Alois Weigand

Personal Details

First Name:	Alois
Middle Name:
Last Name:	Weigand
Suffix:
RePEc Short-ID:	pwe538
	[This author has chosen not to make the email address public]

Affiliation

School of Finance
Universität St. Gallen

Sankt Gallen, Switzerland
http://www.unisg.ch/de/universitaet/schools/finance
RePEc:edi:cfisgch (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

Sorry, no citations of working papers recorded.

Articles

1. Nadia Balemi & Roland Füss & Alois Weigand, 2021. "COVID-19’s impact on real estate markets: review and outlook," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 495-513, December.

   Cited by:

   1. Choi, Sun-Yong, 2022. "Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
   2. Silvius STANCIU, 2022. "Post COVID-19 Lessons. Could the SARS-CoV-2 Virus be a Progress Factor? A Literature Review," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 36-46.
   3. Muskan Dugar & Trupti Dandekar Humnekar & V Moovendhan, 2024. "Analyzing Pre- and Post-Pandemic Housing Market Trends in India: A Quasi-Experimental Approach Using ITS and Panel Data Analysis," International Real Estate Review, Global Social Science Institute, vol. 27(3), pages 393-411.
   4. Zhang, Meng & Wang, Hong & Wang, Hao & Osmadi, Atasya Binti, 2024. "Digital economy, land resource mismatch, and urban housing costs: Evidence from China's digital governance policy perspective," Resources Policy, Elsevier, vol. 92(C).
   5. Víctor Manuel Cuevas Ahumada & Cuauhtémoc Calderón Villarreal, 2023. "Government policies and manufacturing production during the COVID-19 pandemic," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(4), pages 1-19, Octubre -.
   6. Eduard Hromada, 2021. "Development Of The Real Estate Market In The Czech Republic In Connection With The Covid-19 Pandemic," Proceedings of Economics and Finance Conferences 12713389, International Institute of Social and Economic Sciences.

2. Alois Weigand, 2019. "Machine learning in empirical asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 93-104, March.

   Cited by:

   1. Gan, Lirong & Wang, Huamao & Yang, Zhaojun, 2020. "Machine learning solutions to challenges in finance: An application to the pricing of financial products," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
   2. Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
   3. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
   4. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
   5. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
   6. Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi & Richard Y. D. Xu, 2022. "Time‐varying neural network for stock return prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 3-18, January.
   7. Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
   8. Steven Y. K. Wong & Jennifer Chan & Lamiae Azizi & Richard Y. D. Xu, 2020. "Time-varying neural network for stock return prediction," Papers 2003.02515, arXiv.org, revised Jan 2021.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-URE: Urban and Real Estate Economics (2) 2017-04-09 2023-12-11. Author is listed
2. NEP-ENE: Energy Economics (1) 2023-12-11. Author is listed
3. NEP-ENV: Environmental Economics (1) 2023-12-11. Author is listed
4. NEP-EUR: Microeconomic European Issues (1) 2023-12-11. Author is listed

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