Richard Arbury

Student finishing undergraduate in December, 2024 with 3 years of experience in financial data analysis and asset management. Proficient in econometrics, machine learning, and Bayesian statistics, with a strong foundation in optimization, linear algebra and differential equations. Adept at cleaning, processing, and modeling data, aiming to leverage skills in applied mathematics and data science for research or analyst roles with the goal of attending graduate school for mathematical finance.

Python, R, C++, Stata

Experience with Git

Volatility Modeling, Python

• Utilized the HAR-RV model to predict price volatility on the S&P 500 and individual stocks using 5 minute, intraday trading data. Performance was then compared to the AR and GARCH models.

Volume Modeling, Python

• Comparison of predictive performance on the volume of stocks and stock indices using HAR, RNN, and LTSM-NN models.

Kaggle Housing Price Predictions, Python

• Cleaned, transformed, and preformed feature engineering on the Kaggle Housing Price data set to predict prices using XGBoost, Random Forest, and ANN models. Models had hyper-parameter tuning via Tree-Structured Parzen Estimator.