IDEAS home Printed from https://ideas.repec.org/r/eee/jfinec/v145y2022i2p64-82.html
   My bibliography  Save this item

Machine learning in the Chinese stock market

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
  2. 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.
  3. Zimeng Lyu & Amulya Saxena & Rohaan Nadeem & Hao Zhang & Travis Desell, 2024. "Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading," Papers 2410.17212, arXiv.org.
  4. 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).
  5. Jing, Zhongbo & Li, Qin & Zhao, Hongyi & Zhao, Yang, 2024. "Predicting stock price crash risk in China: A modified graph WaveNet model," Finance Research Letters, Elsevier, vol. 64(C).
  6. 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.
  7. Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024. "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper 122899, University Library of Munich, Germany.
  8. Chen, Jian & Qi, Shuyuan, 2024. "Limit-hitting exciting effects: Modeling jump dependencies in stock markets adhering to daily price-limit rules," Journal of Banking & Finance, Elsevier, vol. 163(C).
  9. 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).
  10. 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.
  11. 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).
  12. Zhao, Chencheng & Yuan, Xianghui & Long, Jun & Jin, Liwei & Guan, Bowen, 2023. "Financial indicators analysis using machine learning: Evidence from Chinese stock market," Finance Research Letters, Elsevier, vol. 58(PD).
  13. 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.
  14. 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.
  15. Caparrini, Antonio & Arroyo, Javier & Escayola Mansilla, Jordi, 2024. "S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors," Research in International Business and Finance, Elsevier, vol. 70(PA).
  16. Ma, Tian & Wang, Wanwan & Chen, Yu, 2023. "Attention is all you need: An interpretable transformer-based asset allocation approach," International Review of Financial Analysis, Elsevier, vol. 90(C).
  17. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
  18. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  19. 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.
  20. Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
  21. Li, Shicheng & Huang, Xiaoyong & Cheng, Zhonghou & Zou, Wei & Yi, Yugen, 2023. "AE-ACG: A novel deep learning-based method for stock price movement prediction," Finance Research Letters, Elsevier, vol. 58(PA).
  22. Jiahui Xi & Conghua Wen & Yifan Tang & Feifan Zhao, 2024. "A factor score clustering approach to analyze the biopharmaceutical sector in the Chinese market during COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.
  23. 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.
  24. 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.
  25. 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).
  26. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
  27. Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).
  28. Liu, LiHua & Li, YuQian, 2024. "Enterprise financialization and stock price crash risk," Finance Research Letters, Elsevier, vol. 60(C).
  29. Hanauer, Matthias X. & Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2024. "Factor models for Chinese A-shares," International Review of Financial Analysis, Elsevier, vol. 91(C).
  30. Song, Yuping & Huang, Jiefei & Zhang, Qichao & Xu, Yang, 2024. "Heterogeneity effect of positive and negative jumps on the realized volatility: Evidence from China," Economic Modelling, Elsevier, vol. 136(C).
  31. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
  32. 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.
  33. 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.
  34. Chen, Bin-xia & Sun, Yan-lin, 2024. "Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
  35. 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.
  36. 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).
  37. 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).
  38. 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.
  39. 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).
  40. Zhang, Yaojie & Song, Bingheng & He, Mengxi & Wang, Yudong, 2024. "Abnormal temperature and the cross-section of stock returns in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
  41. 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).
  42. Zhiyuan Pei & Jianqi Yan & Jin Yan & Bailing Yang & Ziyuan Li & Lin Zhang & Xin Liu & Yang Zhang, 2024. "A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images," Papers 2410.19291, arXiv.org, revised Oct 2024.
  43. 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).
  44. 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).
  45. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  46. 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).
  47. 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).
  48. Hong, Ziyang & Liu, Qingfu & Tse, Yiuman & Wang, Zilu, 2023. "Black mouth, investor attention, and stock return," International Review of Financial Analysis, Elsevier, vol. 90(C).
  49. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
  50. Rahman, Sohanur & Sinnewe, Elisabeth & Chapple, Larelle, 2024. "Environment-specific political risk discourse and expected crash risk: The role of political activism," International Review of Financial Analysis, Elsevier, vol. 95(PB).
  51. Umar, Zaghum & Zaremba, Adam & Umutlu, Mehmet & Mercik, Aleksander, 2024. "Interaction effects in the cross-section of country and industry returns," Journal of Banking & Finance, Elsevier, vol. 165(C).
  52. Yang, Hui & Ferrer, Román, 2023. "Explosive behavior in the Chinese stock market: A sectoral analysis," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
  53. 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).
  54. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
  55. 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.
  56. Guo, Minjia & Liu, Jianhe & Luo, Ziping & Han, Xiao, 2024. "Deep reinforcement learning for pairs trading: Evidence from China black series futures," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 981-993.
  57. Hanyu Zhang & Rong Ding & Hang Zhou, 2024. "Public access to in-house meeting reports and stock liquidity: evidence from China," Review of Quantitative Finance and Accounting, Springer, vol. 62(4), pages 1431-1458, May.
  58. 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.
  59. Nusret Cakici & Christian Fieberg & Daniel Metko & Adam Zaremba, 2024. "Do Anomalies Really Predict Market Returns? New Data and New Evidence," Review of Finance, European Finance Association, vol. 28(1), pages 1-44.
  60. 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).
  61. 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.
  62. 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.
  63. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
  64. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
  65. 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).
  66. Xiao, Xiang & Hua, Xia & Qin, Kexin, 2024. "A self-attention based cross-sectional return forecasting model with evidence from the Chinese market," Finance Research Letters, Elsevier, vol. 62(PA).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.