Abstract
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.
Highlights
How well a portfolio performs is always the major concern for investors, and is usually the major metric reflecting investor confidence in the portfolio’s management
As far as we are aware, performance attribution measures are currently used exclusively as a diagnostic, in the sense that if today’s attribute values underperform, changes are implemented in the portfolio with the goal of improving tomorrow’s attribute values
Motivated by this and by the work of Biglova and Rachev (2007) and Rachev et al (2009), we investigate the impact on portfolio optimization using AA and selection effect (SE) as additional constraints on asset weights as a method of combining performance and tail-risk control
Summary
How well a portfolio performs is always the major concern for investors, and is usually the major metric reflecting investor confidence in the portfolio’s management. As is apparent from their definitions, AA and SE measure the differences between mean performance of asset classes in a managed portfolio and those of a market benchmark, and are ‘blind’ to volatility effects, i.e., to tail-risk Motivated by this and by the work of Biglova and Rachev (2007) and Rachev et al (2009), we investigate the impact on portfolio optimization using AA and SE as additional constraints on asset weights as a method of combining performance and tail-risk control. We apply this methodology to a test portfolio of stocks comprising a major market index; the Dow Jones.
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