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Studies in Computational Intelligence 898 Nguyen Ngoc Thach Vladik Kreinovich Nguyen Duc Trung Editors Data Science for Financial Econometrics Studies in Computational Intelligence Volume 898 Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland The series “Studies in Computational Intelligence” (SCI) publishes new develop- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. More information about this series at http://www.springer.com/series/7092 Nguyen Ngoc Thach Vladik Kreinovich (cid:129) (cid:129) Nguyen Duc Trung Editors Data Science for Financial Econometrics 123 Editors Nguyen NgocThach VladikKreinovich Institute for Research Science Department ofComputer Science andBankingTechnology Institute for Research Science BankingUniversity HoChiMinh City andBankingTechnology HoChiMinh, Vietnam ElPaso, TX,USA Nguyen DucTrung BankingUniversity HoChiMinh City HoChiMinh City,Vietnam ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN978-3-030-48852-9 ISBN978-3-030-48853-6 (eBook) https://doi.org/10.1007/978-3-030-48853-6 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseof illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Researchers and practitioners have been analyzing data for centuries, by using techniquesrangingfromtraditionalstatisticaltoolstomorerecentmachinelearning and decision-making methods. Until recently, however, limitations on computing abilitiesnecessitatestheuseofsimplifyingassumptionsandmodels,andprocessing datasamplesinsteadofalltheavailabledata.Inthelastdecades,asteadyprogress both in computing power and in data processing algorithms has enabled us to directly process all the data—to the extent that sometimes (e.g., in applications of deeplearning)ourdatacollectionlagsbehindourcomputingabilities.Asaresult,a new multi-disciplinary field has emerged: data science, a field that combines statistics, data analysis, machine learning, mathematics, computer science, information science, and their related methods in order to understand and analyze real-life phenomena. Data science is largely in its infancy. In many application areas, it has already achieved great successes, but there are still many application areas in which these new techniques carry a great potential. One of such areas is econometrics—qual- itativeandnumericalanalysisofeconomicphenomena.Apartofeconomicswhich isespeciallyripeforusingdatascienceisfinancialeconomics,inwhichallthedata are numerical already. This volume presents the first results and ideas of applying data science tech- niquestoeconomicphenomena—and,inparticular,financialphenomena.Allthisis still work in progress. Some papers build on the successes of the traditional methods and just hint on how a more in-depth application of new techniques can help, some use new methods more bravely, and some deal with theoretical foun- dations behind the new techniques—yet another area that still has many open questions. Overall, papers from this volume present a good picture of a working body of using data science to solve different aspects of economic problems. Thisvolumeshowswhatcanbeachieved,butevenlargeristhefuturepotential. We hope that this volume will inspire practitioners to learn how to apply various data science techniques to economic problems, and inspire researchers to further improve the existing techniques and to come up with new data science techniques for economics. v vi Preface We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments. ThepublicationofthisvolumeispartlysupportedbytheBankingUniversityofHo Chi Minh City, Vietnam. Our thanks to the leadership and staff of the Banking University,forprovidingcrucialsupport.OurspecialthankstoProf.HungT.Nguyen forhisvaluableadviceandconstantsupport. We would also like to thank Prof. Janusz Kacprzyk (Series Editor) and Dr. Thomas Ditzinger (Senior Editor, Engineering/Applied Sciences) for their support and cooperation in this publication. Ho Chi Minh, Vietnam Nguyen Ngoc Thach El Paso, USA Vladik Kreinovich Ho Chi Minh, Vietnam Nguyen Duc Trung January 2020 Contents Theoretical Research A Theory-Based Lasso for Time-Series Data . . . . . . . . . . . . . . . . . . . . . 3 AchimAhrens,ChristopherAitken,JanDitzen,ErkalErsoy,DavidKohns, and Mark E. Schaffer Why LASSO, EN, and CLOT: Invariance-Based Explanation. . . . . . . . 37 Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich, and Chon Van Le Composition of Quantum Operations and Their Fixed Points . . . . . . . . 51 Umar Batsari Yusuf, Parin Chaipunya, Poom Kumam, and Sikarin Yoo-Kong Information Quality: The Contribution of Fuzzy Methods. . . . . . . . . . . 67 Bernadette Bouchon-Meunier Parameter-Centric Analysis Grossly Exaggerates Certainty. . . . . . . . . . 81 William M. Briggs Three Approaches to the Comparison of Random Variables . . . . . . . . . 93 Bernard De Baets A QP Framework: A Contextual Representation of Agents’ Preferences in Investment Choice. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Polina Khrennikova and Emmanuel Haven HowtoMakeaDecisionBasedontheMinimumBayesFactor(MBF): Explanation of the Jeffreys Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Olga Kosheleva, Vladik Kreinovich, Nguyen Duc Trung, and Kittawit Autchariyapanitkul An Invitation to Quantum Probability Calculus. . . . . . . . . . . . . . . . . . . 121 Hung T. Nguyen, Nguyen Duc Trung, and Nguyen Ngoc Thach vii viii Contents Extending the A Priori Procedure (APP) to Address Correlation Coefficients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Cong Wang, Tonghui Wang, David Trafimow, Hui Li, Liqun Hu, and Abigail Rodriguez Variable Selection and Estimation in Kink Regression Model . . . . . . . . 151 Woraphon Yamaka Practical Applications Performance of Microfinance Institutions in Vietnam . . . . . . . . . . . . . . 167 Nguyen Ngoc Tan and Le Hoang Anh Factors Influencing on University Reputation: Model Selection by AIC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Bui Huy Khoi Impacts of Internal and External Macroeconomic Factors on Firm StockPriceinanExpansion Econometricmodel—ACaseinVietnam Real Estate Industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Dinh Tran Ngoc Huy, Vo Kim Nhan, Nguyen Thi Ngoc Bich, Nguyen Thi Phuong Hong, Nham Thanh Chung, and Pham Quang Huy How Values Influence Economic Progress? Evidence from South and Southeast Asian Countries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Nguyen Ngoc Thach Assessing the Determinants of Interest Rate Transmission in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Nhan T. Nguyen, Yen H. Vu, and Oanh T. K. Vu ApplyingLassoLinearRegressionModelinForecastingHoChiMinh City’s Public Investment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Nguyen Ngoc Thach, Le Hoang Anh, and Hoang Nguyen Khai Markov Switching Quantile Regression with Unknown Quantile ¿¿ Using a Generalized Class of Skewed Distributions: Evidence from the U.S. Technology Stock Market . . . . . . . . . . . . . . . . . . . . . . . . 255 Woraphon Yamaka and Pichayakone Rakpho Investment Behavior, Financial Constraints and Monetary Policy – Empirical Study on Vietnam Stock Exchange. . . . . . . . . . . . . . 267 Dinh Thi Thu Ha, Hoang Thi Phuong Anh, and Dinh Thi Thu Hien Non-interest Income and Competition: The Case of Vietnamese Commercial Banks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Nguyen Ngoc Thach, T. N. Nguyen Diep, and V. Doan Hung Contents ix Reconsidering Hofstede’s Cultural Dimensions: A Different View on South and Southeast Asian Countries . . . . . . . . . . . . . . . . . . . . . . . . 291 Nguyen Ngoc Thach, Tran Hoang Ngan, Nguyen Tran Xuan Linh, and Ong Van Nam Risk, Return, and Portfolio Optimization for Various Industries Based on Mixed Copula Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Sukrit Thongkairat and Woraphon Yamaka Herding Behavior Existence in MSCI Far East Ex Japan Index: A Markov Switching Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Woraphon Yamaka, Rungrapee Phadkantha, and Paravee Maneejuk Recovering from the Recession: A Bayesian Change-Point Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Nguyen Ngoc Thach Ownership Structure and Firm Performance: Empirical Study in Vietnamese Stock Exchange. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Tran Thi Kim Oanh, Dinh Thi Thu Hien, Hoang Thi Phuong Anh, and Dinh Thi Thu Ha Support Vector Machine-Based GARCH-type Models: Evidence from ASEAN-5 Stock Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Woraphon Yamaka, Wilawan Srichaikul, and Paravee Maneejuk Macroeconomic Determinants of Trade Openness: Empirical Investigation of Low, Middle and High-Income Countries. . . . . . . . . . . 383 Wiranya Puntoon, Jirawan Suwannajak, and Woraphon Yamaka Determinants of Bank Profitability in Vietnam: An Empirical Lasso Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Van Dung Ha and Hai Nam Pham Financial Performance and Organizational Downsizing: Evidence from Smes in Vietnam. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Van Dung Ha and Thi Hoang Yen Nguyen A New Hybrid Iterative Method for Solving a Mixed Equilibrium Problem and a Fixed Point Problem for Quasi-Bregman Strictly Pseudocontractive Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Kanikar Muangchoo, Poom Kumam, Yeol Je Cho, and Sakulbuth Ekvittayaniphon Copula-Based Stochastic Frontier Quantile Model with Unknown Quantile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445 Paravee Maneejuk and Woraphon Yamaka