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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Contemporary Empirical Asset Pricing: Alternative Big Data and Machine Learning Models.
Author:	Moghimi, Faraz.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	87 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Notes:	Advisor: Wan, Chi.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Finance.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30696377
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 87 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798381377132Subjects--Topical Terms:

183252
Finance.
Subjects--Index Terms:

Machine learning

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