Open Access Helper

OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Empirical asset pricing via machine learning: evidence from the European stock market
Wolfgang Drobetz, Tizian Otto
Journal of Asset Management (2021) Vol. 22, Iss. 7, pp. 507-538
Open Access | Times Cited: 38

Showing 26-50 of 38 citing articles:

Economic Theory and Machine Learning Integration in Asset Pricing and Portfolio Optimization: A Bibliometric Analysis and Conceptual Framework
Patrick Kevin Aritonang, Sudarso Kaderi Wiryono, Taufik Faturohman
Journal of Computer Science (2024) Vol. 20, Iss. 10, pp. 1291-1309
Open Access
Generative AI for European asset pricing: alleviating the momentum anomaly
Matthias Mattusch
European Journal of Finance (2024), pp. 1-39
Open Access
Predicting Corporate Bond Illiquidity via Machine Learning
Axel Cabrol, Wolfgang Drobetz, Tizian Otto, et al.
SSRN Electronic Journal (2023)
Open Access | Times Cited: 1
The study on systemic risk of rural finance based on macro–micro big data and machine learning
Wanling Zhou, Sulin Pang, Zhiliang He
Statistical Theory and Related Fields (2023) Vol. 7, Iss. 4, pp. 261-275
Open Access | Times Cited: 1
Machine Learning Versus Fundamental Investment Analysis: A Meta-Analysis
Ties Rothman
(2021)
Closed Access | Times Cited: 2
Forecasting Market Crashes via Machine Learning: Evidence from European Stock Markets
Hubert Dichtl, Wolfgang Drobetz, Tizian Otto
SSRN Electronic Journal (2021)
Closed Access | Times Cited: 1
Predicting European stock returns using machine learning
Antonio Marsi
SN Business & Economics (2023) Vol. 3, Iss. 7
Closed Access
Less is More? Reducing Biases and Overfitting in Machine Learning Return Predictions
Clint Howard
SSRN Electronic Journal (2023)
Closed Access
Relative informative power and stock return predictability: a new perspective from Egypt
Enas Hendawy, David G. McMillan, Zaki M. Sakr, et al.
Journal of financial reporting & accounting (2023)
Closed Access
Multi-Measures Stock Return Prediction Using Neural Network Models
Cong Wang
SSRN Electronic Journal (2023)
Closed Access
A Literature Study of Asset Pricing Based on Machine Learning Method
Fengtian Zhao
Advances in Economics Management and Political Sciences (2023) Vol. 34, Iss. 1, pp. 27-36
Open Access
The Determinants of Corporate Bond Returns - a Literature Review
Seyed Mohammad Mansouri, Dalibor S. Eterovic
SSRN Electronic Journal (2023)
Closed Access
Return prediction by machine learning for the Korean stock market
Wonwoo Choi, Seong‐Ho Jang, Sanghee Kim, et al.
Journal of the Korean Statistical Society (2023) Vol. 53, Iss. 1, pp. 248-280
Closed Access
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