If you are looking for research on stock selection, you’re in luck — the research is everywhere and has arguably been overdone. Get started with Moon Cycles & Stock Market Returns and The Congressional Calendar & Stock Market Returns to get a sense for how esoteric the research has gotten. Hundreds of these papers have been covered on the Alpha Architect blog and you could spend a lifetime trying to read all the research on the topic.

But one topic, that is arguably just as important as security-selection,  has received very little attention: rebalancing portfolios.

In a prior post, I highlighted an interesting new paper by Brian Baker, Mike Dieschbourg, Damian McIntyre and Arun Muralidhar (BDMM) published on the topic.  In addition to the BDMM paper, there have been some key research pieces published on the topic:(1)

  • Vanguard – This paper shows that calendar based rebalancing strategies don’t improve performance and that tolerance band rebalancing strategies tend to add minimum value.
  • TD Ameritrade published in Journal of Financial Planning – This paper showed that rebalancing returns can be improved with 1) wider tolerance bands (20%) 2) Evaluating portfolios for rebalancing bi-weekly 3) Only rebalancing the asset(s) that is (are) out of balance and 4) Increasing the number of uncorrelated asset classes.
  • Michael Kitces – This post largely reinforces the TD Ameritrade Research and conclusions.

However, after reading these white papers and the BDMM paper there are some practical issues with theses papers:

  1. The TD Ameritrade paper and the BDMM paper use high frequency data (daily) and a short history (typically back to the mid 1990s).  The conclusion that tolerance bands are effective may be simply due to the the time period selected and may not be reflective of their superiority.
  2. The Vanguard paper uses a much longer data history and monthly data frequency, but only uses tolerance bands of up to 10%, which are more narrow than the tolerance band range that was shown to be best in the TD Ameritrade paper (20% tolerance bands).

The rest of this post explores the prior research and I look at new ideas that might improve portfolio rebalancing techniques.

How to Improve Portfolio Rebalancing Results

In addition to using/testing the conclusions of prior research, I wanted to see if I could improve upon the rebalancing results.  If you recall from my prior post, any decision to rebalance is an ACTIVE, MARKET TIMING decision…there, I said it. We are all market timers whether we’d like to admit it or not. Some of us will now choose to put our head in the sand and pretend they didn’t read the last sentence, but for the rest of us looking to embrace the intellectual challenges markets throw our way — it’s time to get our hands dirty!

If we are going to improve on current rebalancing results, we will have to look to research which has shown some ability to provide positive market timing results.  In my mind, the most robust of these strategies are associated with trend following. Here are some pieces on the topic which highlight the depth of evidence on the subject:

The list could go on, but the papers and posts, above, should provide a good starting place.

Data Used

To test various rebalancing strategies, I use Ibbotson Associates monthly data from 1/1927 through 4/2017.  Specifically, I use Ibbotson Associates US Large Cap Total Return series for US Stocks, Ibbotson Associates Intermediate Term Government Bond Total Return series for bonds and Ibbotson Associates 30 Day T-Bill Total Return series for cash.

For simplicity, I will only analyze a portfolio comprised of two assets, stocks and bonds.  To also keep the analysis simple, I will only look at one target asset allocation, 50% stocks and 50% bonds.

The benchmark portfolio will be a 50% stock and 50% bond portfolio rebalanced monthly.  This benchmark is chosen because it has the least variance in stock allocation over time, which should highlight any return improvement (or decrease) due to rebalancing decisions.

In addition, I ignore all transaction and tax costs.

Testing the 20% Portfolio Rebalancing Tolerance Band

One of the first strategies that I wanted to test was the 20% tolerance band rebalancing approach but using a longer data history (1/1927 through 4/2017).  This tolerance band approach was shown to be optimal based on the TD study.

The specific rebalancing rules I tested were as follows:

  1. Set the initial allocation to 50% stocks and 50% bonds on 1/1927.
  2. As long as the stock allocation stayed between 40% and 60% at month end, no rebalancing trades were placed.
  3. If at the end of any month the tolerance range was breached (either at an allocation of 60.01% and above or at an allocation of 39.99% and below), the portfolio was rebalanced back to the 50% stock and 50% bond target allocation.

The results of this strategy versus the benchmark monthly rebalanced portfolio are shown below:

Momentum And Tolerance Bands
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

What these results show are that 20% tolerance bands (when applied to a monthly frequency of reviewing potential rebalancing trades) does appear to improve the portfolio Compounded Average Growth Rate (CAGR) by about 0.24% per year as well as improve the Sharpe Ratio and a slightly reduce the maximum drawdown. However, the standard deviation of the portfolio and the sum of all drawdowns is increased.  This is expected as the portfolio allocation is allowed to drift more due to the tolerance bands. Of course, the most striking feature of the analysis is the # of trades: 1,084 versus 23. Clearly, the tolerance band approach is more efficient through minimizing the number of necessary trades.

Momentum Informed Tolerance Band

The next rebalancing strategy I tested is one of my own design, which I call Momentum Informed Tolerance Band (“20% Tol, Mom” in the following tables).  The idea behind this rebalancing strategy is to use absolute momentum in stocks to help inform (i.e., market time) what half of the tolerance band the stock allocation should be in.

The specific rules are as follows:

  1. If stocks have positive absolute momentum (stock returns are greater than cash returns over the prior 12 months), the stock allocation should be between 50% and 60% of the portfolio.  If it isn’t, the portfolio is rebalanced to either 50% (if under 50%) or 60% (if over 60%).  If the stock allocation is between 50% and 60% no rebalancing trades are placed.
  2. If stocks have negative absolute momentum, the stock allocation should be between 40% and 50% of the portfolio.  If it isn’t, the portfolio is rebalanced to either 40% (if under 40%) or 50% (if over 50%).  If the stock allocation is between 40% and 50% no rebalancing trades are placed.

The results of this strategy (as well as previous ones) are shown in the table, below:

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