03478nam 22005655 450 991057405200332120251202162147.03-030-97319-010.1007/978-3-030-97319-3(MiAaPQ)EBC7007392(Au-PeEL)EBL7007392(CKB)23114200100041EBL7007392(AU-PeEL)EBL7007392(PPN)269148477(BIP)84392719(BIP)83059771(DE-He213)978-3-030-97319-3(EXLCZ)992311420010004120220531d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Intelligence for Financial Markets The Polymodel Approach /by Thomas Barrau, Raphael Douady1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (182 pages)Financial Mathematics and Fintech,2662-7175Description based upon print version of record.Print version: Barrau, Thomas Artificial Intelligence for Financial Markets Cham : Springer International Publishing AG,c2022 9783030973186 1. Introduction -- 2. Polymodel Theory: An Overview -- 3. Estimation Method: the Linear Non-Linear Mixed Model -- 4. Predictions of Market Returns -- 5. Predictions of Industry Returns -- 6. Predictions of Specific Returns -- 7. Genetic Algorithm-Based Combination of Predictions -- 8. Conclusions -- 9. Appendix.This 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, 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 multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach. The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is describedwhich combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.Financial Mathematics and Fintech,2662-7175Social sciencesMathematicsMathematics in Business, Economics and FinanceSocial sciencesMathematics.Mathematics in Business, Economics and Finance.332.64028563332.6015195Barrau Thomas1237779Douady RaphaëlMiAaPQMiAaPQMiAaPQBOOK9910574052003321Artificial Intelligence for Financial Markets2873125UNINA