Idiosyncrasies and challenges of data driven learning in electronic trading
arXiv:1811.09549
Abstract
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.
arXiv commentAccepted for NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy
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Cited by in corpus (3)
- CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets
- Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
- Learning Agents in Black-Scholes Financial Markets: Consensus Dynamics and Volatility Smiles