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Data Science for Financial Econometrics

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data Science for Financial Econometrics/ edited by Nguyen Ngoc Thach, Vladik Kreinovich, Nguyen Duc Trung.
other author:	Ngoc Thach, Nguyen.
Description:	X, 633 p. 91 illus., 71 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Engineering—Data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-48853-6
ISBN:	9783030488536

Data Science for Financial Econometrics
Data Science for Financial Econometrics[electronic resource] /edited by Nguyen Ngoc Thach, Vladik Kreinovich, Nguyen Duc Trung. - 1st ed. 2021. - X, 633 p. 91 illus., 71 illus. in color.online resource. - Studies in Computational Intelligence,8981860-9503 ;. - Studies in Computational Intelligence,564.

A Theory-based Lasso for Time-Series Data -- Invariance-Based Explanation -- Composition of Quantum Operations and Their Fixed Points -- Information quality: the contribution of fuzzy methods -- Parameter-Centric Analysis Grossly Exaggerates Certainty -- Three Approaches to the Comparison of Random Variables -- A QP framework: a contextual representation of agents' preferences in investment choice -- How to Make a Decision Based on the Minimum Bayes Factor (MBF): Explanation of the Jeffreys Scale -- Extending the A Priori Procedure (APP) to Address Correlation Coefficients -- Variable Selection and Estimation in Kink Regression Model -- Performance of microfinance institutions in Vietnam -- Factors Influencing on University Reputation in Viet Nam: Model Selection by AIC -- Impacts of Internal and External Macro Factors on Firm Stock Price in an Expansion Econometric Model – A Case in Vietnam Real Estate Industry -- How Values Influence Economic Progress? An Evidence from South And Southeast Asian Countries -- The Effect of Governance Characteristics on Firm Performance: Evidence from Vietnam -- Does Capital Affect Bank Risk in Vietnam: A Bayesian Approach.

This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. .

ISBN: 9783030488536

Standard No.: 10.1007/978-3-030-48853-6doiSubjects--Topical Terms:

1297966
Engineering—Data processing.

LC Class. No.: TA345-345.5

Dewey Class. No.: 620.00285

Data Science for Financial Econometrics
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520 $a This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. .
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