Tactical methodologies have a number of different factors which influence their results. Making a small change to any factor can drastically change the results of the methodology. For example, most trend following methodologies have a lookback period and a rebalancing frequency. The lookback period can either be long or short and the rebalancing frequency could be monthly, weekly quarterly, daily, etc. In a previous post we talked about the concept of tactical duration where longer lookback periods and rebalancing frequency models will be less sensitive to market movements and shorter term models will be more sensitive. We also recommened that multiple lookbacks and rebalancing frequencies be combined, in either an approach that allocates to all of them or in an approach that dynamically shifts based on the market environment. Doing this smoothes out the return stream but it does very little to reduce the risk of when you actually rebalance.

Any strategy that rebalances must deal with rebalancing risk. Take two identical 60% stock and 40% bond strategies that both rebalance annually. Strategy 1 rebalances on the first trading day of the year and strategy 2 rebalances on the second trading day of the year. Assume that stocks had a good year and bonds had a horrible year so both strategies end the year 80/20 and must rebalance back to 60/40. Strategy 1 sells 20% stocks and buys 20% bonds on the first trading day of the year. If on the second trading day of the year the market crashes then strategy 1 will substantially outperform strategy 2 solely based on the rebalance date. Since tactical strategies often have larger and more frequent rebalancing this risk is especially important to address.

This risk can be reduced by using rebalance date diversification. The concept is fairly simple, lets take a tactical model that rebalances monthly on the last trading day of the month. This type of model obviously has a great degree of rebalancing risk as it has 12 fixed rebalance periods per year. A way to smooth this out would be to split the model into 4 equally weighted portfolios. Portfolio 1 would rebalance after the first week of the month, portfolio 2 would rebalance on the second week, and so on. The final portfolio would be the average of these weekly rebalances.

Of course rebalancing risk can hurt or help performance. In the example above strategy 1 would not have taken issue with the choice or rebalance date. However, not diversifying rebalance dates subjects portfolios to large swings that can go either way. Using rebalance date diversification can smooth these swings out and provide for a better investor experience.