August Liu

M.S. Quantitative Economics at NYU Stern School of Business

Hi, I am currently an M.S. student in Quantitative Economics at NYU Stern. My research interests are concentrated in Applied Macroeconomics, Industrial Organization, and Political Economics. I am also interested in Epistemology and Metaphysics.

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About

I am currently an M.S. student in Quantitative Economics at NYU Stern. I completed my undergraduate studies at NYU College of Arts and Science, majoring in Economics with minors in Mathematics and Philosophy.

My academic interests sit at the intersection of formal modeling, empirical research, and decision-making. I am currently working as a research assistant under the supervision of Professor Alan Chernoff.

Research Projects

I am currently conducting research on merchant-side effects of Buy Now, Pay Later (BNPL) and mispricing in prediction markets.

Merchant-Side Effects of Buy Now, Pay Later (BNPL)

Research Co-Author with Prof. Alan Chernoff, New York University

May 2026 - Present

This project studies how BNPL adoption affects merchant performance using staggered difference-in-differences and event-study designs. I am helping construct an analysis-ready merchant panel by combining alternative data across five BNPL providers with entity resolution, ticker mapping, industry classification, and adoption-date validation. The empirical design examines revenue growth, operating margins, web traffic, and cumulative abnormal returns, with firm and time fixed effects, clustered standard errors, parallel-trends tests, dynamic treatment effects, and heterogeneity across industries, firm sizes, and adoption windows.

Python / Difference-in-Differences / Event Study / Alternative Data / Fixed Effects / Corporate Finance / BNPL

GitHub Repository

Prediction Market Mispricing, Calibration & Limits to Arbitrage

Research Collaborator - Applied Econometrics & Forecast Evaluation

August 2026 - Present

This project evaluates when and why prediction-market prices deviate from calibrated probabilities and where limits to arbitrage may persist. I am supporting the construction of point-in-time Polymarket and Kalshi datasets spanning trades, prices, order books, market resolutions, and liquidity measures while explicitly guarding against look-ahead bias. The research develops calibration regressions, double/debiased machine learning designs, and natural-experiment strategies to study how liquidity, order-book depth, market horizon, and market structure shape out-of-sample forecast performance.

Prediction Markets / Applied Econometrics / Calibration / Causal Inference / Double Machine Learning / Polymarket / Kalshi

Education Background

M.S. in Quantitative Economics

New York University Stern School of Business

July 2026 - May 2027

Master of Science in Economics. Concentrated on Industrial Organization and Applied Macroeconomics.

B.A. in Applied Economics

New York University

September 2022 - May 2026

Bachelor of Arts in Applied Economics with minors in Mathematics and Philosophy. Concentrated on Macroeconomics, Political Economics and Industrial Organization.

Contact

I am always open to conversations about research, economics, financial markets, data projects, and interdisciplinary ideas.

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