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Artificial intelligence for financial marketsthe polymodel approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence for financial marketsby Thomas Barrau, Raphael Douady.
Reminder of title:	the polymodel approach /
Author:	Barrau, Thomas.
other author:	Douady, Raphael.
Published:	Cham :Springer International Publishing :2022.
Description:	xiv, 172 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	InvestmentsStatistical methods.
Online resource:	https://doi.org/10.1007/978-3-030-97319-3
ISBN:	9783030973193$q(electronic bk.)

Artificial intelligence for financial marketsthe polymodel approach /
Barrau, Thomas.

Artificial intelligence for financial marketsthe polymodel approach /[electronic resource] :by Thomas Barrau, Raphael Douady. - Cham :Springer International Publishing :2022. - xiv, 172 p. :ill. (some col.), digital ;24 cm. - Financial mathematics and FinTech,2662-7175. - Financial mathematics and FinTech..

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 described which 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.

ISBN: 9783030973193$q(electronic bk.)

Standard No.: 10.1007/978-3-030-97319-3doiSubjects--Topical Terms:

863709
Investments
--Statistical methods.

LC Class. No.: HG4515.5 / .B37 2022

Dewey Class. No.: 332.64028563

Artificial intelligence for financial marketsthe polymodel approach /
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505 0 $a 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.
520 $a 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 described which 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.
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