Curriculum Vitae

Curriculum Vitae

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Finance PhD (Stockholm University, 2026) specialising in market microstructure and insider trading. I build Python and R pipelines on large tick-level datasets, and three of my studies have been featured in The Wall Street Journal. Open to quantitative research, market surveillance and market structure roles at funds, exchanges and regulators.

Stockholm, Sweden · alexanderhub@hotmail.com · LinkedIn

Experience

Quantitative Researcher, Stockholm University
Aug 2021 – Present
PhD defended April 2026; continuing as postdoctoral researcher

  • Built Python and R pipelines that process 700+ GB of tick-level data and rebuild the London Stock Exchange limit order book.
  • Showed that common liquidity measures overstate trading costs by 7–24%, by matching trades to displayed quotes in the rebuilt order book.
  • Found that returns are predictable at the tick level (out-of-sample R² of 9%) but barely at the daily level (0.06%), with linear, ridge and lasso models on ~200 million observations.
  • Found, with enforcement data on illegal insider trading, that only 2% of suspects are prosecuted, and what makes detection and prosecution more likely.
  • Showed how trading urgency and opacity shape the choice between exchanges and dark markets, combining regulatory and tick-level data.
  • Won the Ola Bengtsson Award for best finance PhD paper; presented to audiences of 150+ at international conferences.

Independent Research (self-directed)
Dec 2025 – Present

  • Design and backtest systematic equity strategies in Python and R (long-run wealth creation, trend following, mean reversion), and publish weekly results with code on LinkedIn and in Articles.
  • Buy the Dip, featured in The Wall Street Journal: across 4,174 UK stocks, larger drawdowns predicted weaker 12-month returns relative to the broad market.
  • Magic Formula, featured in The Wall Street Journal: across 10,571 US stocks, portfolios ranked on earnings yield and return on capital have recently underperformed the S&P 500.
  • Name Changes, featured in The Wall Street Journal: across 8,985 US name changes, firms losing money before a rename underperformed comparable firms by about 13 percentage points over the following year.

Author, An Introduction to the Discount Factor Models
Mar 2020 – Nov 2020

Customer Advisor, Credit, Noba Bank
Jan 2020 – Aug 2021

  • Monitored repayment capacity and credit risk across a portfolio of unsecured consumer loans, supporting risk assessment and revised payment plans for at-risk borrowers.

Customer Advisor, Credit, Sergel Kredittjänster
Aug 2019 – Jan 2020

  • Handled debt-collection cases end-to-end; consistently achieved top-tier customer evaluations.

Working Papers

Bias in Execution Cost Measures (with Björn Hagströmer)
From Suspected to Prosecuted: Investigating Insider Trading, SSRN
Bright Light, Dark Room: Where Do Corporate Insiders Trade? (with Lars L. Nordén), SSRN

Education

PhD in Finance, Stockholm University
Aug 2021 – Apr 2026
Dissertation: Markets in the Dark: Insider Trading and Measurement Bias

MSc in Banking and Finance, Stockholm University
Aug 2019 – Aug 2021
Thesis (with Jesper Andersson): Does the tick size regime on systematic internalisers improve market quality?
Awards: Nasdaq/Swedish House of Finance Thesis Award, Skandia Best Thesis Award

BSc in Business and Economics, Luleå University of Technology
Aug 2016 – Aug 2019

Skills & Interests

Programming: Python (pandas, NumPy, scikit-learn, statsmodels), R (data.table, dplyr), LaTeX, Stata, Excel
Market Data: LSEG Workspace & Tick History, LSEG I/B/E/S, LSE Rebuild Order Book data, PDMR Transactions, regulatory enforcement data (suspicious transaction and order reports, STORs)
Languages: Swedish (native), English (fluent), Italian (conversational)
Interests: Chess (peak rating 1850), long-distance running, skiing