The Black Swan by Nassim Taleb is one of the best, and one of the most interesting, books I’ve read in awhile. I’ve heard people reference Taleb’s work a few times before, mainly in financial settings, but I never quite understood why, which is what inspired me to read this. In general, Taleb argues that throughout human history, nearly all majorly significant events were considered highly improbable before they occured (think: Fall of Roman Empire, rise of Nazi Germany, Bubonic Plague pandemic, sinking of the Titanic). Essentially, the events which have the greatest impact on us lie totally outside of our field of prediction, no matter how much past data, observations, and intuitions we use in making these predictions. These momentous, unpredictable events are called Black Swans, known professionally as fat tails, because of they are perceived as rare (Taleb actually argues that these events are much more common than we’d like to think).
Taleb helps us visualize the phenomenon of Black Swans through two fictional worlds: Mediocristan and Extremistan. Mediocristan is a province where “particular events don’t contribute much individually — only collectively” (Taleb 32), and things are distributed rather evenly. Extremistan, on the other hand, is a world of extremes, where “inequalities are such that one single observation can disproportionately impact the aggregate, or the total” (33). Taleb claims that we live in Extremistan, as much as we’d like to believe it to be Mediocristan, since single events can have disproportionately large impacts on our societies. Evidence of us living Exremistan is seen through other metrics as well, most notably through wealth divides (top 1% vs 99%).
This theory of Black Swans being more significant than regular accumulations of events holds true in all fields in Extremistan, especially (and this is most relevant for us) in finance. One fact Taleb mentions really dumbfounded me, which is that: “In the last fifty years, the ten most extreme days in the financial markets represent half the returns” (275). 10 days in fifty years. The alternative, which is not addressed directly by Taleb, is likely also true: In the last fifty years, the ten most extremely negative days in the financial markets represent half the losses. This is so incredible, and at first this made me wonder why people (like me) try so hard to predict what will happen to a stock’s price day-to-day, when they should really be trying to predict the next Black Swan. I then realized that if a Black Swan can be predicted, it is no longer a Black Swan, and the opportunities for profit are no longer as large since more people are expecting it to happen. Also, once a Black Swan is predicted, a new Black Swan emerges outside of this prediction which becomes the next true Black Swan.
This is also a very sobering thought, considering the work I’m currently doing in predictive trading algorithms. I’m sure Taleb would laugh at the idea of me using historical financial data to predict what will happen to a stock tomorrow, next week, or next month. In his book, Taleb talks a lot about the misleading nature of data, using the example of “1001 days of history”, noting “You observe a hypothetical variable for one thousand days. You subsequently derive solely from this past data a few conclusions concerning the properties of the pattern with projections for the thousand, even five thousand, days. On the thousand and first day — boom! A big change takes place that is completely unprepared for by the past.” I enjoy picturing this concept like this: I know I have been alive every day for the past 40 years, so in conclusion, I can use this data to determine I will be alive tomorrow, next month, and the next 40 years after that! The irony here is that the one event which lies outside of my predictions would be the most significant event in my life. Yet, I am not deterred. I do think that patterns exist in market behavior, and because these patterns repeat cyclically, they can be used to make predictions. The mere existence of Black Swans doesn’t negate the existence of patterns, it’s just possible that one day these patterns will break. I think hedging using options is enough to counter potential negative effects, I just need to learn more about how to do this efficiently.
Another key point brought up is the effect of silent evidence, which relates to history being written by the winners and not the losers. “It is so easy to avoid looking at the cemetery while concocting historical theories” (101) says Taleb, which reminds me of the problem of survivor ship bias in quantitative trading. Survivor ship bias occurs when we train models on existing companies and their stocks, while forgetting the companies which no longer exist (bankruptcies, acquisitions). Oftentimes, this omitted data (the silent data) is most important, as we can learn what events leads up to a bankruptcy or sudden stock collapse. This relates to the idea of blind risk usually leading to better short term rewards, whilst completely backfiring later on down the road: “The fools, the Casanovas, and the blind risk takers are often the ones who win in the short term” (117).
A third interesting point which really made me think was Taleb’s take on information, and more importantly, misinformation. He writes: “The more information you give someone, the more hypotheses they will form along the way, and the worse off they will be. They see more random noise and mistake it for information” (144). This really hit close to home, as someone who is naturally paranoid and always thinking that the solution to my problems lies in all the many books, studies, and reports I haven’t read. It also makes me think about not mentally constructing narratives (which, more often than not, become red-herrings) based off of information I have.
Finally, despite being so apprehensive on the idea of predicting the future, Taleb also claims that “In the end we are being driven by history, all the while thinking that we are the ones doing the driving.” There are many ways to interpret this great quote, but I see it as history being contextual. In other words, we don’t control history, rather, events happen and build off of one another, whether this be in random fashion or through similarly repeating patterns (the latter being my theory, though this might just be me mentally constructing a narrative based on information I have).
If Mr. Taleb ran a hedge fund (which he previously did, under the name Empirica Capital), I’m guessing his strategy would be to benefit off short term, consistent profits while always hedging against potentially huge losses. This way, he would be able to see gradual returns safeguarded from catastrophic events. Likewise, he could make his bigger bets on positive Black Swans, while pursuing a small-scale trading strategy in the time between these rare occurrences. This is where the options trading, which my previous interviewee brought up, comes into play. A strategy founded on single, greatly dispersed events which are not even guaranteed to happen may sound like an overly passive, boring, and minimally profitable way to run a hedge fund. However, in recent years, many asset management companies have gone bust by neglecting Taleb’s advice and discrediting the power of Black Swans. One such instance which comes to mind are the number of hedge funds which invested in cryptocurrencies like Bitcoin, not expecting the price to drop from ~$20,000 all the way down to sub-$4,000.
Another more salient example is with optionstraders.com, a Florida-based hedge fund which recently went bust after unexpected volatility caused his positions to collapse completely. The hedge fund’s manager, who I don’t need to name because he is receiving enough bad publicity already, founded this fund on the premise of ‘naked options’ (as opposed to covered options) — essentially buying either puts or calls without hedging potential losses as a method to save money. The problem with this strategy is that if you sell a call for a stock at a strike price of $50, you are hoping that the stock will remain <= $50 up to a certain date. However, if the stock rises above 50$, the call seller loses money, since he has to buy the stock at this price which is more than what he paid for. In a very unlikely, and very unfortunate, scenario, the price of the stock could grow an incredible amount (say it goes up to $500), and the resulting losses would be even more incredible. This is what happened to optionstraders.com, which lost all of its clients’ investments (+ more) because of an unpredictably volatile period for crude oil prices.
I’ve always found that finishing a quality book is an overwhelming experience, due to the surplus of information you quickly gained (not to mention trying to remember all of this!) and also because it leaves you feeling a bit empty, as if you’ve lost something. Right now, I think of what Taleb’s work can teach me about my project. Some lessons I’ve learned from reading this book are:
- I need to think about what a Black Swan would look like in my context, and how I could protect against this.
- I need to familiarize yourself with statistical terminology. This problem was apparent to me before reading the book, but it’s now been solidified. I have never taken a formal statistics class (which is fine considering Taleb’s theory on harmful over-saturation of information), but it’s clear that I have to do some more focused learning on my own.
- “Note that a ‘history’ is just a series of numbers through time” (119). I was so happy when I read this, as it immediately made me think about how my project is essentially using ‘a series of numbers’ (right now, open/close prices + volatility) to map historical events.
- Don’t be quick to construct narratives, as this can greatly distort perception and information (you try to fit new information to the narrative you’ve established, without ever thinking if this makes sense).
- Don’t be too stressed about shortcomings, because failure is only failure if you are failing on your own established objectives, not some nebulous criteria set by outside sources.
Trying to cover every interesting topic raised in The Black Swan would be a senseless attempt, as you can simply read the book yourself considering that Taleb does a much better job explaining these issues than I do. The topics I mentioned above are the ones I find most relevant to my independent project on using Word2Vec to map similar patterns in market behavior, and also some which intrigued me most. I’m interested in hearing what other people who have read/know of this book think about its implications, and what some important lessons I might be missing?
I’m looking forward to my next read, which is a bit more focused and specific than the previous two: Advances in Financial Machine Learning by Marcos Lopez de Prado. From what I’ve heard (and read), this is more of a textbook-type piece which looks at actual solutions to common problems in ML and trading algorithms. Apparently, “Readers become active users who can test the proposed solutions in their particular setting”, which would be great in my case, as I’m moving into more actual programming and implementation.
“We no longer believe in papal infallibility; we seem to believe in the infallibility of the Nobel, though.” (Taleb, 291)