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Quantitative Momentum by Jack R. Vogel, Wesley R. Gray

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CHAPTER 6Maximizing Momentum: The Path Matters

“…as the formation period return accumulates gradually over many days, the flow of information is continuous.”1

—Z. Da et al., The Review of Financial Studies

Chapter 5 highlighted that stocks with strong intermediate-term momentum signals, generically calculated as the past 12-month cumulative returns (skipping the most recent month), exhibit a strong continuation in returns. The evidence is pervasive across multiple time periods and asset classes. Given this empirical fact, a natural question arises: Can we do better than the generic intermediate-term momentum indicator? Figuring out a way to accomplish this goal can be difficult, especially when the risk of optimization and data mining is high. However, academic researchers have been studying this question for a while and have developed solutions that improve on the generic momentum algorithm, while simultaneously showing how the improvement relates to the theoretical behavioral foundations for momentum's existence. In other words, momentum improvements are evidence-based enhancements developed through the lens of the sustainable active framework, and not data mining run amok.

For over a year, we examined every respectable research piece on momentum stock selection strategies we could find and came to the general conclusion that one of the core ways to improve on a generic momentum strategy is to focus on the time-series characteristics of a momentum stock. In other words, we ...

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