Finance & Data Science

Ivan Hung

Currently looking for Global Markets, Investment Specialist, Portfolio Analytics and Business Scientist roles in Hong Kong.
Available to work in HK, UK and US.

ivanhung04.hk@gmail.com linkedin.com/in/hivan04 github.com/hivan04

Professional Experience

03 roles
Jun 2026 —
Aug 2026
Wealth Management Intern — International Team, APAC
UBS AG
London, UK
Return offer · 2027 Graduate Program
  • Produced a presentation on Sukuks and built three risk-profiled model portfolios using Solver, closing client knowledge gaps to support portfolio discussions
  • Analyzed internal advisory portfolios, identified cash allocation gaps, and drove a 7% performance improvement through reallocation
  • Built an automated prospecting tool using LLM prompt engineering to identify and screen potential clients, bringing £10M in Net New Money
  • Created quarterly performance decks for client advisors, highlighting forward-looking risk exposures and key KPIs
Jun 2025 —
Aug 2025
Data Science Intern
NatWest Group plc (RoosterMoney)
London, UK
Return offer · 2026 Graduate Program
  • Applied machine learning models (logistic regression, XGBoost) via scikit-learn to mitigate customer churn risk, reducing exit rates by 17%
  • Used prompt engineering and an agentic workflow to deploy a generative AI-powered chatbot, cutting response time by 20%
  • Led an end-to-end churn analysis using SQL (Snowflake/AWS) and Tableau, identifying a 46% higher churn rate after a first declined transaction
Jun 2024 —
Sep 2024
Wealth Management Intern
Chow Tai Fook (CTF) Life
Kowloon Bay, HK
  • Collaborated with a team of four on due diligence for Allianz, addressing regulatory and operational risks to pitch an investment deck with 32% ROI
  • Applied financial planning and pension product knowledge to help close a HK$450,000+ premium deal
  • Spearheaded a team of 10 to curate a portfolio website showcasing the firm's financial offerings, increasing traffic by 12%

Research Projects

05 studies
PYTHONQuant Finance

Statistical Arbitrage in HK Equities: A Cointegration & Kalman Filter Strategy

  • Developed a cointegration-based statistical arbitrage strategy using Engle-Granger and ECM frameworks on HK equities across tech and commodities
  • Implemented dynamic hedge ratios via Kalman filtering to capture time-varying relationships between assets
  • Constructed systematic trading signals using spread z-scores with entry/exit thresholds and position management logic to reduce false-signal exposure
 PYTHONDerivs Trading

Rolling Call Strategy on Copper Futures: Options Pricing & Regime Analysis

  • Backtested a rolling 12-month ATM call strategy on copper futures using Black (1976) pricing with realistic roll mechanics and transaction costs
  • Sub-sample analysis revealed strong supply-shock hedging properties — 0.86 Sharpe and -10% max drawdown during the 2016 China supply cycle, vs. 0.24 full-sample
  • Derived annualized volatility inputs from 36-month rolling log-return standard deviations, isolating regime-dependent convexity as the key performance driver
 PYTHONQuant Finance

An Ensemble Approach to the Overnight Equity Anomaly

  • Constructed a survivorship-free, point-in-time backtest over the S&P 1500 (2010–2024) with 50+ cross-sectional features, eliminating look-ahead bias
  • Developed a walk-forward ensemble (Huber, LightGBM, IC-weighted composite) yielding significant predictability — Spearman IC 2.1%, t-stat 6.7
  • Built a costed dollar-neutral long/short book with realistic costs and liquidity caps, isolating turnover as the binding constraint on net returns
PYTHONQuant Macro

Cross-Asset Carry: Currencies, Commodities & Government Bonds

  • Developed a systematic cross-asset carry strategy across FX, commodities and bonds using dollar-neutral long-short ranking with inverse-volatility weighting
  • Validated signal quality via information coefficients, quintile sorts and panel regressions, blending sleeves to deliver a Sharpe of 0.82 at 0.06 equity beta
  • Stress-tested robustness through parameter sensitivity, signal-lag and transaction costs, finding a conservative out-of-sample Sharpe of 0.45
  • Presented findings to lecturers and co-authored the final report as part of a 6-person team, translating results for a non-technical audience
PYTHONQuant Finance/Econometrics

Oil Futures Spread Trading Strategy: Forecasting and Economic Performance

Top grade in cohort
  • Developed and evaluated forecasting models for WTI crude oil futures spreads (CL2–CL1), exploiting term structure dynamics to generate trading signals
  • Implemented ARIMA and ARIMAX models with rolling out-of-sample backtesting, demonstrating the robustness of parsimonious models in volatile regimes
  • Translated forecasts into a systematic spread strategy, achieving higher risk-adjusted returns (Sharpe > 1), ~63% hit rate, and lower drawdowns

Skills

Finance & Risk

Asset Allocation Stress Testing Scenario Analysis Model Risk Management Macro Finance Stochastic Calculus Financial Modelling

Programming & Data

Python R SQL Excel VBA Microsoft Office Stata Tableau AWS SageMaker

Analytics & Modeling

Data Visualization Dashboard Development XGBoost scikit-learn PyTorch

Tools & Platforms

Bloomberg Terminal GitHub Confluence Jira VS Code

Education

Aug 2025 —
Aug 2026
MSc Finance — Investment and Wealth Management
Imperial College Business School, London
Financial Econometrics · Machine Learning · Derivatives · Asset Allocation & Investment Strategies · Applied Quantitative Macro Strategies
Sep 2022 —
Jun 2025
BSc Economics and Finance
University of East Anglia, Norwich
Ranked 1st of 250 students in first and final year · Brightspark Scholarship (£3,000) · Distinguished Performance Award, both years · Alternative Investments (86%) · Corporate Finance (83%)

Certifications & Languages

In Progress

CFA Level I FIMC — Wall Street Prep Data Scientist Associate — DataCamp

Languages

English — Native Cantonese — Fluent Mandarin — Intermediate

Activities Outside of Work

Padel Golf Bouldering Calisthenics Gym Taekwondo Piano — ABRSM Grade 8 (Merit) Drums