Hunter Ng

Hunter Ng

PhD Candidate @ Baruch College

Research

Research Areas

Accounting Standards Agentic AI/Small Language Models Information Systems Trust Escrows Prediction Markets Digital Assets/Distributed Ledgers Market Microstructure Applied Economics Welfare Economics Econometric Theory
Papers

Applied AI in Accounting

  • Working Paper Recovering Accounting Judgment with AI: Textual and Non-Textual Experiments [show abstract]

    Prior research shows that financial-statement presentation affects how users process and weight accounting information. However, artificial intelligence (AI) may not respond to presentation in the same way as human users, even when it receives the same underlying accounting information. This paper develops an empirical approach to examine whether AI recovers established human treatment effects from experimental accounting research. Using an exhaustive sample of published standard-setting experiments in leading accounting journals from 1995 through 2025, the analysis compares AI treatment effects with reported human responses and distinguishes textual from presentation-based manipulations. AI recovers human treatment effects less consistently when the manipulation depends on financial-statement presentation than when it is conveyed primarily through text. Further tests distinguish representation, processing, and judgment explanations for this difference. The evidence is most consistent with judgment differences: AI generally identifies and processes the relevant accounting information correctly but does not give presentation the same influence on judgment as human users. These findings suggest that AI can recover the underlying accounting facts without reproducing how presentation affects human decisions, limiting its use as a proxy for financial-statement users.

Digital Assets

  • Working Paper Price Discovery and Trading in Prediction Markets [show abstract]

    This study investigates price discovery and trading across major prediction markets in the days leading up to the 2024 U.S. presidential election, using novel self-assembled data. We document that the prices in these markets strongly lead election polls, reflecting their informativeness. Polymarket, with higher liquidity, plays a dominant role in price discovery, particularly during periods of heightened liquidity and trading activity. Furthermore, order imbalances from "whale" trades, defined as the top one percent of trade size in our setting, are highly predictive of future returns. There are also significant and persistent crossplatform price disparities, giving rise to profitable cross-platform arbitrage opportunities. These results highlight the economic significance of prediction markets in aggregating information and emphasize the importance of trading activity in shaping market outcomes.

    Download Paper

    Conferences and Presentations

  • Working Paper Outsourced Cryptocurrency Wash Trading [show abstract]
    • Presentations: Baruch–SWUFE Symposium 2025

    I show that outsourced cryptocurrency wash trading is shaped by the objective assigned to the outside firm. When hired to manufacture volume, wash traders buy and sell nearly equal amounts, repeatedly turning over the same inventory. When also tasked with selling issuer holdings, trading becomes markedly more one-sided, with sales exceeding purchases. Using documented enforcement cases and transaction records, I trace these differences in net selling, two-way trading, and inventory replenishment across controlled wallets. The results show that similar reported trading volume can mask fundamentally different economic activity as contractual objectives shape whether manipulation recycles inventory or liquidates issuer holdings.

    Download Paper

    Conferences and Presentations

    • The Annual Baruch College/CUNY Southwestern University of Finance and Economics (SWUFE) Research Symposium, Baruch College, May 2025

Law and Economics

  • Working Paper Information or Institution? AI’s role in Expert Testimony [show abstract]

    This paper studies whether the value of expert testimony lies primarily in its informational content or in the institutional role of the expert witness. Using federal district court Daubert rulings in copyright cases, we show that admissibility is not determined by methodology alone, but also by institutional considerations. We develop a model to ground this intuition and to show how AI can compress differences in methodological production while making institutional signals more important for judicial evaluation. Empirically, we then compare observed expert packages to AI-generated packages from neutralized case descriptions and find that AI reproduces much of the methodology and topic coverage of retained experts, while systematically proposing broader and more credentialed expert designs. AI-generated packages are closest to experts whose testimony is admitted and furthest from those whose testimony is excluded. The results show that methodology is increasingly reproducible, but institutional features of the expert still matter for admissibility.

    Conferences and Presentations

Accounting Education

  • Working PaperIRB Approved Using a Sandboxed Ticketing Bot to Teach Digital Trust and IT Audit [show abstract]

    The verification of Information Technology (IT) controls is a core responsibility of IT auditors. This case places students in the role of IT auditors assigned to assess the effectiveness of controls over a sandboxed online ticketing system. Students first participate in limited ticket drops, inspect the website’s Document Object Model (DOM), and use AI-assisted coding to understand how automated purchasing can occur. Students then evaluate whether the system’s controls over purchase limits, transaction authorization, backend logging, exception reporting, and monitoring are effective. The case introduces students to technical skills such as browser inspection, basic automation logic, and AI-assisted coding while maintaining an internal-control focus. Students document their work through a DOM discovery worksheet, ticket-drop observation sheet, internal-control review matrix, and short reflection. The case is relevant to Accounting Information Systems (AIS), IT Audit, and Audit courses because it connects digital trust, automated transactions, IT controls, and AI-assisted technical fluency. Instructors may use the case at either the undergraduate or graduate level.

  • Working PaperIRB Approved Scalable Data Analytics Pedagogy in Accounting: Randomized Case Studies and Automated Assessment

    The growing importance of data analytics (DA) in accounting has changed how accounting professionals collect, analyze, and interpret data. As DA becomes increasingly integrated into accounting practice, accounting students need assignments that combine analytical, technical, and professional judgment skills. This paper presents a scalable teaching case that uses randomized datasets and automated grading to support individualized student analysis in larger classes. Each student receives a unique dataset generated from a common underlying structure, preserving consistent learning objectives while reducing the usefulness of shared solutions. In the case, students estimate the allowance for doubtful accounts using historical customer-level accounts receivable data and compare the percentage-of-sales method with the aging-of-receivables method. Students complete spreadsheet-based analysis and prepare a written recommendation supported by their results. Automated grading evaluates the technical outputs, while the written report assesses interpretation and communication. The case provides a flexible model for integrating DA competencies into accounting courses without requiring extensive programming expertise from instructors.

Econometrics

  • Working Paper Vine Copula VAR: From Recursive Margins to Joint Forecast Inference [show abstract]
    • Coauthors: Yubo Tao
    • Presentations: MEG 2026

    This paper introduces a scalable and order-invariant framework for modeling high-dimensional macro-financial dynamics. We develop a Time-Varying Parameter VAR with Vine Copula Dependence (VCVAR), which couples a lightweight TVP backbone with a flexible copula representation of cross-sectional dependence. The approach separates marginal dynamics from joint dependence, allowing the model to capture structural drift together with asymmetric, nonlinear, and tail-dependent co-movements that standard Gaussian or Cholesky-based VARs cannot represent. Despite this flexibility, the VCVAR remains computationally tractable through discounted recursive estimation and simplified vine construction. Applications to empirical macroeconomic data and controlled simulation designs show that the VCVAR offers consistent improvements in medium- and long-horizon forecasting, demonstrating the value of combining time variation with non-Gaussian dependence in large systems.

    Download Paper

General Economics

  • Working Paper U.S. most-favored-nation pricing and foreign drug funding [show abstract]

    We examine whether the May 2025 U.S. most-favored-nation pricing announcement was followed by changes in foreign public funding of medicines. International price discrimination theory predicts a tension where accepting lower foreign reimbursement prices under MFN expands sales abroad but risks reducing U.S. revenues, potentially delaying public-funding agreements for medicines with higher U.S. spending. We find that these medicines experienced a larger relative decline in new public-funding entries in potential reference markets. Using records from three OECD and three non-OECD markets, we compare documented funding entry before and after the announcement through May 2026. Among 361 medicines linked to earlier U.S. spending, balancing FDA approval age yields a larger relative OECD decline for higher-spending medicines of 0.261 entries per 100 medicines per month. This negative association persists across alternative weights and robustness tests.

  • Published Legitimacy and the Fluid Singaporean Welfare State (Sociology Compass) [show abstract]

    This paper develops a theory of the fluid welfare state, in which legitimacy rather than efficiency constitutes the central constraint of welfare governance. Using a dynamic model of government–citizen interaction, we show that a state that adjusts its welfare messaging across social groups enhances legitimacy when the persuasive benefit exceeds the credibility cost. The framework is inductively derived from the case of Singapore, which exemplifies how a small, open, and fiscally disciplined East Asian productivist welfare state can sustain public trust through performance and communication rather than extensive redistribution. As welfare communication increasingly takes place through digital platforms and social media, the capacity to adapt these narratives can be a powerful tool for all regimes. The concept of the fluid welfare state therefore provides a framework for understanding how contemporary governments, particularly those facing fiscal and demographic constraints, can preserve welfare legitimacy through adaptive persuasion rather than expansionary spending.

  • Working Paper AI Guardrails in Ambiguous Harm Settings: Evidence from Curses [show abstract]

    This study examines how AI guardrails respond when a request has a hostile objective but uncertain practical capacity to cause harm. I study whether models preserve the request, selectively change the hostile objective, or refuse the request altogether. When a prompt combines permissible content with a hostile objective, models are often able to disentangle the two components. Rather than refusing the request outright, they frequently transform it by preserving the permissible content while changing the hostile objective. Hostile requests intended to be sent directly to the target of the hostile content are preserved less often than otherwise similar requests described as private. These patterns appear across model families, while neither later model releases nor higher reasoning settings produce a consistent directional change in preservation.

  • Working Paper Why is it so hard to find a job now? Enter Ghost Jobs

    This paper investigates "ghost jobs", which are vacancies posted without intent to hire, using a novel dataset of interview reviews from Glassdoor. Using a fine-tuned BERT model, I find that approximately 21% of job postings exhibit patterns consistent with ghost jobs. These are disproportionately concentrated in larger firms and high-skill industries, where firms may benefit from resume collection, market intelligence, or signaling. I also show that incorporating ghost job prevalence helps reconcile the recent disconnect in the Beveridge Curve between vacancy and hiring rates. The results highlight how ghost hiring imposes costs on job seekers, distorts labor market indicators, and warrants closer scrutiny from policymakers.

Notes on Becoming a Researcher

Conferences and Presentations
  • Presenter2026 17th Annual Pre-ICIS Workshop on Accounting Information Systems (SIG ASYS), Lisbon Congress Centre (Centro de Congressos de Lisboa), Lisbon, Portugal, December 12, 2026 - Download Invitation - Download Schedule
  • Presenter2026 Midwest Econometrics Group (MEG) Annual Meeting, Graduate by Hilton Cincinnati, Cincinnati, OH, October 9-10, 2026 - Download Invitation - Download Schedule - Download Slides
  • Presenter2026 The First Workshop on Sovereign AI for Collaborative and Pluralistic AI Ecosystems, HCOMP 2026, Virginia Tech, Washington, DC, September 27, 2026 - Download Invitation - Download Schedule - Download Slides
  • Presenter2026 3rd Financial Fraud, Misconduct and Market Manipulation Conference 2026, Lancaster University, Lancaster, UK, September 10-11, 2026 - Download Schedule - Download PPTX
  • Participant2026 AAA Global Connect Annual Meeting, Caesars Palace, Las Vegas, NV, August 1-5, 2026 - Download Participant List
  • Presenter2026 AAA/Deloitte Foundation/J. Michael Cook Doctoral Consortium, Deloitte University, Westlake, TX, June 7-10, 2026 - Download Agenda - Download PPTX - Download PDF
  • Participant2026 Baruch Workshop on AI in Finance and Accounting, Baruch College, New York, NY, May 7-8, 2026 - Download Agenda
  • Participant2026 The 24th Annual Financial Reporting Conference, Baruch College, New York, NY, May 7, 2026 - Download Agenda
  • Participant2026 Four-School Conference (Baruch, Fordham, Rutgers, UConn), 2026
  • Participant2025 AES Baruch Accounting Theory Summer School, Baruch College, New York, NY, Summer 2025
    • Taught by Ivan Marinovic, Jon Glover, Jeremy Bertomeu
  • Presenter2025 Hawaii Doctoral Institute Summer, University of Hawaii, Honolulu, HI, Summer 2025
  • Presenter2025 The Annual Baruch College/CUNY-SWUFE Research Symposium, Baruch College, New York, NY, May 27, 2025 - Download Agenda
  • Presenter2025 Baruch PhD Research Day in Finance, Baruch College, New York, NY, May 28, 2025 - Download Agenda
  • Presenter2025 Finance Brownbag, Baruch College, New York, NY, 2025 - Download Agenda
  • Participant2025 Baruch/JFQA Climate Finance Conference, Baruch College, New York, NY, 2025
  • Participant2025 Baruch-Fordham-Rutgers Trischool Conference, New York, NY, 2025
  • Participant2024 The Chinese Finance Association TCFA 30th Annual Conference, Gerald W. Lynch Theater, New York, NY, October 26-27, 2024
  • Participant2024 NYU Accounting Theory Summer School, New York University, New York, NY, August 6-8, 2024
    • Taught by Ilan Guttman, Judson Caskey, Jeremy Bertomeu
  • Presenter2024 Duke University Accounting Theory Summer School, Duke University, Durham, NC, 2024
    • Presented “Cybersecurity Disclosures”
    • Taught by Itay Goldstein, Qi Chen, Chandra Kanodia, Thomas Hemmer
Advisors
Peer Reviews
  • Reviewer (1x) for AAA Auditing Section Midyear Meeting (2027) Artificial Intelligence Experiment
  • Reviewer (1x) for Joint Midyear Meeting of the AIS and SET Sections (2027) Teaching Case
  • Reviewer (4x) for FARS Accounting Conference (2027) Cybersecurity DisclosuresInnovation ProtectionAI and Pricing of EarningsMutual Fund Disclosure
  • Reviewer (1x) for Journal of King Saud University Computer and Information Sciences (2026; invited) RAG / Financial Document QA
  • Reviewer (1x) for Cogent (2026) Prediction Markets
  • Reviewer (1x) for Journal of International Financial Markets, Institutions & Money (2026) Prediction Markets
  • Reviewer (1x) for Cogent (2025) Prediction Markets
  • Reviewer (1x) for Journal of International Financial Markets, Institutions & Money (2025) Prediction Markets
  • Reviewer (1x) for Management Science Reproducibility Project (2023) 10-K Footnote Disclosure
Awards
  • 2025/2026 Zicklin Dean’s Teaching Award
  • Mills & Tannenbaum Award 2025
  • Donald Vredenburgh Research Grant 2025