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Are Market Shocks Predictable? Evidence from High-Frequency Scenarios.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Are Market Shocks Predictable? Evidence from High-Frequency Scenarios./
Author:	Sun, Jinwen.
Description:	1 online resource (103 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
Subject:	Applied mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355663860

Are Market Shocks Predictable? Evidence from High-Frequency Scenarios.
Sun, Jinwen.

Are Market Shocks Predictable? Evidence from High-Frequency Scenarios. - 1 online resource (103 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2017.

Includes bibliographical references

Exploring the possibility of market shocks forecasting is a significant topic for both academia and practice in finance. Measured by innovations generated from conventional time series models, market shocks are being assumed to follow specific distributions in the extensive literature. However, inconsistency occurs all the time in the real-world data. In this thesis, we propose and then apply a mutual information-based ARMA-GARCH-Artificial Neural Network framework to predict the direction of innovations under a high-frequency scenario. We leverage on the strength of neural networks in addressing complex pattern recognition problems. We study performances of two variable/feature selection techniques based on mutual information. Moreover, we conduct a series of comprehensive tests based on U.S. stock market high-frequency data to validate the effectiveness of our framework.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355663860Subjects--Topical Terms:

1069907
Applied mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Are Market Shocks Predictable? Evidence from High-Frequency Scenarios.
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502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2017.
504 $a Includes bibliographical references
520 $a Exploring the possibility of market shocks forecasting is a significant topic for both academia and practice in finance. Measured by innovations generated from conventional time series models, market shocks are being assumed to follow specific distributions in the extensive literature. However, inconsistency occurs all the time in the real-world data. In this thesis, we propose and then apply a mutual information-based ARMA-GARCH-Artificial Neural Network framework to predict the direction of innovations under a high-frequency scenario. We leverage on the strength of neural networks in addressing complex pattern recognition problems. We study performances of two variable/feature selection techniques based on mutual information. Moreover, we conduct a series of comprehensive tests based on U.S. stock market high-frequency data to validate the effectiveness of our framework.
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538 $a Mode of access: World Wide Web
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