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Found 7 papers, 1 papers with code
Learning Not to Spoof
In this article, I consider a series of experiments in which an intelligent stock trading agent maximizes profit but may also inadvertently learn to spoof the market in which it participates.
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted server.
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters.
The Importance of Low Latency to Order Book Imbalance Trading Strategies
There is a pervasive assumption that low latency access to an exchange is a key factor in the profitability of many high-frequency trading strategies.
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.
Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency
In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices.
ABIDES: Towards High-Fidelity Market Simulation for AI Research
ABIDES is designed from the ground up to support AI agent research in market applications.
Multiagent Systems
