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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書目名稱Artificial Intelligence for Financial Markets影響因子(影響力)




書目名稱Artificial Intelligence for Financial Markets影響因子(影響力)學科排名




書目名稱Artificial Intelligence for Financial Markets網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence for Financial Markets網(wǎng)絡(luò)公開度學科排名




書目名稱Artificial Intelligence for Financial Markets被引頻次




書目名稱Artificial Intelligence for Financial Markets被引頻次學科排名




書目名稱Artificial Intelligence for Financial Markets年度引用




書目名稱Artificial Intelligence for Financial Markets年度引用學科排名




書目名稱Artificial Intelligence for Financial Markets讀者反饋




書目名稱Artificial Intelligence for Financial Markets讀者反饋學科排名

口味

Polymodel Theory: An Overview, how polymodels are, in several respects, a superior alternative to classical multivariate regressions estimated with OLS, Ridge and Stepwise techniques; we also present the limits of the method. Although it is a regression technique, we clarify how the polymodels framework is closer to artificial i

心神不寧

Estimation Method: The Linear Non-Linear Mixed Model,cing overfitting. We show using numerical simulations that the LNLM model is able to successfully detect patterns in noisy data, with an accuracy similar to or better than data-driven modeling alternatives. We find that our algorithm is computationally efficient, an essential characteristic for mach

感染

temperate

Predictions of Industry Returns,ing out that the non-linearity of the link between market and industry returns is priced by market participants. It is shown to differ from other well-known industry factors, including downside beta and coskewness factors, which are entirely subsumed by the antifragility factor. A trading strategy d

LVAD360

surmount

Nebulous

Conclusions,rns about the limits of Polymodel Theory have been addressed. Returning to the different applications discussed throughout the book, we outline how polymodels successfully contribute to the literature of financial market predictions, thus providing an effective artificial intelligence technique when

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2662-7167 tical portfolio construction.Provides new explicit quantitatThis book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imit

confide

https://doi.org/10.1007/978-3-8349-9782-1d, 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.