科研

Gao, X., Gao, W., Ni, H. (2026). Expected equity costs and corporate cash policy: The role of information transparency. Economic Modelling, 163, 107750. https://doi.org/10.1016/j.econmod.2026.107750

Ni, H., Guo, X., Wang, W., Chen, J. (2026). Global geopolitical risk, ambiguity, and emerging market returns: Evidence from China. Finance Research Letters, 102, 110047. https://doi.org/10.1016/j.frl.2026.110047

Ni, H., He, J.H., Wang, W.J. (2026). Can network centrality explain mutual fund alpha: Evidence from China. International Review of Financial Analysis, 110, 104988. https://doi.org/10.1016/j.irfa.2025.104988

Wang, W., Ni, H. (2025). Financial connectedness in the digital age: The impact of regional FinTech development. Economic Modelling, 125, 107290. https://doi.org/10.1016/j.econmod.2025.107290

Ma, Y., Zhou, M., Ni, H. (2025). Enhancing Market Predictability and Investment Decision-Making: Machine Learning Models for Predicting Stock Market Crashes in China. International Journal of Finance and Economics. https://doi.org/10.1002/ijfe.70091

Ni, H., Zhang, Q., Guo, X., Mirza, S. (2024). Portfolio optimization by enhanced LinUCB. Finance Research Letters, 70, 106266. https://doi.org/10.1016/j.frl.2024.106266

Xu, W., Sun, Z., Ni, H. (2024). Transparency pays: How carbon emission disclosure lowers cost of capital. Economic Analysis and Policy, 83, 165-177. https://doi.org/10.1016/j.eap.2024.05.020

Yao, L., Ni, H. (2023). Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach. Scientometrics, 128, 4933-4969. https://doi.org/10.1007/s11192-023-04736-z

Wang, Y.Q., Ni, H., Uryasev, S. (2023). Buffered-ranking intervals for virtual profit efficiency analysis. Central European Journal of Operations Research, 31, 1149-1181. https://doi.org/10.1007/s10100-023-00847-3

Ni, H., Xu, H., Ma, D., Fan, J. (2023). Contextual combinatorial bandit on portfolio management. Expert Systems with Applications, 221, 119677. https://doi.org/10.1016/j.eswa.2023.119677

Zhang, Q., Ni, H., Xu, H. (2023). Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms. Economic Modelling, 122, 106204. https://doi.org/10.1016/j.econmod.2023.106204

Zhang, Q., Ni, H., Xu, H. (2022). Forecasting models for the Chinese macroeconomy in a data rich environment: Evidence from large dimensional approximate factor models with mixed frequency data. Accounting & Finance. https://doi.org/10.1111/acfi.13003

Ni, H., Wang, Y.Q., Jiang, T., Chen, Z.Y. (2022). Self-Adaptive bagging approach to credit rating. Technological Forecasting and Social Change, 175, 121371. https://doi.org/10.1016/j.techfore.2021.121371

Ni, H., Wang, Y.Q., Xu, B.Y. (2020). Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation. Intelligent Automation & Soft Computing, 26(2), 227-236. https://doi.org/10.31209/2019.100000068

Fu, P., Zhu, A., Ni, H., Zhao, X., Li, X. (2018). Threshold behaviors of social dynamics and financial outcomes of ponzi scheme diffusion in complex networks. Physica A, 490, 632-642. https://doi.org/10.1016/j.physa.2017.08.148

Ni, H., Luan, T., Cao, Y., Finlay, D. (2014). Can venture capital trigger innovation? New evidence from China. International Journal of Technology Management, 65, 189-214. https://doi.org/10.1504/IJTM.2014.060957

Wang, Y.Q., Ni, H. (2013). Multiple-v support vector regression based on spectral risk measure minimization. Neurocomputing, 101, 217-228. https://doi.org/10.1016/j.neucom.2012.09.002

Ni, H. (2013). Stock index tracking by Pareto efficient genetic algorithm. Applied Soft Computing, 13, 4519-4535. https://doi.org/10.1016/j.asoc.2013.08.012

倪禾 (2013). 基于启发式遗传算法的指数追踪组合构建策略. 系统工程理论与实践, 33(10), 2645-2653. https://doi.org/10.12011/1000-6788(2013)10-2645

Xia, F., Jiang, M., Ni, H. (2013). Commodity Futures Price, CPI and Investor Psychology. China Economic Press. ISBN: 978-7-5136-2574-6.

Wang, Y.Q., Ni, H. (2012). Multivariate convex support vector regression with semidefinite programming. Knowledge-Based Systems, 30, 87-94. https://doi.org/10.1016/j.knosys.2011.12.010

Wang, Y.Q., Ni, H. (2012). Nonparametric bivariate copula estimation based on shape-restricted support vector regression. Knowledge-Based Systems, 35, 235-244. https://doi.org/10.1016/j.knosys.2012.05.004

倪禾 (2010). 一种自组织混合模型在汇率波动性预测中的应用. 控制理论与应用, 27(4), 444-450. https://doi.org/10.7641/j.issn.1000-8152.2010.4.CCTA090225

Ni, H. (2010). Consumer Credit Risk Evaluation by Logistic Regression with Self-Organizing Map. Proceedings of the International Conference on Natural Computation, 205-249. https://doi.org/10.1109/ICNC.2010.5582917

Ni, H., Yin, H. (2009). Exchange rate prediction using a hybrid neural networks and trading indicators. Neurocomputing, 72, 2815-2823. https://doi.org/10.1016/j.neucom.2008.09.023

Ni, H., Yin, H. (2009). A Self-Organising Mixture Autoregressive Network for FX Time Series Modelling and Prediction. Neurocomputing, 72, 3529-3537. https://doi.org/10.1016/j.neucom.2009.03.019

Ni, H. (2009). Profitability of technical chart pattern trading on FX rates: Analyzed by wavelet transform. Proceedings of the 3rd International Conference on Intelligent Information Technology Application, 138-141. https://doi.org/10.1109/IITA.2009.290

Ni, H. (2009). A Fast Self-Organizing Map Algorithm by Using Genetic Selection. Proceedings of the 3rd International Conference on Intelligent Information Technology Application, 142-145. https://doi.org/10.1109/IITA.2009.291

Ni, H. (2009). Topology regressive distributed model for financial time series prediction. Proceedings of the 5th International Conference on Natural Computation, 463-467. https://doi.org/10.1109/ICNC.2009.619

Yin, H., Ni, H. (2009). Generalized Self-Organizing Mixture Autoregressive Model for Modeling Financial Time Series. 19th International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 577-586. https://doi.org/10.1007/978-3-642-04274-4_60

Yin, H., Ni, H. (2009). Generalized Self-Organizing Mixture Autoregressive Model. 7th International Workshop on Self-Organizing Maps, Lecture Notes in Computer Science, 353-361. https://doi.org/10.1007/978-3-642-02397-2_40

Ni, H., Yin, H. (2008). Self-organising mixture autoregressive model for non-stationary time series modelling. International Journal of Neural Systems, 18, 1-12. https://doi.org/10.1142/S0129065708001737

Ni, H., Yin, H. (2007). Time-Series Prediction Using Self-Organising Mixture Autoregressive Network. Intelligent Data Engineering and Automated Learning, 8th International Conference, 1000-1009. https://doi.org/10.1007/978-3-540-77226-2_100

Ni, H., Yin, H. (2006). Recurrent Self-Organising Maps and Local Support Vector Machine Models for Exchange Rate Prediction. Lecture Notes in Computer Science, 3973, 504-511. https://doi.org/10.1007/11760191_74