In the last two articles I’ve discussed the benefits of rebalancing and 3 common ways people implement rebalancing strategies.

Discover the significant benefits of Opportunistic Rebalancing

I also mentioned there is one strategy that is far superior to what most people do.

It’s called Opportunistic Rebalancing and it’s been shown, in a study by Gobind Daryanani, to not only control risk really well but to also provide better returns by capturing more buy low and sell high opportunities than other rebalancing methods.

Before I go further, let me warn you. This article is quite a bit longer than my usual articles and it goes into some detailed examples. So you’ll have to concentrate a bit more, but once you understand the process, you’ll benefit immensely – so it’s worth the effort you put in.

Onward.

The idea behind Opportunistic Rebalancing is to look frequently but only rebalance when it’s profitable to do so. Traditional calendar rebalancing looks infrequently and rebalances each time it looks.

That’s inefficient.

It’s far better to constantly monitor what’s happening in your portfolio and then decide whether it makes sense to rebalance.

Think of it this way. If you were standing some distance from a baseball pitcher and he threw a ball at you, would it make more sense to look where the ball is more frequently or less frequently before deciding whether you need to get out of the way or not?

It should be obvious that looking more frequently will give better results, in the case of the thrown ball, more frequent looking will result in you being hit less often.

Calendar rebalancing is like closing your eyes and only opening them once every, say, 20 seconds or so. However if the pitcher throws a ball every 2 seconds, you end up missing 9 out of 10 opportunities to take evasive action.

Opportunistic Rebalancing is similar to keeping your eyes open most of the time. You can see when the pitcher throws the ball and follow the ball’s trajectory. Then you can decide if you need to move in order to avoid being hit. You react based on the most current information rather than trying to guess when the ball will be thrown or where it currently is.

Opportunistic Rebalancing reacts to what stock prices do. It doesn’t try to predict when to rebalance but only rebalances when prices move in such a way that it is profitable to do so.

The Daryanani study showed that an Opportunistic Rebalancing strategy more than doubled the calendar rebalancing benefits over a wide range of market conditions.

Why?

Well, it’s been long known that although stocks sometimes experience short-term momentum, they will eventually revert to some mean value. In other words, when a stock’s price is moving up, it tends to continue for a short time but then falls back to its mean value.

The opposite is also true much of the time. A stock’s price will fall for a short time and then rise back to its mean value.

So stock prices tend to overshoot (or undershoot) their mean values before reverting.  These price movements are actually short-term noise created when people overreact to good or bad news.

Therefore calendar rebalancing is at a disadvantage because it can’t take advantage of this noise much of the time, while Opportunistic Rebalancing can.

So what exactly is Opportunistic Rebalancing?

It’s a strategy similar to the relative threshold rebalancing strategy I described in my last article, but it adds a new parameter called the tolerance band.

Like threshold rebalancing, if a stock is outside its threshold, it is rebalanced. Unlike threshold rebalancing, however, the stock is rebalanced to somewhere within the tolerance band, not to its initial allocation.

In addition, only stocks outside their respective tolerance bands are rebalanced. This means there’s a higher probability not all stocks will need to be rebalanced, thus the number of trades required to complete the rebalancing is reduced.

In essence, the tolerance band provides some wiggle room for stock prices to move around without having to be rebalanced. It acts as a buffer that filters out unprofitable rebalancing activity.

Of course this begs the question of how to set the threshold and tolerance band. Daryanani’s paper provides a comprehensive look at how he optimized and tested various settings, so I won’t repeat everything here (if you’re interested in how he arrived at his final results, you can read his paper).

Instead I’ll simply give you his results.

According to Daryanani’s research, Opportunistic Rebalancing works best over different kinds of market conditions when the threshold is set to 20% and the tolerance band is set to 50% of the threshold (so a 20% threshold will give a 10% tolerance band).

In addition, the interval (which is the number of times we look at the portfolio) should be set to biweekly, so we look at the portfolio once every two weeks but only rebalance if at least one stock’s allocation is outside its threshold – in which case we rebalance by bringing ALL stocks whose allocations are outside their tolerance bands (note this is not their thresholds, but their tolerance bands) back to within their respective tolerance bands.

I’ll clarify with an example. Suppose we have three stocks in our portfolio. Stock A makes up 50%, Stock B comprises 30% and Stock C’s allocation is 20%.

So Stock A would have a threshold of 10% (20% of 50%) and a tolerance band of 5% (50% of the threshold). Similarly Stock B’s threshold would be 6% with a tolerance of 3% and Stock C’s threshold would be 4% with a tolerance of 2%.

To put this into concrete terms, we would rebalance if Stock A’s allocation rose above 60% (that is, its initial allocation of 50% plus its threshold of 10%) or if its allocation fell below 40% (its initial allocation of 50% minus its threshold of 10%).

Similarly we would rebalance if Stock B’s allocation rose above 36% or fell below 24% or if Stock C’s allocation rose above 24% or fell below 16%.

Any of these would trigger a rebalancing event.

Now, once a rebalancing event has been triggered, we look at all stocks whose allocations are outside their associated TOLERANCE BANDS. Note a stock’s allocation does not have to be outside its threshold to be rebalanced (of course one stock’s allocation will be outside its threshold – that’s how the rebalancing event was triggered in the first place), but the important point here is that other stocks whose allocations are outside their respective TOLERANCE BANDS will also be rebalanced.

Stocks whose allocations are within their tolerance bands will not be rebalanced.

Let’s say Stock C’s allocation rose to 27% (above the 24% threshold necessary to trigger a rebalancing event), Stock A’s allocation rose to 54% (well within its threshold) and Stock B’s allocation fell to 25% (again, within its threshold).

Because of Stock C, however, a rebalancing event is triggered.

So we now have to determine which stocks need to be rebalanced. To do this, we check to see if a particular stock’s allocation is outside its tolerance band.

Stock A’s tolerance band is from 45% to 55% (5% on either side of its initial allocation of 50%). Since Stock A’s allocation only rose to 54%, it is within its tolerance band and therefore does not have to be rebalanced.

Stock B’s tolerance band is from 27% to 33% (3% on either side of its initial allocation of 30%). However since Stock B’s allocation fell to 25%, it is now outside its tolerance band (although it’s still within its threshold) and therefore needs to be rebalanced.

And of course Stock C’s allocation of 27% is now outside its tolerance band of 18% to 22% (and also above its threshold of 24%), so it too will have to be rebalanced.

Therefore we would rebalance Stock B to within its tolerance band of 27% to 33% and rebalance Stock C to within its tolerance band of 18% to 22%. Stock A would not be rebalanced.

Notice there is some wiggle room on how to rebalance Stock B and Stock C. We don’t have to reset their allocations exactly to their initial allocations. So we have some flexibility to bring the allocations anywhere within their respective tolerance bands. This allows us to rebalance more efficiently. We could, say, rebalance Stock C by selling shares to bring its allocation to 18% and using the proceeds to purchase shares of Stock B so its allocation is now 33%. Or we could bring Stock C’s allocation to 22% and Stock B’s to 27%, or any other combination that works with the cash we generate from the sale of Stock C.

There’s also one more thing you can do to wring out even more rebalancing benefits and I talked about it in my article on diversification. By holding stocks that are uncorrelated, rebalancing takes advantage of the increased number of buy low and sell high opportunities and therefore potentially increases your portfolio’s returns.

But let’s go back to Daryanani’s paper and summarize some of the benefits. He found that Opportunistic Rebalancing controls portfolio drift, significantly increases returns, reduces trading costs, reduces risk and provides better performance than simple calendar rebalancing.

Of course it takes more effort to implement an Opportunistic Rebalancing strategy than it does for other rebalancing strategies, however the benefits are well worth it.

I’ve built Opportunistic Rebalancing into my Pragmatic Investor software, so if you use this software, you automatically receive its benefits.

However you can implement this strategy for yourself with a spreadsheet or even a pencil and paper. Regardless of whether you choose to implement Opportunistic Rebalancing or not, it’s a good idea to ensure you always use some sort of rebalancing strategy.

Your portfolio will thank you for it.

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