An independent research practice investigating how information, uncertainty, and human behavior move financial markets.
EXPLORE ARCHIVE↓Markets produce noise.
Research finds structure.
“The purpose of computing is insight, not numbers.”— RICHARD HAMMING
An event-study framework for separating inflation surprise, volatility regime, and post-release return windows.
Tests whether the level and term structure of implied volatility change how markets absorb scheduled information.
A reproducibility study of whether common equity-factor signals survive turnover, slippage, and portfolio constraints.
Tracks probability forecasts against realized outcomes using Brier scores, calibration curves, and prediction diaries.
A working view of questions, datasets, models, and evidence. This is where research is made—not just displayed.
LAB NOTE / Dashboard values describe the current research workflow and illustrative model states—not investment recommendations or live trading signals.
A chronological record of hypotheses, reading notes, failed assumptions, and ideas worth testing.
Defining the surprise variable before touching the response data.
Close-to-close and intraday returns answer different research questions.
Every assumption should be visible enough to challenge.
Mapping implied volatility, realized volatility, and uncertainty.
A reproducible event-study system connecting U.S. inflation releases, surprise measures, volatility conditions, and multiple S&P 500 return horizons.
Finance and mathematics student.
Research-driven investor.
Aspiring quantitative researcher.
I am building the technical and intellectual foundation to study markets with rigor: finance for context, mathematics for structure, programming for scale, and writing for clarity.
This archive documents the process in public—including the questions, assumptions, methods, and revisions that precede any result.
B.S. Finance
Mathematics studies
Expected 2028
Inflation · Volatility
Market microstructure
Forecasting · Factor models
Python · SQL · Statistics
Calculus · Econometrics
Market research writing
Python research workflows
Probability and statistics
Market data analysis
Founder, Women In Quantitative Finance Club
Founder, Youth Financial Wellness Program
Beta Alpha Psi
Indianapolis CFA Society
Quantitative research
Graduate financial engineering
Portfolio decision systems
A focused research agenda built around sharper questions, stronger evidence, and models that hold up out of sample.
Extend the CPI event-study database · test surprise and regime effects · publish reproducible findings
Out-of-sample testing · robustness checks · feature stability · forecast calibration · systematic backtests
Factor exposures · risk decomposition · scenario analysis · portfolio construction across market regimes
Market microstructure · stochastic modeling · derivatives · execution research · computational finance