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I'm all for some Taleb bashing, particularly in the midst of a massive tech bull market case around AI, but I wouldn't read this as him predicting a market cras

I'm all for some Taleb bashing, particularly in the midst of a massive tech bull market case around AI, but I wouldn't read this as him predicting a market crash. The gist of his advice is generally that for the classic equity investments (ie buy the Nasdaq), your return distribution is heavily negatively skewed. You make money most of the time, and occasionally you suffer a fairly significant drawdown - ie a market crash. Nobody knows when the drawdowns will happen - that's exactly his thesis. Don't try to time the market, that's a fool's game. Structure your portfolio such that when the crash happens, you cut this convex downside. Another way to think about this is that 90% of your returns are going to be a normal distribution, but the tails become something like a pareto. The simplest thing to do is to hold convex payoff assets - ie buy puts. The devil is in the detail of knowing enough about options to determine what to buy to avoid bleeding all your profits away. This is the business model of Universa - they know what to buy, how much of it, and to a lesser degree when to buy it.

bubbleRefuge 2y ago
yeah. good points. mandlebrot wrote about this as well. that portfolio theory is based "normal" curve returns but crashes occur with much higher probability than a normal distribution would assume. he made a case for fractals. managing options is hard. when do you close the hedge after it gives you a profit? when do you put it back on.
- short_sells_poo 2y ago
  > crashes occur with much higher probability than a normal distribution would assume. This is probably the most intuitive way to reason about it, but there's a subtlety there that relates to the magnitude of the crashes and their probability of happening. Intuitively, if the normal distribution was correct in estimating market behavior, you'd not expect to see 10 sigma events ever (we are looking at something like 1/10^20 of this happening). And yet, these events happen with some regularity when you try to use normal distributions for market returns. So there are two aspects to this: one is that the normal distribution and people underestimate how often crashes can happen, and they also underestimate how big those crashes can be. It's the latter that is arguably more dangerous, because if you lose money more frequently than expected, you might end up earning less, but if you underestimate how bad a drawdown can be, you can be easily wiped out before you can react. So why do we use the normal distribution so often in finance? Because it is convenient. It works fine for 99.99% of the cases, and it is easier to deal with the tails as a special beast rather than always having a complicated model to look at. There's also an element of lottery. Significant crashes happen once every 10 years roughly, so you don't need a great deal of luck to make a lot of money without seeing one :D > managing options is hard. when do you close the hedge after it gives you a profit? when do you put it back on. It is definitely quite hard. Options are nonlinear instruments with a significant complexity to their behavior. This is why people pay Universa and other, lesser known tail risk funds to handle this complexity. You also have instruments like Variance Swaps, which allow one to easily lock in convexity pnl - considerably removing the timing aspect. You can construct these instruments synthetically using options, but this is not for the faint of heart. It is doable on your own, but not without understanding options in depth and having tools that allow you to manage an option portfolio. These days, a person who is decent at freshman math and knows how to use python+pandas can easily manage this entirely on their own with a handful of scripts, but they'll likely need to spend a few months learning the theory and then it can take a year to build the neccessary intuition.
  - bubbleRefuge 2y ago
    yeah 100% . picking up pennies in from of the train. thanks for the bits. so I should search on "Variance Swaps"? . I do a great deal of options trading and play around with some ATS software for options trading that i've developed using IB api. lot of factors and complexity as you mentioned. factors like liquidity . IV spikes, are dimensions to consider. thinking of trading high probability 0DTE spreads with portfolio hedging in place.
blablabla123 2y ago
I mean Nvidia has also so far been priced for perfection, things just continuing like this. Also considering the future value being priced in already. At this point I'm thinking AI is here to stay. But reliance on CUDA or a small set of companies probably not.

bubbleRefuge 2y ago

yeah. good points. mandlebrot wrote about this as well. that portfolio theory is based "normal" curve returns but crashes occur with much higher probability than a normal distribution would assume. he made a case for fractals. managing options is hard. when do you close the hedge after it gives you a profit? when do you put it back on.

short_sells_poo 2y ago
> crashes occur with much higher probability than a normal distribution would assume. This is probably the most intuitive way to reason about it, but there's a subtlety there that relates to the magnitude of the crashes and their probability of happening. Intuitively, if the normal distribution was correct in estimating market behavior, you'd not expect to see 10 sigma events ever (we are looking at something like 1/10^20 of this happening). And yet, these events happen with some regularity when you try to use normal distributions for market returns. So there are two aspects to this: one is that the normal distribution and people underestimate how often crashes can happen, and they also underestimate how big those crashes can be. It's the latter that is arguably more dangerous, because if you lose money more frequently than expected, you might end up earning less, but if you underestimate how bad a drawdown can be, you can be easily wiped out before you can react. So why do we use the normal distribution so often in finance? Because it is convenient. It works fine for 99.99% of the cases, and it is easier to deal with the tails as a special beast rather than always having a complicated model to look at. There's also an element of lottery. Significant crashes happen once every 10 years roughly, so you don't need a great deal of luck to make a lot of money without seeing one :D > managing options is hard. when do you close the hedge after it gives you a profit? when do you put it back on. It is definitely quite hard. Options are nonlinear instruments with a significant complexity to their behavior. This is why people pay Universa and other, lesser known tail risk funds to handle this complexity. You also have instruments like Variance Swaps, which allow one to easily lock in convexity pnl - considerably removing the timing aspect. You can construct these instruments synthetically using options, but this is not for the faint of heart. It is doable on your own, but not without understanding options in depth and having tools that allow you to manage an option portfolio. These days, a person who is decent at freshman math and knows how to use python+pandas can easily manage this entirely on their own with a handful of scripts, but they'll likely need to spend a few months learning the theory and then it can take a year to build the neccessary intuition.
- bubbleRefuge 2y ago
  yeah 100% . picking up pennies in from of the train. thanks for the bits. so I should search on "Variance Swaps"? . I do a great deal of options trading and play around with some ATS software for options trading that i've developed using IB api. lot of factors and complexity as you mentioned. factors like liquidity . IV spikes, are dimensions to consider. thinking of trading high probability 0DTE spreads with portfolio hedging in place.

blablabla123 2y ago

I mean Nvidia has also so far been priced for perfection, things just continuing like this. Also considering the future value being priced in already. At this point I'm thinking AI is here to stay. But reliance on CUDA or a small set of companies probably not.