2014The Journal of Portfolio ManagementRequires access

Return Predictability and Dynamic Asset Allocation: How Often Should Investors Rebalance?

Himanshu Almadi, David E. Rapach, Anil Suri

Open publisher page 19 citations

Abstract

To exploit return predictability via dynamic asset allocation, investors face the important practical issue of how often to rebalance their portfolios. More frequent rebalancing uses statistically and economically significant short-horizon return predictability to aggressively pursue the dynamic investment opportunities afforded by changes in expected returns. However, the degree of return predictability typically appears stronger at longer horizons, which, along with lower transaction costs, favors less frequent rebalancing. The authors analyze the performance effects of rebalancing frequency in the context of dynamic portfolios constructed from monthly, quarterly, semi-annual, and annual return forecasts for U.S. stocks, bonds, and bills, where the dynamic portfolios rebalance at the same frequency as the forecast horizon. Along the transaction-cost/rebalancing frontier, monthly (annual) rebalancing provides the greatest outperformance when unit transaction costs are below (above) approximately 50 basis points, and dynamic portfolios based on annual rebalancing typically outperform the benchmarks for unit transaction costs well in excess of 400 basis points. TOPICS:Portfolio theory, statistical methods, portfolio construction

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To exploit return predictability via dynamic asset allocation, investors face the important practical issue of how often to rebalance their portfolios. More frequent rebalancing uses statistically and economically significant short-horizon return predictability to aggressively pursue the dynamic investment opportunities afforded by changes in expected returns. However, the degree of return predictability typically appears stronger at longer horizons, which, along with lower transaction costs, favors less frequent rebalancing. The authors analyze the performance effects of rebalancing frequency in the context of dynamic portfolios constructed from monthly, quarterly, semi-annual, and annual return forecasts for U.S. stocks, bonds, and bills, where the dynamic portfolios rebalance at the same frequency as the forecast horizon. Along the transaction-cost/rebalancing frontier, monthly (annual) rebalancing provides the greatest outperformance when unit transaction costs are below (above) approximately 50 basis points, and dynamic portfolios based on annual rebalancing typically outperform the benchmarks for unit transaction costs well in excess of 400 basis points. TOPICS:Portfolio theory, statistical methods, portfolio construction

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Available abstract

To exploit return predictability via dynamic asset allocation, investors face the important practical issue of how often to rebalance their portfolios. More frequent rebalancing uses statistically and economically significant short-horizon return predictability to aggressively pursue the dynamic investment opportunities afforded by changes in expected returns. However, the degree of return predictability typically appears stronger at longer horizons, which, along with lower transaction costs, favors less frequent rebalancing. The authors analyze the performance effects of rebalancing frequency in the context of dynamic portfolios constructed from monthly, quarterly, semi-annual, and annual return forecasts for U.S. stocks, bonds, and bills, where the dynamic portfolios rebalance at the same frequency as the forecast horizon. Along the transaction-cost/rebalancing frontier, monthly (annual) rebalancing provides the greatest outperformance when unit transaction costs are below (above) approximately 50 basis points, and dynamic portfolios based on annual rebalancing typically outperform the benchmarks for unit transaction costs well in excess of 400 basis points. TOPICS:Portfolio theory, statistical methods, portfolio construction

Key concepts: Predictability, Transaction cost, Portfolio, Basis point, Asset allocation, Bond, Econometrics, Context (archaeology)

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