Reading & Learning

A working bibliography. I keep this page because the honest version of "what are you working on" is "what are you reading, and what did it turn into." Where a source fed a specific project, I say so. Reverse-chronological-ish within each section.

Papers

Empirical asset pricing

• Cochrane (2011), Discount Rates (AFA presidential address). The reframing that the central question is which characteristics carry independent information. The reason RegimeFactorZoo exists.
• Fama & French (1993), Common Risk Factors in the Returns on Stocks and Bonds. The foundation I replicate before trusting anything downstream.
• Gu, Kelly & Xiu (2020), Empirical Asset Pricing via Machine Learning. The case that ML, done with discipline, genuinely beats the linear model out of sample.
• Harvey, Liu & Zhu (2016), …and the Cross-Section of Expected Returns. Multiple-testing skepticism — why most of the factor zoo should never have cleared the bar.
• Baba-Yara (2026), In Search of Sparsity. The sparse-Bayesian framing that is the most direct parent of my regime-stability question.

Quantum computing

• Preskill (2018), Quantum Computing in the NISQ Era and Beyond. The sober map of what today's hardware can and cannot do.
• Stamatopoulos et al. (2020), Option Pricing using Quantum Computers. The concrete bridge: amplitude estimation applied to the pricing problems I already care about.
• Devitt, Munro & Nemoto (2013), Quantum Error Correction for Beginners. Where I am headed once the foundations settle.

Books

Currently working through

• Sutor, Dancing with Qubits — my primary quantum-computing text.
• Wong, Introduction to Quantum Computing: From a Layperson to a Programmer in 30 Steps — the hands-on, Qiskit-first companion.
• Ruppert & Matteson, Statistics and Data Analysis for Financial Engineering — the empirical-finance reference behind RegimeFactorZoo.
• Boyce & DiPrima, Elementary Differential Equations — coursework, and the runway toward stochastic calculus.

Reference shelf (consulted, not read cover to cover)

• Nielsen & Chuang, Quantum Computation and Quantum Information — the cathedral.
• Strang, Introduction to Linear Algebra — the lens for PCA, factor models, and quantum gates alike.
• James, Witten, Hastie & Tibshirani, An Introduction to Statistical Learning — the regularization chapters feed the ML factor work.
• Wilmott, Quantitative Finance; Shreve, Stochastic Calculus for Finance — derivatives and the math under them.
• López de Prado, Advances in Financial Machine Learning; Tsay, Analysis of Financial Time Series — the practitioner and time-series angles.

Courses & certificates

• MITx 15.415.1x — Foundations of Modern Finance I (MIT MicroMasters). In progress. CAPM, APT, and factor pricing — the theory under the empirical project.
• IBM Generative AI Engineering Professional Certificate (Coursera). Completed. Transformers, fine-tuning, and RAG, applied in the legal-document-intelligence build.
• Fundamentals of Quantitative Modeling — Wharton Online (Coursera). Completed.
• Financial Markets — Yale (Coursera). Completed.

Up next

A serious pass through Nielsen-Chuang once Sutor and Wong are done, the stochastic-calculus sequence toward derivatives pricing, and Putnam preparation aimed at December 2027 — because the problem-solving muscle is worth building deliberately, not by accident.

---

Where this reading turned into something runnable, it is on the portfolio and written up on the blog.