25 citations · 50 across the 5 of their papers we have counts for
5 papers
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
Shuo Yu, Hongyan Xue, Xiang Ao +4
In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha facto…
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
Dapeng Li, Feiyang Pan, Jia He +3
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable. For example, vo…
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
Feiyang Pan, Tongzhe Zhang, Ling Luo +2
Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-spli…
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
Feiyang Pan, Jia He, Dandan Tu +1
It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model err…
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning
Changying Du, Jia He, Changde Du +3
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices…