This research shows that Naïve rebalancing may not be able to improve your strategy’s performance or risk profile, and proposes some alternative ideas for “intelligent” rebalance.

In this paper, we will just focus on the robo-advisors and argue that one key activity underlying these platforms and even practiced very broadly in institutional investing and defined contribution funds, naïve rebalancing, is predicated on bad theory and myths and fails the key test of whether it truly improves performance and/or risk management. We will demonstrate that many previous studies of rebalancing examine this issue from a simplistic perspective of a two-asset portfolio and a simple measure of risk, volatility. When we examine these strategies from the perspective of a multi-asset portfolio and a broader set of risk statistics (that a sophisticated investor would apply), then these naïve rebalancing strategies are nothing more than a form of poor market timing and the performance is a coin-toss and the risk profile of the portfolio is typically worsened. Once these flaws of naïve rebalancing are exposed, we suggest that investors would be well advised to adopt a more intelligent form of rebalancing, where an intelligent analysis is conducted of the relative attractiveness of assets to then set allocations within a client’s policy ranges. This approach has a higher likelihood of improving performance and risk management and the ideas to implement such a program are in the public domain.

Source: The Mythology of Rebalancing: A Random Walk down Performance and Risk Management by Brian D Baker, Mike T Dieschbourg, Damian McIntyre, Arun Muralidhar :: SSRN

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