The author examines momentum profits and attempts to determine if momentum trading strategies are driven by time-varying systematic risk based on a version of the capital asset pricing model that uses conditional heteroskedastic models.
Although prior studies report evidence of abnormal returns from momentum strategies, none explain why momentum strategies are profitable. The author attempts to address this issue by examining whether time-varying systematic risk—estimated from conditional variance and covariance modeled using ARCH (autoregressive conditional heteroskedastic), GARCH (generalized autoregressive conditional heteroskedastic), and GARCH-M (generalized autoregressive conditional heteroskedastic-in-mean) processes—explains the existence of momentum profits associated with various trading strategies.
How Is This Research Useful to Practitioners?
According to market efficiency theory, security prices follow a random walk. Thus, any profit-generating trading strategy is unsuccessful in an efficient market. But researchers have analyzed profitable trading strategies that show that past performance can help predict future security prices.
The momentum trading strategy is one such strategy; it involves buying securities that have performed well over different time horizons and selling underperforming securities. It exploits the serial correlation present in security returns. The beta and expected return on a security depend on information available at any particular point in time and thus are time varying.
The author describes the time-varying systematic risk, as measured by the beta of winner and loser portfolios, as the ratio of (1) conditional covariance between the residuals from an autoregressive model for each winner/loser portfolio return and the market portfolio and (2) the conditional variance of the residuals from an autoregressive model for the market portfolio return. For the majority of momentum trading strategies, winner portfolios show higher systematic risk than loser portfolios (ignoring transaction costs); in some cases, this difference is found to be statistically significant. The authors show that momentum profits exist for the medium term. These findings would be useful for practitioners attempting to devise profitable trading strategies.
How Did the Author Conduct This Research?
The companies the author selects are a fair representation of the companies listed on the UK stock market over the period of January 1980–December 2010. Securities are ranked according to their performance over the previous 3, 6, 12, 18, and 24 months (i.e., five time periods). A security’s performance is determined by calculating its cumulative continuous monthly return (CCR).
Securities are ranked in ascending order and assigned to 1 of 10 portfolios based on each security’s CCR. That is, Portfolio 1 consists of those securities that performed the worst, and Portfolio 10 consists of those that performed the best. The next step is to buy winner portfolios and sell loser portfolios, holding onto the winner portfolios for a period of k months. Then, the authors calculate the average portfolio return at the end of k months and over the total data period. The process is repeated with five overlapping periods to avoid the influence of the economic cycle.
The author’s findings show that for some trading strategies, incorporating conditional information into asset pricing helps explain momentum profit. But conditional time-varying systematic risk alone cannot explain the anomaly of momentum profit found in the UK stock market. Further research in this direction will provide greater insights.