PT  - JOURNAL ARTICLE
AU  - Chris Gowlland
AU  - Zhijie Xiao
AU  - Qi Zeng
TI  - Beyond the Central Tendency: <em>Quantile Regression as a Tool in Quantitative Investing</em>
AID  - 10.3905/JPM.2009.35.3.106
DP  - 2009 Apr 30
TA  - The Journal of Portfolio Management
PG  - 106--119
VI  - 35
IP  - 3
4099  - https://pm-research.com/content/35/3/106.short
4100  - https://pm-research.com/content/35/3/106.full
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