MSc Finance candidate with strong quantitative skills and hands-on experience in financial analysis, valuation, and portfolio management.
Oslo, Norway
Professional summary
I combine financial theory with computational tools to understand markets. My work spans derivatives pricing, portfolio optimization, and machine learning for financial applications — from building risk engines to exploring quantum computing for option pricing. Currently completing my MSc Finance thesis at BI Norwegian Business School on the relationship between passive investing growth and active management alpha.
Academic background
Personal and academic work in quantitative finance and technology
Examines whether the growth of passive investing (from ~6% to 51% of U.S. mutual fund AUM, 1996–2025) is associated with changes in aggregate active manager alpha. Uses ~29,800 active and ~3,700 passive funds from Morningstar Direct with CAPM, Fama–French 3- and 5-factor models. Documents a specification pitfall in fee-spread controls, finds no robust linear association between passive share and alpha under multifactor models, and provides exploratory evidence of stress-conditional effects on alpha during periods of high passive concentration.
Proprietary risk management system for real-time portfolio risk assessment. Production-ready enterprise infrastructure with CI/CD pipeline, designed for institutional-grade portfolios.
Python-based tools for analyzing and backtesting systematic options strategies. Applied delta, implied volatility, and liquidity analysis with risk management through diversification and defined-loss structures.
Built a PyTorch LSTM model to explore predictive signals from OHLCV data. Tested model insights through simulated trading and performance evaluation.
Applied Modern Portfolio Theory to construct diversified portfolios using Python. Compared risk-adjusted returns against market benchmarks using Markowitz optimization.
Quantum computing applications for complex derivatives pricing using VQE and QAOA algorithms. Research into quantum advantage in financial computation.
Automated arbitrage detection and execution system for prediction markets with real-time probability calculations and live trading.
Bachelor thesis project using machine learning to predict Norwegian real estate market trends. Feature engineering with economic indicators and property-level data.
Web application for derivatives pricing with real-time market data integration. Features Greeks calculation, volatility surface visualization, and portfolio tracking.
Technical and domain expertise
Professional background
Season winner probabilities and next-race winner signal using form + telemetry-style pace proxies
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