Well That’s A Bummer!

I find it rather annoying to waste my time. Unfortunately, it seems I have to tag that handle onto my threshold rebalancing strategy over the last ten year period. A quick recap: I follow a once and done rebalance strategy anytime my equity holdings drift 15% from their normal allocation. The idea is to capture upside during a rebound.

After a recent post about my experience doing a rebalance during the April ’25 “liberation day” tariff announcement, a comment from a fellow HD reader mentioned she used a 10% threshold. I speculated about which strategy would have generated better alpha but pointed out I was too lazy to try the comparison.

The idea stayed in my mind unresolved until I had the simple epiphany to ask my friendly AI Claude to have a stab at crunching the numbers for a side by side comparison of the two rebalance points over the last ten years. I duly received the results which proved interesting. Essentially they are neck and neck — 108.2% return vs 108.8% in favour of the 10% trigger point.

The fly in the ointment occurred when Claude pointed out that a 60:40 portfolio with an annual rebalance beat both, with a ten year return of 111%. It seems I’ve done a bit of faffing about for no good reason over the last ten years — all a bit of a bummer if you ask me!

I have to admit, it’s a bit of a black box situation. I truly have no idea of the maths methodology Claude used but I’m taking a leap of faith and assuming the results have some basis in reality. If nothing else, it might show the power the average investor now has to run complex calculations using AI on their investment portfolio.

Only five years ago I would have needed to pay someone for this type of analysis. And if I’m truthful, it would have been just as much of a black box result coming from an advisor. Maybe this is a genuine use case for AI for the average Joe — the ability to somewhat level the financial landscape. If I choose to, I could pepper an AI with questions about its methodology for hours; I’d be far too uncomfortable doing the same with an advisor.

I asked Claude to provide a short summary of its method and I’ve tacked it on at the end of the post. . Although it disregarded dividend reinvestment and used a flat bond scenario, which would skew the returns downwards, I assume that would translate reasonably equally across the projections.

So where will I go from here? I’m going to stick with my 15% threshold rebalancing, I’m too individualistic and stubborn: data informs my choices but doesn’t dictate my actions. The last ten years might not have provided any alpha if Claude is to be believed, but I’m going with my gut feeling that the period in question has been nearly one continuous bull market, and that’s probably doing a lot of the work in these results.

A sideways or sawtooth market is a different beast entirely; I feel that’s the environment where a threshold strategy should earn its keep, selling the spikes and buying the troughs. Maybe someday, if I’m feeling a bit bored, I’ll try to craft an AI prompt to test that theory… just not today.

(Methodology: Claude used real S&P 500 monthly closing prices from March 2016 to March 2026, sourced from public historical data. It modelled a simple 60/40 portfolio — 60% equity (S&P 500) and 40% bonds — with bonds assumed flat throughout. For the threshold strategies, a rebalance trigger fires whenever the equity position moves 10% or 15% from its last reset point in either direction, at which point the portfolio is reset once to 60/40. The annual rebalance version resets to 60/40 every March. No transaction costs, taxes, or dividends were included in the model. **Please note this is an AI generated paragraph.**)