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 twice in The Wall Street Journal: Buy the Dip and Magic Formula.
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.
Six ordinary processing steps, each measured against a truth known by construction. One dropna call takes a third off the terminal wealth, and a missing delisting return adds 73.1%.
Read articleOn its own a name change predicts almost nothing. Among firms that were losing money before they renamed, the renamers underperform comparable firms by roughly 13 percentage points over the following year.
Read articleOn simulated data where the edge is real, the reported return runs from -1.6% to +6.3% a year, depending on eight ordinary portfolio choices.
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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