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Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models./
作者:	Luo, Sherry.
面頁冊數:	1 online resource (143 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
Contained By:	Dissertations Abstracts International84-05A.
標題:	Forecasting techniques. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29805352click for full text (PQDT)
ISBN:	9798352977576

Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models.
Luo, Sherry.

Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models. - 1 online resource (143 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: A.

Thesis (Ph.D.)--Lancaster University (United Kingdom), 2022.

Includes bibliographical references

This thesis attempts to model and forecast realized volatility and stock market tail risk using hybrid models integrating Machine Learning algorithms with Financial Time Series models. One of the advantages of Machine Learning approaches is that it can well approximate a wide range class of linear and nonlinear functions, forming the input-output map by learning the data rather than assuming the data generating process. Traditional Time Series models, however, focus on reproducing the stylized facts of target variables through statistical modeling. By hybriding these two types of models, we find that Machine Learning approaches well complement Financial Time Series models in variable screening, complex relationship detection and nonlinearity modeling. In addition, it is found that instead of using raw data in the Machine Learning algorithms, Financial Time Series models generate more effective features that significantly improves learning ability of those algorithms.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352977576Subjects--Topical Terms:

3564845
Forecasting techniques.
Index Terms--Genre/Form:

542853
Electronic books.

Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models.
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245 1 0 $a Essays on Hybrid Modeling of Machine Learning Algorithms and Financial Time Series Models.
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300 $a 1 online resource (143 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
500 $a Advisor: Izzeldin, Marwan.
502 $a Thesis (Ph.D.)--Lancaster University (United Kingdom), 2022.
504 $a Includes bibliographical references
520 $a This thesis attempts to model and forecast realized volatility and stock market tail risk using hybrid models integrating Machine Learning algorithms with Financial Time Series models. One of the advantages of Machine Learning approaches is that it can well approximate a wide range class of linear and nonlinear functions, forming the input-output map by learning the data rather than assuming the data generating process. Traditional Time Series models, however, focus on reproducing the stylized facts of target variables through statistical modeling. By hybriding these two types of models, we find that Machine Learning approaches well complement Financial Time Series models in variable screening, complex relationship detection and nonlinearity modeling. In addition, it is found that instead of using raw data in the Machine Learning algorithms, Financial Time Series models generate more effective features that significantly improves learning ability of those algorithms.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Forecasting techniques. $3 3564845
650 4 $a Investments. $3 566987
650 4 $a Forecasting. $3 547120
650 4 $a Normal distribution. $3 3561025
650 4 $a Neural networks. $3 677449
650 4 $a Feature selection. $3 3560270
650 4 $a Time series. $3 3561811
650 4 $a Competition. $3 537031
650 4 $a Finance. $3 542899
655 7 $a Electronic books. $2 lcsh $3 542853
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710 2 $a Lancaster University (United Kingdom). $3 1294170
773 0 $t Dissertations Abstracts International $g 84-05A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29805352 $z click for full text (PQDT)