Aug 11, 2026
Same data, same trading signal, different answer
On 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 articleAlexander Hübbert
About
Alexander Hübbert
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.
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The art of thinking clearly as a quant
Six biases that distort quantitative research, each one measured on a simulated market where the truth is known in advance.
Read articleJul 25, 2026
When out of sample stops being out of sample
On a simulated market with no edge, a researcher who keeps tuning against a hold-out pushes its Sharpe ratio to 2.8, while a final test set opened once stays at zero. An out-of-sample test becomes in-sample the moment we optimise against it.
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Working papers
The 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.
Browse the research