Portfolio
Nick Aroney
Finance (Honours) graduate, University ofTechnology Sydney, with interests in financialmarkets, data analytics and financial technology.
About
My academic background is in finance and accounting, with training in financial analysis, corporate finance, financial markets, empirical research, and data analysis.
Completing an honours thesis strengthened my ability to manage detailed analytical work over an extended period, engage with feedback, and communicate findings clearly.
University of Technology Sydney
Feb 2025 - Nov 2025
Bachelor of Business (Honours) (Finance)
Feb 2022 - Nov 2024
Bachelor of Business (Accounting & Finance)
Thesis
Shadow Banking via Stablecoins: Evidence from the GENIUS Act
Jun 2025 - Nov 2025
- Authored an honours thesis analysing a global dataset of 608 listed banks across 55 countries, combining market, balance-sheet and crypto-adoption data to assess how US stablecoin regulation (GENIUS Act) affects banks' risk exposure, with a focus on emerging markets.
- Applied event-study, difference-in-differences and panel regression methods with robustness checks to estimate the impact of the GENIUS Act and assess implications for stablecoins, digital dollarisation and financial stability in weaker monetary systems.
Coursework
- Apr 2025Financial Markets - Market Microstructure Evaluation: Ethereum Liquidity Across Trading Venues
- Built a Python-based data pipeline to source, clean and standardise high-frequency minute-level Ethereum price data from Binance spot market, CME Ether futures, and Uniswap AMM pool, enabling cross-venue liquidity benchmarking.
- Developed rolling-window algorithms to compute quoted spread, quoted depth and price impact, using time-series visualisations to compare liquidity across markets and draw insights on trading venue choice and transaction costs.
- 2024Investment Banking – M&A Mock Deal Analysis: Dick's Sporting Goods Acquisition of Foot Locker
- Built a financial model in Excel to analyse deal profitability under multiple financing structures, including equity-only and debt-equity mix, incorporating synergy scenarios and accretion/dilution analysis to assess the acquisition's financial impact.
- Delivered a detailed pitchbook presentation outlining the target company's financial health, industry outlook, optimised deal structure, and profitability analysis, highlighting key insights on liquidity, leverage, and associated risks.
- 2024Applied Portfolio Management - Investment Strategy Analysis
- Designed and implemented a 130/30 trading strategy using Python, leveraging historical financial data and backtested financial health factors to optimise performance through parameter tuning (share allocation, rebalancing frequency).
- Conducted statistical analysis and applied machine learning techniques in Python, employing a decision tree model with cross-validation to assess predictive power and evaluate strategy performance under varying interest rate environments.