Best Quotes From Fooled By Randomness By Nassim Taleb:
Bullish or bearish are terms used by people who do not engage in practicing uncertainty, like the television commentators, or those who have no experience in handling risk. Alas, investors and businesses are not paid in probabilities; they are paid in dollars. Accordingly, it is not how likely an event is to happen that matters, it is how much is made when it happens that should be the consideration. How frequent the profit is irrelevant; it is the magnitude of the outcome that counts. It is a pure accounting fact that, aside from the commentators, very few people take home a check linked to how often they are right or wrong. What they get is a profit or a loss. As to the commentators, their success is linked to how often they are right or wrong. This category includes the ‘chief strategists’ of major investment banks the public can see on TV, who are nothing better than entertainers. They are famous, seem reasoned in their speech, plow you with numbers, but, functionally, they are there to entertain – for their predictions to have any validity they would need a statistical testing framework. Their frame is not the result of some elaborate test but rather the result of their presentation skills.
Unfortunately, the techniques used in economics are often imported from other areas – financial economics is still a young discipline (it is certainly not yet a ‘science’). People in most fields outside of it do not have problems eliminating extreme values from their sample, when the difference in payoff between different outcomes is not significant, which is generally the case in education and medicine. A professor who computes the average of his students’ grades removes the highest and lowest observations, which he would call outliers, and takes the average of the remaining ones, without this being an unsound practice. A casual weather forecaster does the same with extreme temperatures – an unusual occurrence might be deemed to skew the overall result…So people in finance borrow the technique and ignore infrequent events, not noticing that the effect of a rare event can bankrupt a company.
Sometimes market data becomes a simple trap; it shows you the opposite of its nature, simply to get you to invest in the security or mismanage your risks. Currencies that exhibit the largest historical stability, for example, are the most prone to crashes…The problem is that we read too much into shallow recent history, with statements like ‘this has never happened before,’ but not from history in general (things that never happened before in one area tend to eventually happen). In other words, history teaches us that things that never happened before do happen. It can teach us a lot outside of the narrowly defined time series; the broader the look, the better the lesson. In other words, history teaches us to avoid the brand of naive empiricism that consists of learning from casual historical facts.
Investors, merely for emotional reasons, will be drawn into strategies that experience rare but large variations. It is called pushing randomness under the rug. Psychologists recently found out that people tend to be sensitive to the presence or absence of a given stimulus rather than its magnitude. This implies that a loss is first perceived as just a loss, with further implications later. The same with profits. The agent would prefer the number of losses to be low and the number of gains to be high, rather than optimizing the total performance.
I can use data to disprove a proposition, never to prove one. I can use history to refute a conjecture, never to affirm it. For instance, the statement: The market never goes down 20% in a given three-month period…can be tested but is completely meaningless if verified. I can quantitatively reject the proposition by finding counterexamples, but it is not possible for me to accept it simply because, in the past, the market never went down 20% in any three month period (you cannot easily make the logical leap from ‘has never gone down’ to ‘never goes down’). Samples can be greatly insufficient; markets may change; we may not know much about the market from historical information.
Markets (and life) are not simple win/lose types of situations, as the cost of the losses can be markedly different from that of the wins. Maximizing the probability of winning does not lead to maximizing the expectation of the game when one’s strategy may include skewness, i.e., a small chance of large loss and a large chance of a small win. If you engaged in a Russian roulette-type strategy with a low probability of a large loss, one that bankrupts you every several years, you are likely to show up as the winner in almost all samples – except in the year when you are dead.
Optimism, it is said, is predictive of success. Predictive? It can also be predictive of failure. Optimistic people certainly take more risks as they are overconfident about the odds; those who win show up among the rich and famous, others fail and disappear from the analyses. Sadly.
Our brain is not cut out for nonlinearities. People think that if, say, two variables are causally linked, then a steady input in one variable should always yield a result in the other one. Our emotional apparatus is designed for linear causality. For instance, you study every day and learn something in proportion to your studies. If you do not feel that you are going anywhere, your emotions will cause you to become demoralized. But reality rarely gives us the privilege of a satisfying linear positive progression: You may study for a year and learn nothing, then, unless you are disheartened by the empty results and give up, something will come to you in a flash…owing to this nonlinearity, people cannot comprehend the nature of the rare event.
Consider a bet you make with a colleague for the amount of $1,000, which, in your opinion, is exactly fair. Tomorrow night you will have zero or $2,000 in your pocket, each with a 50% probability. In purely mathematical terms, the fair value of a bet is the linear combination of the states, here called the mathematical expectation, i.e., the probabilities of each payoff multiplied by the dollar values at stake (50% multiplied by 0 and 50% multiplied by $2,000 = $1,000). Can you imagine (that is visualize, not compute mathematically) the value being $1,000? We can conjure up one and only one state at a given time, i.e., either 0 or $2,000. Left to our own devices, we are likely to bet in an irrational way, as one of the states would dominate the picture – the fear of ending with nothing or the excitement of an extra $1,000.
One of the most irritating conversations I’ve had is with people who lecture me on how I should behave. Most of us know pretty much how we should behave. It is the execution that is the problem, not the absence of knowledge. I am tired of the moralizing slow-thinkers who pound me with platitudes like I should floss daily, eat my regular apple, and visit the gym outside of the New Year’s resolution. In the markets the recommendation would be to ignore the noise component in the performance. We need tricks to get us there but before that we need to accept the fact that we are mere animals in need of lower forms of tricks, not lectures.
Probability entered mathematics with gambling theory, and stayed there as a mere computational device. Recently, an entire industry of ‘risk measurers’ emerged, specializing in the application of these probability methods to assess risks in social sciences. Certainly, the odds in games where the rules are clearly and explicitly defined are computable and the risks consequently measured. But not in the real world. For mother nature did not endow us with clear rules. The game is not a deck of cards (we do not even know how many colors there are). But somehow people ‘measure’ risks, particularly if they are paid for it.
One would think that when scientists make a mistake, they develop a new science that incorporates what has been learned from it. When academics blow up trading, one would expect them to integrate such information in their theories and make some heroic statement to the effect that they were wrong, but that now they have learned something about the real world. Nothing of the sort. Instead they complain about the behavior of their counterparts in the market who pounced on them like vultures, thus exacerbating their downfall. Accepting what has happened, clearly the courageous thing to do, would invalidate the ideas they have built throughout an entire academic career.
Being a Successful Trader Isn’t About Being Right or Wrong – It’s About Overall Performance:
So many new traders get caught up in thinking that becoming a successful trader is about making accurate forecasts and predictions. But the probabilities of being “right” or “wrong” aren’t the only things that matter – the magnitude also needs to be accounted for.
In essence, this is the concept of expected value. In the vast majority of cases, however, people would rather be “right” (experience a profitable trade) most of the time than be “wrong” (experience a losing trade) even if overall performance is negative due to the magnitude of those wins/losses. These are cases of people choosing short-term emotional gratification over long-term profitability.
Take, for example, Trader A who is profitable on 90% of his trades, but his average win is $100 and average loss is $1,000. Sure, it might feel good to “win” on about nine out of ten trades, but the one out of ten trades that’s a loser will wipe out his gains and then some. The expected value per trade is negative $10 (meaning Trader A has a strategy that loses money long-term).
Now consider Trader B – the exact opposite of Trader A – who is only profitable on 10% of his trades, but his average win is $1,000 and his average loss is $100. It might not feel good to take losses so often, but the 10% of trades that end up winners more than make up for the losses over time. It’s hard to associate being “right” 10% of the time with “winning”, but Trader B is actually profitable.
The point of sharing this is that probabilities – by themselves – don’t matter. You might have a high win rate and make money on 90% of trades, but if you lose an ungodly amount on the 10% that you’re wrong – then you’re building up your account just to tear it back down.
Successful Traders Are Practitioners of Uncertainty – They Think Probabilistically:
Most new traders will come across a trader like Trader A, see a phenomenal win rate, and automatically assume he’s a great trader (even though he’s a net loser). While Trader B with his low win rate is constantly battling losing streaks (but is net profitable).
Similar to Quote #9 above – it’s extremely hard for people to look at these two traders and imagine that Trader A is essentially losing $10 per trade and Trader B is winning $10 per trade. We’re just not naturally wired to think in terms of the expected value formula.
Instead, we have a bias toward “winning” more often than “losing” – even if it doesn’t necessarily equate to long-term profitability. And we only tend to view results from an individual trade perspective – like for Trader A – either a $100 win or a $1,000 loss.
Winning $100 on most trades feels so good emotionally that we almost forget the possibility of loss even exists. But when that loss finally occurs, it takes out all of our progress. This is a small example of how most traders fail to take “rare events” into account.
But just because something is unlikely to happen, doesn’t mean it won’t – and it doesn’t mean that the magnitude of it won’t be large. And in addition to that, historical data can only do so much when it comes to predicting events (unforeseen events can and do happen).
In the end, traders need to accept the uncertainty and randomness inherent within markets and be prepared for such rare events.
Learn More in the Trading Success Framework Course
Written by Matt Thomas (@MattThomasTP)
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