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Hedge Fund Replication with Deep Neural Networks and Generative Adversarial Networks.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Hedge Fund Replication with Deep Neural Networks and Generative Adversarial Networks./
Author:	Chatterji, Devin Mathew.
Description:	1 online resource (87 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-07, Section: B.
Contained By:	Dissertations Abstracts International84-07B.
Subject:	Finance. -
Online resource:	click for full text (PQDT)
ISBN:	9798368412764

Hedge Fund Replication with Deep Neural Networks and Generative Adversarial Networks.
Chatterji, Devin Mathew.

Hedge Fund Replication with Deep Neural Networks and Generative Adversarial Networks. - 1 online resource (87 pages)

Source: Dissertations Abstracts International, Volume: 84-07, Section: B.

Thesis (Ph.D.)--Illinois Institute of Technology, 2022.

Includes bibliographical references

Hedge fund replication is a means for allowing investors to achieve hedge fund-like returns, which are usually only available to institutions. Hedge funds in total have over $3 trillion in assets under management (AUM). More traditional money managers would like to offer hedge fund-like returns to retail investors by replicating their performance. There are two primary challenges with existing hedge fund replication methods, difficulty capturing the nonlinear and dynamic exposures of hedge funds with respect to the factors, and difficulty in identifying the right factors that reflect those exposures. It has been shown in previous research that deep neural networks (DNN) outperform other linear and machine learning models when working with financial applications. This is due to the ability of DNNs to model complex relationships, such as non-linearities and interaction effects, between input features without over-fitting. My research investigates DNNs and generative adversarial networks (GAN) in order to address the challenges of factor-based hedge fund replication. Neither of these methods have been applied to the hedge fund replication problem. My research contributes to the literature by showing that the use of these DNNs and GANs addresses the existing challenges in hedge fund replication and improves on results in the literature.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798368412764Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Artificial intelligenceIndex Terms--Genre/Form:

554714
Electronic books.

Hedge Fund Replication with Deep Neural Networks and Generative Adversarial Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 84-07, Section: B.
500 $a Advisor: Van Vliet, Benjamin.
502 $a Thesis (Ph.D.)--Illinois Institute of Technology, 2022.
504 $a Includes bibliographical references
520 $a Hedge fund replication is a means for allowing investors to achieve hedge fund-like returns, which are usually only available to institutions. Hedge funds in total have over $3 trillion in assets under management (AUM). More traditional money managers would like to offer hedge fund-like returns to retail investors by replicating their performance. There are two primary challenges with existing hedge fund replication methods, difficulty capturing the nonlinear and dynamic exposures of hedge funds with respect to the factors, and difficulty in identifying the right factors that reflect those exposures. It has been shown in previous research that deep neural networks (DNN) outperform other linear and machine learning models when working with financial applications. This is due to the ability of DNNs to model complex relationships, such as non-linearities and interaction effects, between input features without over-fitting. My research investigates DNNs and generative adversarial networks (GAN) in order to address the challenges of factor-based hedge fund replication. Neither of these methods have been applied to the hedge fund replication problem. My research contributes to the literature by showing that the use of these DNNs and GANs addresses the existing challenges in hedge fund replication and improves on results in the literature.
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538 $a Mode of access: World Wide Web
650 4 $a Finance. $3 559073
653 $a Artificial intelligence
653 $a Deep neural networks
653 $a Generative adversarial networks
653 $a Hedge fund replication
653 $a Hedge funds
653 $a Quantitative finance
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