From the 1 of 7 linked papers with an AI index.
7 papers
Baselines Before Architecture: Evaluating Coding Agents for Autonomous Penetration Testing
Ananda Dhakal, Krish Neupane, Aarjan Chaudhary
The paper conducts a controlled evaluation of plain coding agents versus specialized penetration‑testing architectures on the XBOW benchmark, showing that baseline agents already s…
Detecting and Explaining Unlawful Insider Trading: A Shapley Value and Causal Forest Approach to Identifying Key Drivers and Causal Relationships
Krishna Neupane, Igor Griva, Robert Axtell +2
Corporate insiders trade for diverse reasons, often possessing Material Non-Public Information (MNPI). Determining whether specific trades leverage MNPI is a significant challenge…
The Strategic Gap: How AI-Driven Timing and Complexity Shape Investor Trust in the Age of Digital Agents
Krishna Neupane
Traditional models of market efficiency assume that equity prices incorporate information based on content alone, often neglecting the structural influence of reporting timing and…
The Information Dynamics of Insider Intent: How Reporting Inversions (Form 144) Mask Informational Rents in Insider Sales (Form 4)
Krishna Neupane
This study identifies and quantifies a significant informational friction embedded in the SEC Form 144 disclosure regime, characterized as predictive decoupling. Drawing on a theor…
Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability
Krishna Neupane, Prem Sapkota, Ujjwal Prajapati
This study establishes the causal effects of market sentiment on firm profitability, moving beyond traditional correlational analyses. It leverages a causal forest machine learning…
An extreme Gradient Boosting (XGBoost) Trees approach to Detect and Identify Unlawful Insider Trading (UIT) Transactions
Krishna Neupane, Igor Griva
Corporate insiders have control of material non-public preferential information (MNPI). Occasionally, the insiders strategically bypass legal and regulatory safeguards to exploit M…