Titlebook: Artificial Intelligence for Financial Markets; The Polymodel Approa Thomas Barrau,Raphael Douady Book 2022 The Editor(s) (if applicable) an

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Die Abgrenzung zu anderen Vertriebsarten-known industry factors, including downside beta and coskewness factors, which are entirely subsumed by the antifragility factor. A trading strategy derived from the factor exhibits a Sharpe ratio of 1.10, and successfully resists various robustness tests.

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Introduction,d, and we explain how contributing to the model actually coincides with contributing to the literature. We finally develop the plan of the book, which answers each of the different points evocated about the literature of financial market prediction.

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Estimation Method: The Linear Non-Linear Mixed Model,lar to or better than data-driven modeling alternatives. We find that our algorithm is computationally efficient, an essential characteristic for machine learning applications often involving a large number of estimations.

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Book 2022ls is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional mul

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Predictions of Market Returns,signal, which is shown to be strongly significant, both from an economic and statistical point of view. Results are robust across different time-periods, for various sets of explanatory variables, and among 12 different stock markets.

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Predictions of Specific Returns,stness tests. Through the implementation of the trading strategy, we propose a method to tackle the problem of the aggregation of the predictions of a polymodel, based on the information added by each elementary model.