國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Contemporary Empirical Asset Pricing :/
其他題名:	Alternative Big Data and Machine Learning Models.
作者:	Moghimi, Faraz.
面頁冊數:	1 online resource (87 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
標題:	Finance. -
電子資源:	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.
LDR:02512ntm a22003857 4500 001 1142115
005 20240414211949.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381377132
035 $a (MiAaPQ)AAI30696377
035 $a AAI30696377
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Moghimi, Faraz. $3 1466310
245 1 0 $a Contemporary Empirical Asset Pricing : $b Alternative Big Data and Machine Learning Models.
264 0 $c 2023
300 $a 1 online resource (87 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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
690 $a 0508
690 $a 0984
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Massachusetts Boston. $b Business Administration (PhD). $3 1191881
773 0 $t Dissertations Abstracts International $g 85-07B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30696377 $z click for full text (PQDT)