I’m a Postdoctoral Researcher in Finance at Stockholm University, having defended my PhD in April 2026. My research focuses on market microstructure and insider trading. Both topics demand precision, from examining which corporate announcements trigger insider trading investigations to showing how common liquidity measures can materially overstate trading costs.
Outside my research, I design and backtest investing ideas in Python and R, including long-run wealth creation, trend following, and mean reversion. I share the results each week on LinkedIn and post longer write-ups with code in Articles. Recent work has been featured three times in The Wall Street Journal: Buy the Dip, Magic Formula, and Name Changes.
I’m on the job market. If you are hiring at a fund, an exchange, or a regulator, or just want to talk markets, send me a message.
Sorted at year-ends 2001 to 2020, the most expensive fifth of large US stocks grew its revenue three times as fast as the cheapest fifth over five years, and returned less.
Read articleWhen two stocks move almost together, raising one stock's estimated volatility from 20% to 20.2% changes the other stock's minimum-variance weight from 50% to 99.5%.
Read articleFrom November 2015 to September 2026, the funds of all seven smart beta strategies had a lower average Sharpe ratio than their cap-weighted benchmarks.
Read articleThe papers study the measurement of trading costs, the enforcement of insider trading regulation, and corporate insiders’ choice of trading venue. A common thread is how information is reflected in market data and how that data is used by researchers, regulators, and market participants.
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