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Nonlinear investing = a quantamental...

Ma, Lingjie.

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Nonlinear investing = a quantamental approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear investing/ by Lingjie Ma.
Reminder of title:	a quantamental approach /
Author:	Ma, Lingjie.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvii, 340 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction -- Chapter 2 Quantamental Analysis -- Chapter 3 Nonlinear Factor Effects on Returns -- Chapter 4 Nonlinear Alpha Modeling -- Chapter 5 Tail Portfolios -- Chapter 6 Nonlinear Investing: Japan Stock Selection Strategy -- Chapter 7 Nonlinear Investing: Currency -- Chapter 9 Nonlinear Investing: Commodity -- Index.
Contained By:	Springer Nature eBook
Subject:	Investments. -
Online resource:	https://doi.org/10.1007/978-3-031-76305-2
ISBN:	9783031763052

Nonlinear investing = a quantamental approach /
Ma, Lingjie.

Nonlinear investinga quantamental approach /[electronic resource] :by Lingjie Ma. - Cham :Springer Nature Switzerland :2025. - xvii, 340 p. :ill., digital ;24 cm.

Chapter 1 Introduction -- Chapter 2 Quantamental Analysis -- Chapter 3 Nonlinear Factor Effects on Returns -- Chapter 4 Nonlinear Alpha Modeling -- Chapter 5 Tail Portfolios -- Chapter 6 Nonlinear Investing: Japan Stock Selection Strategy -- Chapter 7 Nonlinear Investing: Currency -- Chapter 9 Nonlinear Investing: Commodity -- Index.

This book focuses on nonlinear investing with a quantamental approach. Pricing relationships in financial markets are often nonlinear, which raises serious questions for portfolio management: How can we characterize nonlinear patterns in asset pricing? Why do such nonlinear patterns occur and in what contexts? How can we know whether such relationships will persist in the future? And how much is the value added by a nonlinear over a linear model? These questions cannot be answered by piecing together fundamental prospects based on personal experience and preference, which can be biased, or by torturing the data to make it confess whatever we want (particularly big data, which allows more freedom for data mining). Rather, nonlinear investing should rely on both fundamental insights and quantitative analysis: the former ensures that similar nonlinear patterns will occur in the future and the latter validates the nonlinear pattern with historical data. In this way, quant marries fundamental: a quantamental approach! The book provides a systematic guide to conducting nonlinear investing through quantamental analysis. The author demonstrates how nonlinear investment strategies, achieving both depth and breadth, add significant value to portfolio performance for different asset classes. The primary audience for this book is senior professional investors and quant/fundamental investment shops who look for new ideas to enhance their existing products or develop new products. The book will also be helpful to finance faculty and graduate students who are interested in frontier industry practices.

ISBN: 9783031763052

Standard No.: 10.1007/978-3-031-76305-2doiSubjects--Topical Terms:

566987
Investments.

LC Class. No.: HG4521

Dewey Class. No.: 332.6

Nonlinear investing = a quantamental approach /
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505 0 $a Chapter 1 Introduction -- Chapter 2 Quantamental Analysis -- Chapter 3 Nonlinear Factor Effects on Returns -- Chapter 4 Nonlinear Alpha Modeling -- Chapter 5 Tail Portfolios -- Chapter 6 Nonlinear Investing: Japan Stock Selection Strategy -- Chapter 7 Nonlinear Investing: Currency -- Chapter 9 Nonlinear Investing: Commodity -- Index.
520 $a This book focuses on nonlinear investing with a quantamental approach. Pricing relationships in financial markets are often nonlinear, which raises serious questions for portfolio management: How can we characterize nonlinear patterns in asset pricing? Why do such nonlinear patterns occur and in what contexts? How can we know whether such relationships will persist in the future? And how much is the value added by a nonlinear over a linear model? These questions cannot be answered by piecing together fundamental prospects based on personal experience and preference, which can be biased, or by torturing the data to make it confess whatever we want (particularly big data, which allows more freedom for data mining). Rather, nonlinear investing should rely on both fundamental insights and quantitative analysis: the former ensures that similar nonlinear patterns will occur in the future and the latter validates the nonlinear pattern with historical data. In this way, quant marries fundamental: a quantamental approach! The book provides a systematic guide to conducting nonlinear investing through quantamental analysis. The author demonstrates how nonlinear investment strategies, achieving both depth and breadth, add significant value to portfolio performance for different asset classes. The primary audience for this book is senior professional investors and quant/fundamental investment shops who look for new ideas to enhance their existing products or develop new products. The book will also be helpful to finance faculty and graduate students who are interested in frontier industry practices.
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