Many in academia have studied the predictability of stock returns along various cross-sections based on past returns. Some of these cross-sectional analyses dissect stock returns along time (returns patterns like momentum over the short and long term), industry (sector returns) among other dimensions.
Jason Wei of the University of Toronto proposes that momentum and reversals coexist. Here, momentum is understood to be the rate of acceleration of a security's price. Reversals are defined as changes in the direction of a price trend. Wei's research, detailed in the paper titled “Do momentum and reversals coexist?”, states that rather than assuming momentum and reversals as separate phenomena, the two occur simultaneously. Further, Wei also studies return predictability along the dimensions of size and volatility. Wei’s research documents that for large-cap/ low-volatility stocks, reversals prevail while large-cap/high-volatility stocks experience momentum.
Quantpedia concludes that this cannot be fully rationalized by either risk-based or behavioral-based explanations, with Wei adding that some behavioral-based models go the furthest in rationalizing the findings.
In order to study Wei’s findings, my notebook recreates the methodology using the Q1500 universe of stocks for a time period ranging from December 1, 2010 to December 1, 2016. When tuning and backtesting the corresponding algorithm, I noted a consistent decline or stagnation in performance from early 2015 to mid 2016. The reason for this is still unknown.
As my first exercise writing a notebook and conducting quantitative research, I’d love to receive feedback from the community. How can I heighten my researching skills? Thank you for reading and for your responses.