We open up the "black-box" to identify the predictive general price patterns in price chart images via the deep learning image analysis techniques. Our identified price patterns lead to the construction of image-induced importance (triple-I) weights, which are applied to weighted moving average the existing price trend trading signals according to their level of importance in predicting price movements. From an extensive empirical analysis on the Chinese stock market, we show that the triple-I weighting scheme can significantly enhance the price trend trading signals for proposing portfolios, with a thoughtful robustness study in terms of network specifications, image structures, and stock sizes. Moreover, we demonstrate that the triple-I weighting scheme is able to propose long-term portfolios from a time-scale transfer learning, enhance the news-based trading strategies through a non-technical transfer learning, and increase the overall strength of numerous trading rules for portfolio selection.
@misc{Zhu2024Enhancement,title={Enhancement of Price Trend Trading Strategies via Image-induced Importance Weights},author={Zhu, Zhoufan and Zhu, Ke},year={2024},eprint={2408.08483},archiveprefix={arXiv},primaryclass={q-fin.PM},}
JOEF
Big Portfolio Selection by Graph-based Conditional Moments Method.
@article{Zhu2024Graph,title={Big Portfolio Selection by Graph-based Conditional Moments Method.},author={Zhu, Zhoufan and Zhang, Ningning and Zhu, Ke},journal={Journal of Empirical Finance},volume={78},number={},pages={101533},year={2024},}
JBES
Asset Pricing via the Conditional Quantile Variational Autoencoder.
@article{Yang2024Asset,title={Asset Pricing via the Conditional Quantile Variational Autoencoder.},author={Yang, Xunling and Zhu, Zhoufan and Li, Dong and Zhu, Ke},journal={Journal of Business \& Economic Statistics},volume={42},number={2},pages={681-694},year={2024},publisher={Taylor & Francis},}
TNNLS
Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning
Chenjia Bai, Ting Xiao, Zhoufan Zhu, Lingxiao Wang, and 5 more authors
IEEE Transactions on Neural Networks and Learning Systems, 2024
@article{Bai2024Monotonic,author={Bai, Chenjia and Xiao, Ting and Zhu, Zhoufan and Wang, Lingxiao and Zhou, Fan and Garg, Animesh and He, Bin and Liu, Peng and Wang, Zhaoran},journal={IEEE Transactions on Neural Networks and Learning Systems},title={Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning},year={2024},volume={35},number={7},pages={8954-8968},}
2023
ICML
Variance Control for Distributional Reinforcement Learning
Qi Kuang†, Zhoufan Zhu†, Liwen Zhang, and Fan Zhou
In Proceedings of the 40th International Conference on Machine Learning, 2023
@inproceedings{Kuang2023Variance,author={Kuang, Qi and Zhu, Zhoufan and Zhang, Liwen and Zhou, Fan},title={Variance Control for Distributional Reinforcement Learning},year={2023},publisher={JMLR.org},articleno={736},numpages={22},booktitle={Proceedings of the 40th International Conference on Machine Learning},}
2022
CJS
Shrinkage Quantile Regression for Panel Data with Multiple Structural Breaks
Liwen Zhang, Zhoufan Zhu, Xingdong Feng, and Yong He
@article{Zhang2022Shrinkage,author={Zhang, Liwen and Zhu, Zhoufan and Feng, Xingdong and He, Yong},title={Shrinkage Quantile Regression for Panel Data with Multiple Structural Breaks},journal={Canadian Journal of Statistics},volume={50},number={3},pages={820-851},year={2022},}
2021
IJCAI
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning
Fan Zhou, Zhoufan Zhu, Qi Kuang, and Liwen Zhang
In Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021
@inproceedings{Zhou2021Non,author={Zhou, Fan and Zhu, Zhoufan and Kuang, Qi and Zhang, Liwen},booktitle={Proceedings of the 30th International Joint Conference on Artificial Intelligence},pages={3455-3461},title={Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning},year={2021},}