About

Background, research interests, and how to reach me.

Short version

I’m David Maguire, a quantitative researcher based in [CITY / COUNTRY]. I build systematic trading models and spend my spare cycles working through the academic literature and testing ideas of my own. I’m preparing to apply for a PhD in [TARGET FIELD — e.g. financial econometrics / machine learning for finance / market microstructure], and this site is where I keep the trail of that work in the open.

Replace the bracketed placeholders throughout this page with your own details. The structure is deliberately close to what a supervisor scans for: interests, evidence you can do research, and fit with their group.

Research interests

I’m currently most interested in:

  • [Interest 1] — e.g. selection bias and multiple testing in strategy discovery; how to report performance honestly.
  • [Interest 2] — e.g. volatility and the cross-section of returns; the idiosyncratic-volatility puzzle and its competing explanations.
  • [Interest 3] — e.g. machine-learning methods for return prediction and the gap between backtested and realised performance.

If you supervise in these areas, the Research page is the fastest way to see how I think. I try to keep this list narrow and aligned with the groups I’d want to work with, rather than long.

Background

  • [Role / employer or “independent researcher”], [dates]. [One line on what you do — the quant modelling, the stack, the scale.]
  • [Degree, institution, year]. [Relevant coursework, thesis, or result.]
  • Tools I work in daily: Python (NumPy/pandas, scikit-learn, PyTorch), [SQL / kdb+ / R], and a lot of careful backtesting.

How I work

Three principles run through everything here. Reproducibility first — if a result isn’t backed by code someone else can run, I treat it as a hypothesis, not a finding. Report the misses — negative results and failed replications are published alongside the wins. Cite the literature — new ideas are framed against what’s already known, not in a vacuum.

Contact

If you’re a prospective PhD supervisor, I’d be glad to send a short research proposal tailored to your group’s work.