Master in Finance graduate 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. My Master in Finance thesis at BI Norwegian Business School, on the relationship between passive investing growth and active management alpha, continues as Sisyphus.
Academic background
Personal and academic work in quantitative finance and technology
A keyboard-driven market terminal, built from scratch: a market-regime engine, a one-screen research loop with honest verdicts, an AI analyst that reads the wire, and whole-industry views. Forty function codes, one command bar.
Monthly forecasts for the Norwegian housing market: prices, days on market and sales volume. Grew out of my bachelor thesis on ML and Norwegian housing prices, now with a live macro data pipeline. Every forecast is published before the official number is released.
My master's thesis on passive investing and active alpha, kept alive. 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. Now continued out of sample: the same alpha and flow tests re-run monthly on public SEC filings.
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.
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
