VN.

Hello! I'm Vihaan Narvekar.

I am an undergraduate student at New York University studying Finance, Computer Science, and Data Science. I'm currently focusing on quantitative research for financial markets, particularly using machine learning, economic data, and probabilistic modeling to understand how information is reflected in prices.

I am currently exploring language models for analyzing monetary-policy reasoning, fair-value modeling in event markets, and portfolio optimization theory.

Selected work

Research and applied technical work

01

Python · DuckDB · Parquet · scikit-learn

Modeling Weather Event Markets

An end-to-end quantitative research system for estimating fair values and studying market signals in weather prediction markets.

  • Built data pipelines for Kalshi weather contracts and normalized Yes/No order books.
  • Compared market-implied probabilities with NOAA and National Weather Service forecast-implied outcomes.
  • Mapped settlement rules to weather observations to support point-in-time, leakage-aware backtesting.
02

Python · LLM evaluation · NLP

Extracting Economic Arguments from FOMC Deliberations

A human-validated language-modeling workflow for classifying and evaluating economic arguments in Federal Open Market Committee discussions.

  • Engineered rubric-guided workflows for classifying approximately 1,000 FOMC statements.
  • Benchmarked open- and closed-source models against human-labeled validation data.
  • Implemented repeatable argument-strength evaluation as part of a broader NYU Stern research project.
03

Python · pandas · statsmodels

Forecasting Macroeconomic ETF Returns

A walk-forward forecasting study of SPY, QQQ, and IWM designed to evaluate whether macroeconomic inputs contain useful out-of-sample return signals.

  • Compared OLS, Lasso, Ridge, and quantile-regression models using rolling train and test windows.
  • Evaluated return magnitude and directional forecasts on strictly out-of-sample periods.
  • Translated model outputs into portfolio positions to study the relationship between forecasts and market timing.

Experience

Research and professional experience

NYU Stern Department of Finance

Assistant Researcher · NLP & FOMC Classification

Building language-model classification, benchmarking, and human-validation workflows for monetary-policy research.

Integrus Partners

Data Science Intern · Healthcare Group

Developed valuation and forecasting models and automated industry-data collection for healthcare transaction analysis.

Hevesta Capital

Investments Intern

Conducted market research and developed sourcing frameworks for energy operations and maintenance acquisition opportunities.

NYU Volatility and Risk Institute

Undergraduate Researcher

Analyzed labor-market datasets, developed monetary-policy indicators, and produced weekly macroeconomic research.

Education

New York University

B.S. in Computer Science and B.S. in Business, with concentrations in Finance and Data Science

Leonard N. Stern School of Business · Aug 2024 — May 2028 · GPA: 3.91/4.00

Clubs

tech@NYU, Quantitative Finance Society, and Business Analytics Club.

Outside research

A little more about me

Outside of research, I enjoy reading, playing violin, following Formula 1, playing rapid chess and golf, collecting watches and Japanese stationery, and drawing on my background in competitive swimming.