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Quantitative hedge funds = discretionary, systematic, AI, ESG and quantamental /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Quantitative hedge funds/ Richard D. Bateson.
Reminder of title:
discretionary, systematic, AI, ESG and quantamental /
Author:
Bateson, Richard D.
Published:
London :World Scientific Publishing Europe, : c2023.,
Description:
1 online resource (288 p.)
Subject:
Hedge funds. -
Online resource:
https://www.worldscientific.com/worldscibooks/10.1142/q0358#t=toc
ISBN:
9781800612174
Quantitative hedge funds = discretionary, systematic, AI, ESG and quantamental /
Bateson, Richard D.
Quantitative hedge funds
discretionary, systematic, AI, ESG and quantamental /[electronic resource] :Richard D. Bateson. - London :World Scientific Publishing Europe,c2023. - 1 online resource (288 p.)
Includes bibliographical references.
Preface -- About the author -- Guide to acronyms -- Glossary of notations -- Efficient markets -- Real markets -- Discretionary adventures -- Systematic profits -- The factor game -- AI again -- ESQ investing -- Towards quantamental.
"Welcome to the secretive club of modern hedge funds, where important players in the world of investing and capital markets have invested close to 4 trillion globally. If you're intrigued by the inner workings of hedge funds, investment techniques and technologies they use to source investment alpha, this book is for you. Focusing on the author's three decades of trading experience at leading banks and hedge funds, it covers both discretionary and computer-driven strategies and perspectives on AI-based and quantamental investing using new alternative data, which includes numerous examples and insights of real trades and investment strategies. No mathematical knowledge is required, with the relevant algorithms detailed in the appendices. Discretionary investing details equity and credit investing across the corporate capital structure. Through trading equities, bonds and loans, event-driven trades can target profitable special situations and relative value opportunities. Systematic trading involves computer-driven strategies derived from a scientific and statistical analysis of liquid markets. The investment strategies of both CTAs and long/short equity funds are detailed, from trend-following to factor-based approaches. AI investing is fashionable but does the reality for hedge funds correspond to the AI hype present in other non-financial domains? AI using neural nets and other machine learning techniques are outlined along with their practical application in regards to investing. Quantitative Hedge Funds also discusses environmental, social and governance (ESG) investing, which has rapidly evolved as the public and institutions demand solutions to global problems such as climate change, pollution and unethical labour practices. ESG investment strategies are migrating out of the long-only space and into hedge funds. Finally, the advent of big data has led to multiple alternative datasets available for hedge fund managers. The integration of alternative data into the investment process is discussed, together with the rise of so-called quantamental investing, a hybrid of the best of human skill and computer-based technologies"--
Mode of access: World Wide Web.
ISBN: 9781800612174Subjects--Topical Terms:
558590
Hedge funds.
LC Class. No.: HG4530 / .B34 2023
Dewey Class. No.: 332.64524
Quantitative hedge funds = discretionary, systematic, AI, ESG and quantamental /
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discretionary, systematic, AI, ESG and quantamental /
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1 online resource (288 p.)
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Includes bibliographical references.
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Preface -- About the author -- Guide to acronyms -- Glossary of notations -- Efficient markets -- Real markets -- Discretionary adventures -- Systematic profits -- The factor game -- AI again -- ESQ investing -- Towards quantamental.
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"Welcome to the secretive club of modern hedge funds, where important players in the world of investing and capital markets have invested close to 4 trillion globally. If you're intrigued by the inner workings of hedge funds, investment techniques and technologies they use to source investment alpha, this book is for you. Focusing on the author's three decades of trading experience at leading banks and hedge funds, it covers both discretionary and computer-driven strategies and perspectives on AI-based and quantamental investing using new alternative data, which includes numerous examples and insights of real trades and investment strategies. No mathematical knowledge is required, with the relevant algorithms detailed in the appendices. Discretionary investing details equity and credit investing across the corporate capital structure. Through trading equities, bonds and loans, event-driven trades can target profitable special situations and relative value opportunities. Systematic trading involves computer-driven strategies derived from a scientific and statistical analysis of liquid markets. The investment strategies of both CTAs and long/short equity funds are detailed, from trend-following to factor-based approaches. AI investing is fashionable but does the reality for hedge funds correspond to the AI hype present in other non-financial domains? AI using neural nets and other machine learning techniques are outlined along with their practical application in regards to investing. Quantitative Hedge Funds also discusses environmental, social and governance (ESG) investing, which has rapidly evolved as the public and institutions demand solutions to global problems such as climate change, pollution and unethical labour practices. ESG investment strategies are migrating out of the long-only space and into hedge funds. Finally, the advent of big data has led to multiple alternative datasets available for hedge fund managers. The integration of alternative data into the investment process is discussed, together with the rise of so-called quantamental investing, a hybrid of the best of human skill and computer-based technologies"--
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Mode of access: World Wide Web.
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System requirements: Adobe Acrobat reader.
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Description based on print version record.
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https://www.worldscientific.com/worldscibooks/10.1142/q0358#t=toc
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