23 citations · 86 across the 27 of their papers we have counts for
27 papers
TADACap: Time-series Adaptive Domain-Aware Captioning
Elizabeth Fons, Rachneet Kaur, Zhen Zeng +4
While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. E…
Empirical Equilibria in Agent-based Economic systems with Learning agents
Kshama Dwarakanath, Svitlana Vyetrenko, Tucker Balch
We present an agent-based simulator for economic systems with heterogeneous households, firms, central bank, and government agents. These agents interact to define production, cons…
Tax Credits and Household Behavior: The Roles of Myopic Decision-Making and Liquidity in a Simulated Economy
Kshama Dwarakanath, Jialin Dong, Svitlana Vyetrenko
There has been a growing interest in multi-agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL)…
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Elizabeth Fons, Rachneet Kaur, Soham Palande +4
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, clima…
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?
Andrea Coletta, Kshama Dwarakanath, Penghang Liu +2
Modeling subrational agents, such as humans or economic households, is inherently challenging due to the difficulty in calibrating reinforcement learning models or collecting data…
ABIDES-Economist: Agent-Based Simulator of Economic Systems with Learning Agents
Kshama Dwarakanath, Tucker Balch, Svitlana Vyetrenko
We present ABIDES-Economist, an agent-based simulator for economic systems that includes heterogeneous households, firms, a central bank, and a government. Agent behavior can be de…
Transparency as Delayed Observability in Multi-Agent Systems
Kshama Dwarakanath, Svitlana Vyetrenko, Toks Oyebode +1
Is transparency always beneficial in complex systems such as traffic networks and stock markets? How is transparency defined in multi-agent systems, and what is its optimal degree…
Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience
Haibei Zhu, Svitlana Vyetrenko, Serafin Grundl +3
We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to pre…
Analyzing the Impact of Tax Credits on Households in Simulated Economic Systems with Learning Agents
Jialin Dong, Kshama Dwarakanath, Svitlana Vyetrenko
In economic modeling, there has been an increasing investigation into multi-agent simulators. Nevertheless, state-of-the-art studies establish the model based on reinforcement lear…
A Model-Based Synthetic Stock Price Time Series Generation Framework
Haibei Zhu, Svitlana Vyetrenko, Tucker Balch
The Ornstein-Uhlenbeck (OU) process, a mean-reverting stochastic process, has been widely applied as a time series model in various domains. This paper describes the design and imp…
LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
Matteo Prata, Giuseppe Masi, Leonardo Berti +6
The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL mod…
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Tom Bamford, Andrea Coletta, Elizabeth Fons +4
Financial firms commonly process and store billions of time-series data, generated continuously and at a high frequency. To support efficient data storage and retrieval, specialize…
INTAGS: Interactive Agent-Guided Simulation
Song Wei, Andrea Coletta, Svitlana Vyetrenko +1
In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to pro…
On the Constrained Time-Series Generation Problem
Andrea Coletta, Sriram Gopalakrishan, Daniel Borrajo +1
Synthetic time series are often used in practical applications to augment the historical time series dataset for better performance of machine learning algorithms, amplify the occu…
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Andrea Coletta, Joseph Jerome, Rahul Savani +1
Limit order books are a fundamental and widespread market mechanism. This paper investigates the use of conditional generative models for order book simulation. For developing a tr…
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Andrea Coletta, Svitlana Vyetrenko, Tucker Balch
Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with…
Optimal Stopping with Gaussian Processes
Kshama Dwarakanath, Danial Dervovic, Peyman Tavallali +2
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that str…
Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets
Penghang Liu, Kshama Dwarakanath, Svitlana S Vyetrenko +1
Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological bias…
Learning to simulate realistic limit order book markets from data as a World Agent
Andrea Coletta, Aymeric Moulin, Svitlana Vyetrenko +1
Multi-agent market simulators usually require careful calibration to emulate real markets, which includes the number and the type of agents. Poorly calibrated simulators can lead t…
Equitable Marketplace Mechanism Design
Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch
We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilita…
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…
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…