Starting with the good (chapters 15 - 17), within chapter 15 Taleb explains where the Bell curve works and where it does not. The Bell curve captures well variables that don't deviate much from the mean. Otherwise, it does not work. Taleb suggests we often fool ourselves in believing that correlation, regression coefficients, or standard deviation convey much information. This is because those coefficients are unstable (and can flip sign when possible) depending on the time selected. This is because the underlying variables are often not stationary enough for these coefficients to be stable.
Chapter 16 is excellent as an introduction to Mandelbrot's fractal geometry as an alternative to Gaussian based investment theory. He supports well that these mathematical tools do capture randomness (of non-stationary variables) far better than the Normal distribution. However, he admits that Mandelbrotian models are not predictive. When looking at the same data set, he and numerous colleagues each came up with different underlying parameters to build fractal-like models. And a small difference in such parameters makes a huge difference in outcome. That's why you will not hear much of fractal geometry within the quantitative financial community. Nevertheless, this is a fascinating subject that deserves further exploration. For this purpose, I recommend Mandelbrot's The Misbehavior of Markets
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