001447206 000__ 03532cam\a2200553Ii\4500
001447206 001__ 1447206
001447206 003__ OCoLC
001447206 005__ 20230310004107.0
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001447206 007__ cr\un\nnnunnun
001447206 008__ 220604s2022\\\\sz\a\\\\ob\\\\000\0\eng\d
001447206 019__ $$a1323252229
001447206 020__ $$a9783030973193$$q(electronic bk.)
001447206 020__ $$a3030973190$$q(electronic bk.)
001447206 020__ $$z9783030973186
001447206 020__ $$z3030973182
001447206 0247_ $$a10.1007/978-3-030-97319-3$$2doi
001447206 035__ $$aSP(OCoLC)1323245489
001447206 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dN$T$$dSFB$$dUKAHL$$dOCLCQ
001447206 049__ $$aISEA
001447206 050_4 $$aHG4515.5
001447206 08204 $$a332.640285/63$$223/eng/20220610
001447206 1001_ $$aBarrau, Thomas,$$eauthor.
001447206 24510 $$aArtificial intelligence for financial markets :$$bthe polymodel approach /$$cThomas Barrau, Raphael Douady.
001447206 264_1 $$aCham :$$bSpringer,$$c[2022]
001447206 264_4 $$c©2022
001447206 300__ $$a1 online resource :$$billustrations (some color).
001447206 336__ $$atext$$btxt$$2rdacontent
001447206 337__ $$acomputer$$bc$$2rdamedia
001447206 338__ $$aonline resource$$bcr$$2rdacarrier
001447206 4901_ $$aFinancial mathematics and FinTech
001447206 504__ $$aIncludes bibliographical references.
001447206 5050_ $$a1. Introduction -- 2. Polymodel Theory: An Overview -- 3. Estimation Method: the Linear Non-Linear Mixed Model -- 4. Predictions of Market Returns -- 5. Predictions of Industry Returns -- 6. Predictions of Specific Returns -- 7. Genetic Algorithm-Based Combination of Predictions -- 8. Conclusions -- 9. Appendix.
001447206 506__ $$aAccess limited to authorized users.
001447206 520__ $$aThis book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach. The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is described which combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.
001447206 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 10, 2022).
001447206 650_0 $$aInvestments$$xStatistical methods.
001447206 650_0 $$aArtificial intelligence$$xFinancial applications.
001447206 655_7 $$aLlibres electrònics.$$2thub
001447206 655_0 $$aElectronic books.
001447206 7001_ $$aDouady, Raphaël,$$eauthor.
001447206 77608 $$iPrint version:$$z3030973182$$z9783030973186$$w(OCoLC)1294285189
001447206 830_0 $$aFinancial mathematics and FinTech.
001447206 852__ $$bebk
001447206 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-97319-3$$zOnline Access$$91397441.1
001447206 909CO $$ooai:library.usi.edu:1447206$$pGLOBAL_SET
001447206 980__ $$aBIB
001447206 980__ $$aEBOOK
001447206 982__ $$aEbook
001447206 983__ $$aOnline
001447206 994__ $$a92$$bISE