Search

Your search keyword '"Su, Ruidan"' showing total 566 results
Start Over Author "Su, Ruidan"

Refine your results

more »

more »

more »

more »
566 results on '"Su, Ruidan"'

2. Searching for Textual Adversarial Examples with Learned Strategy

Author

Guo, Xiangzhe, Su, Ruidan, Tu, Shikui, Xu, Lei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tanveer, Mohammad, editor, Agarwal, Sonali, editor, Ozawa, Seiichi, editor, Ekbal, Asif, editor, and Jatowt, Adam, editor

Published
2023
Full Text
View/download PDF

27. A Deep Reinforcement Learning Approach for Portfolio Management in Non‐Short‐Selling Market.

Author

Su, Ruidan, Chi, Chun, Tu, Shikui, Xu, Lei, and Liu, Tianyuan

Subjects
REINFORCEMENT learning, DEEP reinforcement learning, PORTFOLIO management (Investments), FINANCIAL management, SHARPE ratio
Abstract

Reinforcement learning (RL) has been applied to financial portfolio management in recent years. Current studies mostly focus on profit accumulation without much consideration of risk. Some risk‐return balanced studies extract features from price and volume data only, which is highly correlated and missing representation of risk features. To tackle these problems, we propose a weight control unit (WCU) to effectively manage the position of portfolio management in different market statuses. A loss penalty term is also designed in the reward function to prevent sharp drawdown during trading. Moreover, stock spatial interrelation representing the correlation between two different stocks is captured by a graph convolution network based on fundamental data. Temporal interrelation is also captured by a temporal convolutional network based on new factors designed with price and volume data. Both spatial and temporal interrelation work for better feature extraction from historical data and also make the model more interpretable. Finally, a deep deterministic policy gradient actor–critic RL is applied to explore optimal policy in portfolio management. We conduct our approach in a challenging non‐short‐selling market, and the experiment results show that our method outperforms the state‐of‐the‐art methods in both profit and risk criteria. Specifically, with 6.72% improvement on an annualized rate of return, 7.72% decrease in maximum drawdown, and a better annualized Sharpe ratio of 0.112. Also, the loss penalty and WCU provide new aspects for future work in risk control. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

45. ULRAS: Ultra-Lightweight RFID Authentication Scheme for Mobile Device

Author

Fan, Kai, Ge, Nan, Gong, Yuanyuan, Li, Hui, Su, Ruidan, Yang, Yintang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xu, Kuai, editor, and Zhu, Haojin, editor

Published
2015
Full Text
View/download PDF

49. Design and Implementation of a Parallel Crypto Server

Author

Rong, Xiaofeng, Gao, Xiaojuan, Su, Ruidan, Zhou, Lihua, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Hao, Yue, editor, Liu, Jiming, editor, Wang, Yu-Ping, editor, Cheung, Yiu-ming, editor, Yin, Hujun, editor, Jiao, Licheng, editor, Ma, Jianfeng, editor, and Jiao, Yong-Chang, editor

Published
2005
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results