode for the market.  Since that is an approximate measure of the health of blue chip US equities, one would also be justified in saying that that period was a bearish period more generally for major US companies.

The DJIA has been published since 1896.  The graph looks like a long uptrend punctuated by occasional bear markets.  You can see this below.

People tend to talk less about the technical definition of a bull market.  They will often use it more colloquially to just mean “stocks are going up.”  But if one wanted to be precise, it would just be the opposite of a bear market.  It would mean that a particular index had increased by 20% from a trough.

See Also:

Why #Value Investors Should Buy #Bank Stocks

What Is “Theory Of Mind?”

Cognitive Biases And How They Affect Stock Markets

the psychology of successful trading

Value Investment: Buy Bank Stocks


I recently discussed (in Investment Styles) the two major different styles of investing: value and momentum. One difficulty with following a value approach is the difficulty in measuring value. That’s because many assets these days are not very tangible. I will suggest here that, counter-intuitively, buying bank stocks is the solution to this problem.

Value Investing: What is it?

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The value approach to investing is simple to understand, though perhaps a little harder to implement.  The basic idea is that you buy things when they are cheap.  Finding cheap assets would classically rely on looking at concepts like “book value.” That is just the accounting value of everything owned by the firm in which you are thinking of investing.

In previous decades, book value would have been simple to calculate: you could just look at the published accounts. You would check how much the accountants said each asset was worth.  A company making cars, say, would own a lot of items like factories, car parts, machinery and land.  You could look at all of those items that you could walk up to and touch, and add up all the values. And that’s it: you have calculated book value.  

If you can buy the stock for less than book value per stock, you have made a good investment.  If the company sold all of its assets, and turned that book value into actual cash, each shareholder would get more than book value.  That’s why value investing is a good idea, and why you should try to buy stocks at less than book value.

It is true that value investment has not performed well in the last decade. We need to see if this changes. I would prefer to fail buying cheap assets than buying expensive ones.

Why Using Book Value is More Difficult These Days

This simple approach is more difficult in modern times, because IP — Intellectual Property — is much more important than it used to be.  IP is anything the company owns which is valuable but that you can’t touch.  It could be a suite of software, the value of a brand, or

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simply the know-how involved in producing the products or services that the company produces.  To illustrate the scale of this IP problem for value investors, consider the following estimate.  Ocean Tomo, an investment bank, reckoned that the proportion of the value of S&P500 companies which was tied up in IP increased from 17% in 1975 to a huge 84% in 2015.  So it is clear that there is a very serious problem in adopting a value investment approach these days, and that’s unfortunate because in my opinion, it is the only approach that works.

Value Investment: What Should Investors Do?

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If you look at the balance sheet for Deutsche Bank, for example, you will see a very large number of items.  They will all have market values though.  That will be true of shares, bonds, interest rate swaps, credit default swaps, loans to corporates, futures and options, office buildings, warrants, cash in various currencies and any of the other myriad financial assets.  There will also be a certain amount of brand value but I think that will be fairly low in the mix.  So basically everything owned by Deutsche Bank could be turned into cash, and a known amount of cash, quite quickly.


Value Investment: Conclusion

Banks typically traded at 2.0x book value before the crisis.  The rule of thumb for value investors in the sector was “buy at 1.0x book value, sell at 2.0.”  Something like this is still true: you can buy Deutsche Bank at 0.3x book value and I think you should.  That’s the right approach for value investors today.

See Also:

Investment Styles

the psychology of successful trading

Investment Styles: Value Investment

What are Investment Styles?

There are two major types of investment styles which take completely different approaches.  They are value investing and momentum investing.  The former, also known as contrarianism, seeks to find cheap assets to buy.  It is called contrarianism because often it involves looking for assets which are cheap because no one likes them.  Momentum investing is simpler.  This simply observes that often, assets that have been performing well continue to do so.  So investors adopting this style just look for assets which have gone up and hope that they will continue to do so.

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Investment Styles: Value

I think the best investment style is value investing.  One reason for this is because the problem with momentum investing is that assets which have done well continue to so until they don’t.  There is no way to tell when something which has gone up will stop doing so.  And we definitely know that nothing will appreciate forever!

The difficulty with value investing is knowing when an asset is cheap.  In the early days of investing, the concept of book value was very useful.  This is simply the accounting value.  If a company owns a factory and some machinery, the book value will be close to the value for which the factory and the machines could be sold. If you can buy a share, or a slice of the company, for less than the book value per share, you should.  

One top course is:

Book Value

Book value is still very useful on many occasions.  But modern companies are very complicated, and often much of what they do cannot be valued simply.  A lot of their worth might be tied up in software, for example, which is harder to value than a building.  Or they might own a lot of IPR — intellectual property which again, is intangible and hard to value.  But the effort is worth it.  Finding a cheap company to buy is one of the best ways to trade successfully. 

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Psychology as an Aspect of Investment Style

I have written a lot about the importance of psychological factors in investing.  It is absolutely crucial that you understand these, for two reasons.  Knowing about your own psychology will help you understand and improve your decision-making processes. It will be especially valuable to know when cognitive biases are likely to cause you to make errors in evaluating investments.  But just as important is knowing how other investors will think — after all, they have the same psychology as you do!  And knowing what other investors are likely to think of an asset is the key.  Because you want to find an asset which is not just cheap — but unjustifiably so.  Then you can expect it to go up sustainably.

See also: for how to implement this.