35 citations · 225 across the 65 of their papers we have counts for
9 papers · 1 filter
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization
Yuanlu Bai, Henry Lam, Svitlana Vyetrenko +1
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine le…
Profit equitably: An investigation of market maker's impact on equitable outcomes
Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch
We look at discovering the impact of market microstructure on equitability for market participants at public exchanges such as the New York Stock Exchange or NASDAQ. Are these envi…
Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets
Mahmoud Mahfouz, Tucker Balch, Manuela Veloso +1
Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments. In this…
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets
Selim Amrouni, Aymeric Moulin, Jared Vann +3
Model-free Reinforcement Learning (RL) requires the ability to sample trajectories by taking actions in the original problem environment or a simulated version of it. Breakthroughs…
Towards Realistic Market Simulations: a Generative Adversarial Networks Approach
Andrea Coletta, Matteo Prata, Michele Conti +5
Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approa…
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators
Victor Storchan, Svitlana Vyetrenko, Tucker Balch
In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test tr…
Visual Time Series Forecasting: An Image-driven Approach
Naftali Cohen, Srijan Sood, Zhen Zeng +2
In this work, we address time-series forecasting as a computer vision task. We capture input data as an image and train a model to produce the subsequent image. This approach resul…
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Yuanlu Bai, Tucker Balch, Haoxian Chen +3
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability. To ensure accurate prediction, the model needs to be calibrated and v…
Deep Video Prediction for Time Series Forecasting
Zhen Zeng, Tucker Balch, Manuela Veloso
Time series forecasting is essential for decision making in many domains. In this work, we address the challenge of predicting prices evolution among multiple potentially interacti…