35 citations · 225 across the 65 of their papers we have counts for
69 papers
OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting
Jung Hun Phee, Tucker Balch
We study the conditional distribution of limit-order volume submitted over the next five minutes, given the current order book and recent order flow. The task requires capturing pr…
Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization
Srijan Sood, Kassiani Papasotiriou, Marius Vaiciulis +1
Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. Portfolio opti…
The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance
Eren Kurshan, Tucker Balch, David Byrd
Generative and agentic artificial intelligence is entering financial markets faster than existing governance can adapt. Current model-risk frameworks assume static, well-specified…
Is All the Information in the Price? LLM Embeddings versus the EMH in Stock Clustering
Bingyang Wang, Grant Johnson, Maria Hybinette +1
This paper investigates whether artificial intelligence can enhance stock clustering compared to traditional methods. We consider this in the context of the semi-strong Efficient M…
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…
LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
William Watson, Nicole Cho, Nishan Srishankar +7
Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is cha…
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations
Gaurav Verma, Rachneet Kaur, Nishan Srishankar +3
State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting…
Behavioral Sequence Modeling with Ensemble Learning
Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande +3
We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavi…
Variational Neural Stochastic Differential Equations with Change Points
Yousef El-Laham, Zhongchang Sun, Haibei Zhu +2
In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training…
AI in Investment Analysis: LLMs for Equity Stock Ratings
Kassiani Papasotiriou, Srijan Sood, Shayleen Reynolds +1
Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), o…
Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters
Nancy Thomas, Saba Rahimi, Annita Vapsi +5
Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilize…
Auditing and Enforcing Conditional Fairness via Optimal Transport
Mohsen Ghassemi, Alan Mishler, Niccolo Dalmasso +4
Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features.…
AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI
Eren Kurshan, Dhagash Mehta, Bayan Bruss +1
Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of ge…
Ensemble Methods for Sequence Classification with Hidden Markov Models
Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande +3
We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced…
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…
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi +3
Adversarially robust optimization (ARO) has emerged as the *de facto* standard for training models that hedge against adversarial attacks in the test stage. While these models are…
LETS-C: Leveraging Text Embedding for Time Series Classification
Rachneet Kaur, Zhen Zeng, Tucker Balch +1
Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time…
HiddenTables & PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies
William Watson, Nicole Cho, Tucker Balch +1
A myriad of different Large Language Models (LLMs) face a common challenge in contextually analyzing table question-answering tasks. These challenges are engendered from (1) finite…
Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy
Antigoni Polychroniadou, Gabriele Cipriani, Richard Hua +1
Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rate…
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…
Six Levels of Privacy: A Framework for Financial Synthetic Data
Tucker Balch, Vamsi K. Potluru, Deepak Paramanand +1
Synthetic Data is increasingly important in financial applications. In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it p…
FlowMind: Automatic Workflow Generation with LLMs
Zhen Zeng, William Watson, Nicole Cho +4
The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requir…
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting
Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa +2
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges. Recent studies have explored image-driven approaches…
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…