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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:

Do industry returns predict the stock market? A reprise using the random forest
Cetin Ciner
The Quarterly Review of Economics and Finance (2018) Vol. 72, pp. 152-158
Closed Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Stock index futures price prediction using feature selection and deep learning
Wan‐Lin Yan
The North American Journal of Economics and Finance (2022) Vol. 64, pp. 101867-101867
Closed Access | Times Cited: 19
Do industries predict stock market volatility? Evidence from machine learning models
Zibo Niu, Rıza Demirer, Muhammad Tahir Suleman, et al.
Journal of International Financial Markets Institutions and Money (2023) Vol. 90, pp. 101903-101903
Closed Access | Times Cited: 12
Forecasting the BRICS Stock Returns with Best Subset Regressions
Cetin Ciner
Finance research letters (2025), pp. 107038-107038
Closed Access
Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?
Sabyasachi Mohapatra, Rohan Mukherjee, Arindam Roy, et al.
Journal of risk and financial management (2022) Vol. 15, Iss. 8, pp. 350-350
Open Access | Times Cited: 16
From vineyard to table: Uncovering wine quality for sales management through machine learning
Rui Ma, Mao Di, Dongmei Cao, et al.
Journal of Business Research (2024) Vol. 176, pp. 114576-114576
Open Access | Times Cited: 3
Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China
Wei Liu, Yoshihisa Suzuki, Shuyi Du
Computational Economics (2023) Vol. 63, Iss. 5, pp. 2035-2068
Closed Access | Times Cited: 7
Machine learning for US cross-industry return predictability under information uncertainty
Haithem Awijen, Younes Ben Zaied, Béchir Ben Lahouel, et al.
Research in International Business and Finance (2023) Vol. 64, pp. 101893-101893
Open Access | Times Cited: 6
Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms
Zongxiao Wu, Cong Zang, Chia‐Huei Wu, et al.
Journal of Global Information Management (2021) Vol. 30, Iss. 3, pp. 1-23
Open Access | Times Cited: 14
Evaluation Optimal Prediction Performance of MLMs on High-volatile Financial Market Data
Hongxing Yao, Hafiz Muhammad Naveed, Muhammad Usman Answer, et al.
International Journal of Advanced Computer Science and Applications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 9
Industry volatility concentration and the predictability of aggregate stock market volatility
Mengxi He, Danyan Wen, Lü Xing, et al.
International Review of Economics & Finance (2024) Vol. 95, pp. 103488-103488
Closed Access | Times Cited: 1
Apply RF-LSTM to Predicting Future Share Price
Haoxian Nan
SHS Web of Conferences (2023) Vol. 170, pp. 02012-02012
Open Access | Times Cited: 3
Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models
Abdullah H. Alenezy, Mohd Tahir Ismail, S. Al Wadi, et al.
Risks (2023) Vol. 11, Iss. 7, pp. 121-121
Open Access | Times Cited: 3
INVESTMENT PERFORMANCE OF MACHINE LEARNING: ANALYSIS OF S&P 500 INDEX
Chia‐Cheng Chen, Chun‐Hung Chen, Ting-Yin Liu
International Journal of Economics and Financial Issues (2019) Vol. 10, Iss. 1, pp. 59-66
Open Access | Times Cited: 8
AN ANALYSIS ON INVESTMENT PERFORMANCE OF MACHINE LEARNING: AN EMPIRICAL EXAMINATION ON TAIWAN STOCK MARKET
Chia-Cheng Chen, Yi‐Sheng Liu, Ting-Hsin Hsu
International Journal of Economics and Financial Issues (2019) Vol. 9, Iss. 4, pp. 1-10
Open Access | Times Cited: 7
The asymmetric effect of air quality on cross-industries’ stock returns: evidence from China
Chi‐Wei Su, Kai‐Hua Wang, Ran Tao, et al.
Environmental Science and Pollution Research (2019) Vol. 26, Iss. 30, pp. 31422-31433
Closed Access | Times Cited: 7
Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST
Şevval TOPRAK, Gültekin Çağıl, Abdullah Hulusi Kökçam
Düzce Üniversitesi Bilim ve Teknoloji Dergisi (2023) Vol. 11, Iss. 2, pp. 958-976
Open Access | Times Cited: 2
Machine learning in finance: Major applications, issues, metrics, and future trends
Nawaf Almaskati
International Journal of Financial Engineering (2022) Vol. 09, Iss. 03
Closed Access | Times Cited: 4
Is there relationship between air quality and China’s stock market? Evidence from industrial heterogeneity
Ge Song, Zhiqing Xia, Kai‐Hua Wang, et al.
Economic Research-Ekonomska Istraživanja (2022) Vol. 36, Iss. 1, pp. 2320-2340
Open Access | Times Cited: 3
Realized Stock-Market Volatility: Do Industry Returns Have Predictive Value?
Rıza Demirer, Rangan Gupta, Christian Pierdzioch
Intelligent systems reference library (2024), pp. 377-405
Closed Access
Do industries lead the stock market? Evidence from an emerging stock market
Rıza Demirer, Aydın Yüksel
Borsa Istanbul Review (2024)
Open Access
Research on Machine Learning-based Prediction of Coffee Futures Prices
Yuduo Chen
Highlights in Science Engineering and Technology (2024) Vol. 92, pp. 199-209
Open Access
Financial regulatory policy Uncertainty: An informative predictor for financial industry stock returns
Yaojie Zhang, Xinyi Zhao, Zhikai Zhang
The North American Journal of Economics and Finance (2024), pp. 102321-102321
Closed Access
LSTM-Based Deep Model for Investment Portfolio Assessment and Analysis
Haohua Yang
Applied Bionics and Biomechanics (2022) Vol. 2022, pp. 1-6
Open Access | Times Cited: 2
Associating Fundamental Features with Technical Indicators for Analyzing Quarterly Stock Market Trends Using Machine Learning Algorithms
Moore Nicholas, Bagui Sikha
BOHR International Journal of Computer Science (2021) Vol. 1, Iss. 1, pp. 88-103
Open Access | Times Cited: 2
Evaluation of optimal economic and technical indicators for agriculture stock trading decision
Boris Kuzman, Biljana Petković, Dalibor Petković
International Journal of Economic Practice and Policy (2021) Vol. 18, Iss. 2, pp. 124-140
Open Access | Times Cited: 2
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