3 citations · 7 across the 23 of their papers we have counts for
24 papers
Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems
CheolWon Na, Hao Ni, Lukasz Szpruch +7
LLM-based multi-agent trading systems, in which specialized agents collaborate through structured communication to produce trading decisions, are moving rapidly from research proto…
What Makes a Peer? Valuation-Anchored Similarity in Private Markets
Sebastian Frank, Jingrao Lyu, Max Jarmey +5
As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer compa…
When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
Hoyoung Lee, Suhwan Park, Seunghan Lee +15
Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…
Evaluating LLMs in Finance Requires Explicit Bias Consideration
Yaxuan Kong, Hoyoung Lee, Yoontae Hwang +7
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contami…
Deep Reinforcement Learning for Optimum Order Execution: Mitigating Risk and Maximizing Returns
Khabbab Zakaria, Jayapaulraj Jerinsh, Andreas Maier +3
Optimal Order Execution is a well-established problem in finance that pertains to the flawless execution of a trade (buy or sell) for a given volume within a specified time frame.…
Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI
Samarth Sarin, Lovepreet Singh, Bhaskarjit Sarmah +1
Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical c…
A Unified AI System For Data Quality Control and DataOps Management in Regulated Environments
Devender Saini, Bhavika Jain, Nitish Ujjwal +3
In regulated domains such as finance, the integrity and governance of data pipelines are critical - yet existing systems treat data quality control (QC) as an isolated preprocessin…
Interpretable Model-Aware Counterfactual Explanations for Random Forest
Joshua S. Harvey, Guanchao Feng, Sai Anusha Meesala +2
Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to pro…
STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades
Mingshu Li, Dhruv Desai, Jerinsh Jeyapaulraj +4
Accurately measuring portfolio similarity is critical for a wide range of financial applications, including Exchange-traded Fund (ETF) recommendation, portfolio trading, and risk a…
AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
Tianjiao Zhao, Jingrao Lyu, Stokes Jones +3
The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human…
Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
Dimitris Vamvourellis, Dhagash Mehta
We investigate the effectiveness of large language models (LLMs), including reasoning-based and non-reasoning models, in performing zero-shot financial sentiment analysis. Using th…
Explainable Unsupervised Anomaly Detection with Random Forest
Joshua S. Harvey, Joshua Rosaler, Mingshu Li +2
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real d…
Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning
Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan +5
We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formali…
A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation
Bhaskarjit Sarmah, Kriti Dutta, Anna Grigoryan +3
We argue that the Declarative Self-improving Python (DSPy) optimizers are a way to align the large language model (LLM) prompts and their evaluations to the human annotations. We p…
How to Choose a Threshold for an Evaluation Metric for Large Language Models
Bhaskarjit Sarmah, Mingshu Li, Jingrao Lyu +4
To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a met…
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…
Can an unsupervised clustering algorithm reproduce a categorization system?
Nathalia Castellanos, Dhruv Desai, Sebastian Frank +2
Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not a…
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
Gregory Yampolsky, Dhruv Desai, Mingshu Li +2
The explainability of black-box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated indus…
HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction
Bhaskarjit Sarmah, Benika Hall, Rohan Rao +3
Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challen…
Quantile Regression using Random Forest Proximities
Mingshu Li, Bhaskarjit Sarmah, Dhruv Desai +4
Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn't just point prediction but determini…
Machine Learning-based Relative Valuation of Municipal Bonds
Preetha Saha, Jingrao Lyu, Dhruv Desai +4
The trading ecosystem of the Municipal (muni) bond is complex and unique. With nearly 2\% of securities from over a million securities outstanding trading daily, determining the va…
Open Set Recognition for Random Forest
Guanchao Feng, Dhruv Desai, Stefano Pasquali +1
In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowled…
Towards reducing hallucination in extracting information from financial reports using Large Language Models
Bhaskarjit Sarmah, Tianjie Zhu, Dhagash Mehta +1
For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions. How…
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
Joshua Rosaler, Dhruv Desai, Bhaskarjit Sarmah +4
We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF…