Reading

The research that informs the method. Education, not investment advice.

Conviction Read is an experimental research engine. Its approach draws on a body of public, peer-reviewed work in capital-growth theory, factor investing, position sizing, and the statistics of honest backtesting. The selected references below are listed for transparency and further reading. They are the sources that shaped the methodology, not a claim that the engine reproduces any of them, and citing a work does not imply that its authors endorse this site or its output.

Capital growth and online portfolio selection

How to compound capital over a sequence of bets, and the bound a forecast-free strategy can guarantee. These frame the question the engine tries to answer better than a no-edge baseline.

• Kelly, J. L. (1956). A New Interpretation of Information Rate. Bell System Technical Journal.
• Cover, T. M. (1991). Universal Portfolios. Mathematical Finance 1(1).
• Helmbold, D., Schapire, R., Singer, Y. & Warmuth, M. (1998). On-Line Portfolio Selection Using Multiplicative Updates. Mathematical Finance 8(4).
• Kalai, A. & Vempala, S. (2002). Efficient Algorithms for Universal Portfolios. Journal of Machine Learning Research 3.
• Li, B. & Hoi, S. C. H. (2014). Online Portfolio Selection: A Survey. ACM Computing Surveys 46(3).

Factor investing: quality, value, momentum

The evidence behind the engine factor pillars, and why combining decorrelated factors (value and momentum together) matters more than any single one.

• Jegadeesh, N. & Titman, S. (1993). Returns to Buying Winners and Selling Losers. Journal of Finance 48(1).
• Piotroski, J. D. (2000). Value Investing: The Use of Historical Financial Statement Information. Journal of Accounting Research 38.
• Asness, C., Moskowitz, T. & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance 68(3).
• Asness, C., Frazzini, A. & Pedersen, L. (2019). Quality Minus Junk. Review of Accounting Studies 24.

Risk, position sizing, and volatility

Sizing is most of the outcome. These cover volatility scaling and the regime behavior that governs when exposure should be reduced.

• Barroso, P. & Santa-Clara, P. (2015). Momentum Has Its Moments. Journal of Financial Economics 116(1).
• Daniel, K. & Moskowitz, T. (2016). Momentum Crashes. Journal of Financial Economics 122(2).
• Moreira, A. & Muir, T. (2017). Volatility-Managed Portfolios. Journal of Finance 72(4).

Backtesting rigor and avoiding overfitting

The reason a great-looking backtest is usually wrong, and the corrected statistics (deflated Sharpe, multiple-testing controls) that keep a track record honest. This is the discipline behind publishing a dated, forward record rather than a tuned hypothetical.

• Bailey, D., Borwein, J., López de Prado, M. & Zhu, Q. (2014). Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting. Notices of the American Mathematical Society 61(5).
• Bailey, D. & López de Prado, M. (2014). The Deflated Sharpe Ratio: Correcting for Selection Bias and Non-Normality. Journal of Portfolio Management 40(5).
• Harvey, C., Liu, Y. & Zhu, H. (2016). and the Cross-Section of Expected Returns. Review of Financial Studies 29(1).
• López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.

Machine learning in finance

A map of where machine learning helps in markets, and a candid review of where reinforcement learning for trading has and has not delivered.

• Kelly, B. & Xiu, D. (2023). Financial Machine Learning. Foundations and Trends in Finance.

This list is a curated subset and will grow as the research review continues. It is provided for education. Nothing here is a recommendation or a solicitation. See the disclaimer.