Does momentum-trading work with monthly charts, part 2

Wei and Yang (2012), from the University of Toronto, Canada, examined momentum in a large sample of U.S. stocks from 1964 to 2009. At the beginning of each month, stocks were sorted quintiles based on their realized past returns. Equally weighted portfolios were formed and held for up to 12 months. The overall results show significant and consistent momentum, as is evidenced in the following chart:

The average monthly return percentage is based on the combined profits of both long and short trades. Based on data from Wei and Yang (2012).

The average monthly return percentage is based on the combined profits of both long and short trades. Based on data from Wei and Yang (2012).

Interestingly, although the momentum effect was statistically significant, the researchers also discovered a subpopulation of stocks for which there was a significant reversal effect: large-cap stocks of low volatility. For small stocks, no reversals were observed. For both large and small cap stocks, the momentum effects were stronger when volatilities were higher.

The following chart show large cap stocks separated into quintiles according to volatility. The formation or ranking period is for one month. This finding suggests that, if you wish to benefit from a volatility-based portfolio, you would be wise to eliminate large cap stocks of low volatility.

Based on data from Wei and Yang (2012).

Based on data from Wei and Yang (2012).

While the study of Gutierrez and Kelley (2008) [presented elsewhere in Book Two] suggests that there is a time-sequence relationship between reversal and momentum, Wei and Yang’s findings point to the simultaneous occurrence of these opposite tendencies in different populations of large cap stocks. These distinctly different findings are not necessarily inconsistent with each other. However, the relationship between reversal and momentum is an area of inquiry that clearly merits further research.

Trading strategy: Focus on Australian and Canadian stocks or on other markets where volatility is relatively high. Use a six-month formation or ranking period. Skip one month between the end of the ranking period and the beginning of the holding period (to mitigate potential reversal effects). Hold long and short positions for one to three months. 

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Book Three: Trading With The News

Learn about a news-based trading system that yielded a back-tested, average annualized, compounded return from 2000 to 2011 of 58.6%.

“Only once you’ve done your homework will you be able to understand how the stock market works and learn to distinguish between news and noise.” Maria Bartiromo, Use The News

Book Two: Technical Analysis

Learn about the "trend recalling" algorithm that yielded researchers a simulated annual return of greater than 400% in multiple tests.

“The scientific method is the only rational way to extract useful knowledge from market data and the only rational approach for determining which technical analysis methods have predictive power.”
David Aronson, Evidence Based Technical Analysis

Book One: Analysts’ Forecasts

Learn the strategy, based on analysts' revised forecasts, that yielded researchers an average of 1.13% - 2.19% profit per trade, for trades lasting one to two days?

Learn how certain analysts' recommendations, following brokerage hosted investment conferences, yielded profits of over 3% during a two-day holding period?

Learn how researchers found an average profitability of 1.78% for two-hour trades following an earnings announcement?

"This set of tools can help both ordinary and professional investors alike to re-think and re-vitalize their stock picking, timing and methods. A young, aspiring Warren Buffet could put this book to good use."
James P. Driscoll, PhD, investor

Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments by David Aronson (software included)

Evidence-Based Technical Analysis by David Aronson

Archive of Earlier Posts