DRICORN-K: A Dynamic RIsk CORrelation-driven Non-parametric Algorithm for Online Portfolio Selection

Abstract

Online Portfolio Selection is regarded as a fundamental problem in Computational Finance. Pattern-Matching methods, and the CORN-K algorithm in particular, have provided promising results. Despite making notable progress, there exists a gap in the current state of the art – systematic risk is not considered. The lack of attention to systematic risk could lead to poor investment returns, especially in volatile markets. In response to this, we extend the CORN-K algorithm to present DRICORN-K – a Dynamic RIsk CORrelation-driven Non-parametric algorithm. DRICORN-K continuously adjusts a portfolio’s market sensitivity based on the current market conditions. We measure market sensitivity using the \(\beta \) measure. DRICORN-K aims to take advantage of upward market trends and protect portfolios against downward market trends. To this end, we implement a number of market classification methods. We find that an exponentially weighted moving linear regression method provides the best classification of current market conditions. We further conducted an empirical analysis on five real world stock indices: the JSE Top 40, Bovespa, DAX, DJIA and Nikkei 225 against twelve state of the art algorithms. The results show that DRICORN-K can deliver improved performance over the current state of the art, as measured by cumulative return, Sharpe ratio and maximum drawdown. The experimental results lead us to conclude that the addition of dynamic systematic risk adjustments to CORN-K can result in improved portfolio performance.