RT Journal Article
SR Electronic
T1 Beyond the Central Tendency: <em>Quantile Regression as a Tool in Quantitative Investing</em>
JF The Journal of Portfolio Management
FD Institutional Investor Journals
SP 106
OP 119
DO 10.3905/JPM.2009.35.3.106
VO 35
IS 3
A1 Chris Gowlland
A1 Zhijie Xiao
A1 Qi Zeng
YR 2009
UL https://pm-research.com/content/35/3/106.abstract
AB Quantitative investors frequently analyze factor performance using regression based on the familiar ordinary least squares approach. This is highly effective for understanding the central tendency within a dataset, but will often be less useful for assessing the behavior of datapoints close to the upper or lower extremes within a population. But from the perspective of active investors or risk managers, the datapoints at the extremes may be precisely the ones of greatest interest. For such applications, a more appropriate methodology is quantile regression. The authors show how quantile regression represents an extension of the conventional ordinary least squares method, and present an empirical analysis of factor effectiveness applied to a universe of U.S. small-cap stocks in order to illustrate the insights offered by this technique.TOPICS: Portfolio construction, statistical methods, accounting and ratio analysis