Quantile Regression as a Tool for Portfolio Investment Decisions
Faculty of Business and Law
School of Accounting, Finance and Economics / Finance, Economics, Markets and Accounting Research Centre
The worldwide impact of the Global Financial Crisis on stock markets, investors and fund managers has lead to a renewed interest in appropriate tools for robust risk management. Quantile regression is a powerful technique and deserves the interest of financial decision makers given its remarkable capabilities for capturing and explaining the behaviour of financial return series across a distribution more effectively than ordinary least squares regression methods which are the standard tool. In this paper we present quantile regression estimation as an attractive additional investment tool, which is more effective than Ordinary Least Squares in analyzing information across the quantiles of a distribution. This translates into the more accurate calibration of asset pricing models and subsequent informational gains in portfolio formation. We present empirical evidence of the superior capabilities of quantile regression based techniques as applied across the quantiles of return distributions to derive information for portfolio formation. We show, via stocks in Dow Jones Industrial Index, that at times of financial shocks, such as the Global Financial Crisis, a portfolio of stocks formed using quantile regression in the context of the Fama-French three factor model, performs better than the one formed using traditional OLS.