Anomaly Detection in Exchange Traded Funds

Detecting anomalous returns in Exchange Traded Fund(ETF) constituents helps us maintain industry sector exposure while at the same time tuning sector ETF holdings by augmenting portfolio with dollar neutral and sector neutral trades. We demonstrate how Robust Principal Component Analysis (RPCA) can be used to tease out observed ETF constituent returns X into L, a low dimensional manifold, and S, an anomalous matrix containing abnormal returns. Abnormality of returns is determined by checking entries in anomalous matrix against public filings in U.S. Securities and Exchange Commission EDGAR database. Additionally, we show how sparsity in S can be controlled by a tuning parameter that gives us improved f1_scores. Moreover, we build several portfolios with daily and weekly turnover to show how trading in sector ETF anomalies can alter the portfolio return profile.