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Contemporary Empirical Asset Pricing : = Alternative Big Data and Machine Learning Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Contemporary Empirical Asset Pricing :/
Reminder of title:	Alternative Big Data and Machine Learning Models.
Author:	Moghimi, Faraz.
Description:	1 online resource (87 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Finance. -
Online resource:	click for full text (PQDT)
ISBN:	9798381377132

Contemporary Empirical Asset Pricing : = Alternative Big Data and Machine Learning Models.
Moghimi, Faraz.

Contemporary Empirical Asset Pricing :Alternative Big Data and Machine Learning Models. - 1 online resource (87 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--University of Massachusetts Boston, 2023.

Includes bibliographical references

I develop novel frameworks to predict future stock returns, using alternative big data sources, and various machine learning models. This research endeavor can be systematically described as a mixture of the following steps : 1) Study and quantify an alternative big data source that could potentially inform future returns. 2) Modify and utilize state-of-the-art machine learning approaches that could improve prior estimations or inferences. 3) Provide better interpretable and understandable conclusions of the driving forces in return predictability. Specifically, I present three related essays : (1) I examine the technical mechanics of machine learning models specifically suited for empirical asset pricing with an emphasis on loss function design and the subsequent effects on model behavior. (2) I examine whether temporal dependencies improve return predictions in a generalized framework that encompasses various factors; where I propose an interpretable long short-term memory (LSTM) framework. (3) We predict future returns by developing a novel dynamic framework to measure the proportional demand for tech-oriented skills in a firm hiring profile.

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

Mode of access: World Wide Web

ISBN: 9798381377132Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Machine learning Index Terms--Genre/Form:

554714
Electronic books.

Contemporary Empirical Asset Pricing : = Alternative Big Data and Machine Learning Models.
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500 $a Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
500 $a Advisor: Wan, Chi.
502 $a Thesis (Ph.D.)--University of Massachusetts Boston, 2023.
504 $a Includes bibliographical references
520 $a I develop novel frameworks to predict future stock returns, using alternative big data sources, and various machine learning models. This research endeavor can be systematically described as a mixture of the following steps : 1) Study and quantify an alternative big data source that could potentially inform future returns. 2) Modify and utilize state-of-the-art machine learning approaches that could improve prior estimations or inferences. 3) Provide better interpretable and understandable conclusions of the driving forces in return predictability. Specifically, I present three related essays : (1) I examine the technical mechanics of machine learning models specifically suited for empirical asset pricing with an emphasis on loss function design and the subsequent effects on model behavior. (2) I examine whether temporal dependencies improve return predictions in a generalized framework that encompasses various factors; where I propose an interpretable long short-term memory (LSTM) framework. (3) We predict future returns by developing a novel dynamic framework to measure the proportional demand for tech-oriented skills in a firm hiring profile.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Finance. $3 559073
650 4 $a Computer science. $3 573171
650 4 $a Information science. $3 561178
653 $a Machine learning
653 $a Big data
653 $a Model behavior
653 $a Hiring profile
655 7 $a Electronic books. $2 local $3 554714
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