Taleb antifragile investing in oil

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Thus the question arises: if markets are now dependent on central bank interventions, in what way has risk evaluation been affected, and how might this spill over into how risk management operates going forward? Is it reasonable to assume that central bank interventions will always boost the market?

Answer: no, of course not; as we saw in both and see second chart below , once investors are risk-averse, nothing the Fed does makes any difference. Source: davidstockmanscontracorner. I have discussed the evidence undermining MPT elsewhere, but I think it is important to note here that there have been extended periods when markets have mispriced equities e. There is also clear evidence that actual data do not support a proportionality between reward returns and risk Beta or Sigma as envisaged by the Capital Asset Pricing Model "CAPM" component of "MPT," at least for long periods of time such as the year period ending in , according to data from JPMorgan; see also the chart below from Societe Generale.

This has been clearly demonstrated by data published in by the same Nobel laureate, Eugene Fama, who developed the "CAPM" in the first place. Finally, the economic theory that investors generally act rationally has been completely undermined by over 25 years of research on behavioral finance, which now includes amongst its laurels two Nobel prizes to Kahneman and Shiller in economics. Source: Dylan Grice, Societe Generale; zerohedge.

In spite of all of the evidence cited above to the contrary, and in spite of their massive losses in that were not predicted by their models, most big firms still use some version of Value at Risk "VaR" modeling to monitor and control the risk of their in-house portfolios. Famous author and thinker Nassim Nicholas Taleb has comprehensively criticized such models as unrealistic and prone to all sorts of errors.

His books include Fooled by Randomness , 2nd Ed. One of Taleb's chief contributions has been his rigorous evaluation of asymmetric or "fat tail" risk. He has made a compelling argument in favor of the idea that most change comes from huge events that are intrinsically unpredictable, i. This was completely unexpected and, although caused by a variety of stressors in the system, was at least in part caused by program trading.

Obviously, from the turkey's point of view there is no warning of what is coming, and the result is catastrophic for the turkey at least. Applying Taleb's metaphor to the crash, it seems clear that fat-tail risk was not sufficiently discounted by pretty much everyone.

Of course, there were a few people who actually benefited from the crash, either because of dumb luck or what Taleb calls "antifragile" characteristics in their asset allocations. I personally did very well out of that catastrophic market collapse, as I sold a month before the drop and then bought back in during the week it bottomed. However, I was a geologist at the time, and it was clearly an example of once-in-a-lifetime dumb luck.

Source: N. Taleb; businessinsider. Black Swans are often observed in nature or in the interaction of mankind with nature: e. There is good reason to suspect that new Black Swan events could hit the markets again at any point in time. By definition this would not be an expected event, which automatically might exclude current risks that are at least partially discounted, i. However, some unpredictable chain of events that involves a combination of the aforementioned threats with new unknown factors might easily produce a new Black Swan.

So although we can't predict Black Swans, we can take note of conditions of asymmetric risk or imbalances that suggest risk is not adequately modeled. In his most recent book, Taleb describes how fragility and antifragility work mathematically, and in practical examples. Let's use your car as an example of fragility.

If you drove it into a wall at 50 mph it would cause far more damage than ten times the amount caused by running it into a wall 10 times at 5 mph. Likewise, if you jump off a building 30 feet tall, the damage done to you will be far more than that resulting from jumping three feet ten times.

In fact, as Taleb points out, you most likely will be dead. If you were to chart this as a time series you would see a feature called concavity, as shown in the chart below. The chart indicates that as the variable in question "x" increases, you experience moving downward on the curve from the spot labeled "you are here" losses that increase at a faster and faster rate, producing a line that curves inward.

If you move in the opposite direction on the curve towards a lower level of the variable "x," you experience negative asymmetry, i. An investing example of this would at first glance seem to be the use of long-term Treasuries in a balanced portfolio during a recession and bear market. The worse things get as the economy worsens and the stock market collapses, the better the Treasuries do.

We will come back to discuss this example again later because it may not actually turn out to be a very good example of antifragile behavior. The theoretical depiction of an antifragile effect is shown as convexity in the second chart below; i. Under convexity antifragility , if you move in the opposite direction on the curve towards lower values of "x," you experience negative asymmetry, i. There are a number of very good examples of Black Swan events in the markets that we could use as backtests of the antifragility of assets, including those already mentioned above.

How to be in a position of antifragility and hence benefit from randomness and volatility? Simply put, By systematic trial and error. To be more specific, trial and small error. Basically what this implies is that one does not have to be intelligent, rather one has to set up an antifragile system such that one may benefit from volatility.

If there are only small errors, then there are only small costs. However if gains are large and unlimited then you are positioned properly for a black swan. This is an antifragile system that possesses a positive convexity. Same goes for financial options.

Be as broad as you can with your strategy so you can benefit from the volatility and place yourself in a state of antifragility which will in turn, allow you to benefit from a black swan event. Lets bring in a little history. This saying was formed as a result of everyone believing there were only white swans because they never saw a black one.

Things however changed, when they discovered there were black swans in Australia. With that in mind we can then go on to the definition of what a black swan event is. Look at the three requirements and ponder why. Here we run into a problem: How can we predict the future which is infinite and unknown with something finite and known historical data? Okay, so there is a black swan.. How about a grey swan? What is the difference between them? Implications of black swan blindness There is a fairly long list of implications that result from our inevitable black swan blindness — and they are: the error of confirmation the narrative fallacy stories stick, statistics do not we are not programmed for black swans Tunneling we only go to what we know, not what we dont know mediocristan vs extremistan for mediocristan: when your sample size is large, no single instance will significantly change the aggregate or the total; for extremistan eg.

This is called the convex combination. Might not be always possible. Instead of using insurance companies, you can also use a stop-loss strategy to simulate this. This strategy is also a convex one. Unlimited gain. Minimized risk. Yet those unseen consequences..