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Machine learning in the Chinese stock market

Citations

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Cited by:

  1. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
  2. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
  3. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
  4. Li, Zhuo & Wen, Fenghua & Huang, Zhijian James, 2023. "Asymmetric response to earnings news across different sentiment states: The role of cognitive dissonance," Journal of Corporate Finance, Elsevier, vol. 78(C).
  5. Jiawei Wang & Zhen Chen, 2023. "Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
  6. Lyu, Yongjian & Yi, Heling & Cao, Jin & Yang, Mo, 2022. "Time-varying monetary policy shocks and the dynamics of Chinese commodity prices," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
  7. Sudarshan Kumar & Sobhesh Kumar Agarwalla & Jayanth R. Varma & Vineet Virmani, 2023. "Harvesting the volatility smile in a large emerging market: A Dynamic Nelson–Siegel approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1615-1644, November.
  8. Shirui Wang & Tianyang Zhang, 2024. "Predictability of commodity futures returns with machine learning models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 302-322, February.
  9. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  10. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
  11. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
  12. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
  13. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
  14. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
  15. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
  16. Hao Li & Zhisheng Li, 2022. "The effect of daily price limits on stock liquidity: Evidence from the Chinese stock market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(5), pages 4885-4917, December.
  17. Gong, Xingyue & Jia, Guozhu, 2023. "Impactful messaging: Elite sentiment in Chinese new energy vehicle vs machine learning perspective," Finance Research Letters, Elsevier, vol. 57(C).
  18. Corneli, Flavia & Ferriani, Fabrizio & Gazzani, Andrea, 2023. "Macroeconomic news, the financial cycle and the commodity cycle: The Chinese footprint," Economics Letters, Elsevier, vol. 231(C).
  19. Liu, Xiaoqun & Zhang, Yuchen & Tian, Mengqiao & Chao, Youcong, 2023. "Financial distress and jump tail risk: Evidence from China's listed companies," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 316-336.
  20. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
  21. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
  22. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
  23. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
  24. Yuan, Yuan & Hu, May & Cheng, Chen, 2023. "CEO succession and corporate innovation: A managerial myopic perspective," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
  25. Huang, Chuangxia & Cai, Yaqian & Yang, Xiaoguang & Deng, Yanchen & Yang, Xin, 2023. "Laplacian-energy-like measure: Does it improve the Cross-Sectional Absolute Deviation herding model?," Economic Modelling, Elsevier, vol. 127(C).
  26. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
  27. Yang, Hui & Ferrer, Román, 2023. "Explosive behavior in the Chinese stock market: A sectoral analysis," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
  28. Liu, Chao & Wang, FeiFei & Xue, Wenjun, 2023. "The annual report tone and return Comovement—Evidence from China's stock market," International Review of Financial Analysis, Elsevier, vol. 88(C).
  29. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
  30. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
  31. Jun Liu & Kai Wu & Fuwei Jiang & Zhiqi Shen, 2023. "How is illiquidity priced in the Chinese stock market?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(S1), pages 1285-1320, April.
  32. Liu, Feng & Long, Xiao & Dong, Lin & Fang, Mingjie, 2023. "What makes you entrepreneurial? Using machine learning to investigate the determinants of entrepreneurship in China," China Economic Review, Elsevier, vol. 81(C).
  33. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
  34. Yujia Hu, 2023. "A Heuristic Approach to Forecasting and Selection of a Portfolio with Extra High Dimensions," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
  35. Liu, Chang & Sun, Peng & Zhu, Dongming, 2023. "Lottery preference, short-sale constraint, and the salience effect: Evidence from China," Economic Modelling, Elsevier, vol. 125(C).
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