Analyzing the investment behavior in the Iranian stock exchange during the COVID-19 pandemic using hybrid DEA and data mining techniques
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Autor:innen
Sarfaraz, Amir Homayoun
Yazdi, Amir Karbassi
Gizem, Özaydin
Khalili-Damghani, Kaveh
Husseinagha, Saiedeh Molla
Autor:in (Körperschaft)
Publikationsdatum
2022
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Mathematical Problems in Engineering
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Zusammenfassung
The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
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Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1563-5147
1024-123X
1024-123X
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
SARFARAZ, Amir Homayoun, Amir Karbassi YAZDI, Thomas HANNE, Özaydin GIZEM, Kaveh KHALILI-DAMGHANI und Saiedeh Molla HUSSEINAGHA, 2022. Analyzing the investment behavior in the Iranian stock exchange during the COVID-19 pandemic using hybrid DEA and data mining techniques. Mathematical Problems in Engineering. 2022. DOI 10.1155/2022/1667618. Verfügbar unter: https://doi.org/10.26041/fhnw-7282