Idiosyncrasies and challenges of data driven learning in electronic trading
We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
The behavior of dealers and clients on the European corporate bond market: the case of Multi-Dealer-to-Client platforms
For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditiona…
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading
Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of financial trading. …
Time SeriesTime Series AnalysisTime Series ForecastingVocal Bursts Intensity PredictionJanus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling
Financial market movements are often driven by discrete financial events conveyed through news, whose impacts are heterogeneous, abrupt, and difficult to capture under purely numerical prediction objectives. These limita…
Reinforcement LearningTrading on the Floor after Sweeping the Book
Informed traders need to trade fast in order to profit from their private information before it becomes public. Fast electronic markets provide such liquidity. Slow markets provide execution in an auction based trading f…
Decision Tree Psychological Risk Assessment in Currency Trading
This research paper focuses on the integration of Artificial Intelligence (AI) into the currency trading landscape, positing the development of personalized AI models, essentially functioning as intelligent personal assi…
Decision Making