Inspired by the Breaking the Market blog, I did a six month experiment with geometric balancing.

Geometric balancing is an investment strategy that revolves around frequent re-balancing of a portfolio of (hopefully) uncorrelated assets. The theory is somewhat complicated (I summarized some its blog posts here), but in practice it seems to simplify the investment process a lot. Similar to ergodicity economics, it is a powerful tool to better understand and handle complex decisions.

I was never a very disciplined investor. I didn’t develop a good framework for it. Most of the time, I focused on the wrong questions (how to time the market, which industries or stocks to buy, etc.). Investment decisions were based on a mix of emotions, vague intuitions and selective memory. Gut type decisions (see “The Hour Between Dog and Wolf”). Depending on the mood of the day, an investment decision was driven by my best or worst investment memories.

My investment results were very mixed and often caused a fair amount of anxiety. This anxiety increased my aversion to risk and ultimately made me lose interest in investing altogether.

I was looking for a framework to help me re-enter the market and came across geometric balancing on the Breaking the Market blog. The math was not always easy to follow, but I liked the simplicity of its general framework. On top of that, I was attracted by one of the strategy’s key objectives: lowering volatility. Given my risk aversion, I was curious what geometric balanced investing would “feel” like. I also wondered if I could replicate the results achieved by the blog’s author. You can read about his one year experiment with geometric balancing in his post here.

So I decided to give geometric balancing a six month test run.

Key benefit: less stress

Two quick points before getting to the benefits.

First, I could not have done this experiment without access to the Breaking the Market blog. The blog has many posts explaining the theory and provides daily asset weight suggestions. Without these, I would not have learned about geometric balancing or even run this experiment. So I owe a debt of gratitude to Breaking the Market. Of course, any mistakes and misinterpretations are my own.

Second, on the topic of mistakes and misinterpretations, this experiment was far from perfect. I made a bunch of trading mistakes and there were several practical limitations. During the first month, I maintained a small cash balance (I couldn’t yet trade on margin), even when the suggested cash position was zero. If a suggested trade was very small, I typically would not execute it. Time zone differences made things more difficult (I think Breaking the Market is based in the UK; I’m in the Asia-Pacific region). I was not overly disciplined on making portfolio changes at exactly the same time each day. In short, there were flaws. Hopefully they didn’t affect the results too much.

The key benefit of this experiment was a material, tangible reduction in stress.

Discipline was a major factor in reducing stress. A clear framework and trading discipline made investment decisions very simple. I didn’t have to think too much or second-guess myself. I just followed the suggested allocation on a daily basis and executed the trades.

Sometimes the suggested trades were in line with my intuition. Sometimes they were not. It didn’t matter. I just did what I was told to do. This new discipline made old intuitions irrelevant.

And that was a good thing. I quickly saw how often my old intuitions were wrong. A reminder that intuitions may not work that well in complex environments.

Less volatility was another major stress reducer. The market moved violently during the experiment, providing a real test for the strategy. But there were no stubborn attachments to positions or visions about the future. Market exits and entries were swift and emotionless. At the height of market volatility (mid-March 2020), the portfolio had 11% in stocks and 60% in cash. That seemed appropriate and felt good.

In terms of investment return, the strategy outperformed. I think that may be a fluke. It’s difficult, and perhaps meaningless, to analyze investment returns over such a short time period.

While the strategy’s ability to deliver an acceptable return is important, it is probably the most uncertain variable and least predictable. If you are going to drive a particular variable, it probably makes more sense to focus on volatility than returns.

Some issues to consider

Geometric balancing requires regular re-balancing. But what is sufficiently regular? The daily adjustments, while not a ton of work, felt a bit much. Three months ago, I started a portfolio where I make only weekly adjustments. After 3 months, the weekly geometric portfolio’s returns are about the same as the daily geometric portfolio’s returns. Its volatility is slightly lower.

Portfolio adjustments are based on asset class weights that are calculated by Breaking the Market. I think its calculations are based on returns, volatility and correlations, but it is a black box. To implement this strategy I need the daily weights. If Breaking the Market stops publishing these weights, I will not be able to use this strategy.

In times of extreme volatility, it helps to have more of a finger on the pulse. It probably makes sense to set alerts for extreme market movements. Once an alarm is triggered, the default will be to check Breaking the Market weights. If no information is available there, it may make sense to move some fixed percentage of assets in or out of cash.

Going forward

I will continue to use some form of geometric balancing. I may adjust some of its elements:

• Diversification – no changes:
  • Continue with index funds only.
  • Continue with stocks, bonds, gold and cash only.
• Re-balancing – small changes:
  • Switch from daily to weekly re-balancing.
  • Continue with weights suggested by Breaking the Market.
  • If unavailable, switch to a conservative Fixed Combo strategy.
  • Set alerts to handle extreme volatility.

Key takeaways

• Diversification: keep it simple.
  • Limited number of asset classes (stocks, bonds, gold).
  • Index funds (not individual stocks).
• Re-balancing: make it easy.
  • External, objective instructions provide useful discipline.
  • Less instinctual adjustments and second-guessing.
• Biggest benefit is lower stress.
  • Simple, easy decisions.
  • Discipline.
  • Less volatility.
• Moderate return expectations.

Summary results

The Geometric strategy had higher investment returns than an all stock strategy (“All Stock”) and a fixed diversified portfolio (“Fixed Combo”) strategy. The strategies are explained in more detail below. The Geometric strategy was also substantially less volatile than the other two strategies:

An interesting side note. These results illustrate that averages can be very misleading. In this experiment, the average daily return for the All Stock portfolio was +0.01%. And yet, the All Stock portfolio was down for the period.

Process: asset selection

Step 1: initial asset selection – diversification (3 index funds + cash)

First, invest in uncorrelated assets. Or, assets that are uncorrelated as much as possible. To keep it simple, I followed the example of Breaking the Market and invested in stocks, bonds, gold and cash. This post explains the logic of picking these assets classes.

Second, invest in index funds, rather than individual stocks. For stocks, I picked “SPY”. For bonds, I picked “TLT”. For gold, I picked “GLD”. These particular index funds seem the most liquid. They can also be traded outside of regular US trading hours  (important to investors outside of the US).

Step 2: ongoing asset selection – re-balancing (daily updates)

Re-balancing is at the core of this investment strategy. Every day, the appropriate weight of each asset class (stocks, bonds, gold, cash) is assessed. As suggested allocations on the Breaking the Market blog changed, I re-balanced the investment portfolio.

I made one or more changes to the portfolio on 84 days out of the 125 trading days in this experiment (67%). In total, I made about 217 individual trades. The most traded asset was TLT (75 trades).

The Breaking the Market blog also suggests a leverage factor. I didn’t apply any leverage.

Over the course of six months, this is what the investment portfolio composition looked like.

Results: returns – strategy outperformed, probably “lucky”

Individual asset class performance for the relevant period was as follows:

• Stocks (SPY): down 4%
• Bonds (TLT): up 18%
• Gold (GLD): up 13%

I was most interested in how geometric balancing performed against an all stock or fixed diversified portfolio. If I wouldn’t use geometric balancing, I would put 100% of the portfolio into stocks (and keep it there). Or, I would put some amount in stocks, bond, gold and cash and leave it unchanged (in this case, 66% stocks, 16% bonds, 16% gold and 2% cash). I would not invest 100% in bonds or gold, so I was not interested in comparing geometric balancing against bonds or gold.

The all stock strategy is labeled “All Stock” and the fixed diversified strategy is labeled “Fixed Combo”. The strategies performed as follows during the six month period:

• All Stock: down 4%
• Fixed Combo: up 2%
• Geometric: up 6%

 

The Geometric strategy ended up outperforming the All Stock strategy (by about 10%) and the Fixed Combo strategy (by about 4%).

I was curious to what extent this out-performance depended on the day the experiment started.

Quite a bit, it turns out.

Simulating different starting dates had a big impact. Against the All Stock strategy, the maximum out-performance by the Geometric strategy was about 11%. Close to the experiment’s 10% out-performance. So I was lucky in timing the experiment. The maximum under-performance was as low as 30%. This was of course against the All Stock portfolio started on March 23, 2020, the day the stock market reached its lowest point for 2020.

The range of relative performance is illustrated by the graph below. The x-axis shows the date each portfolio was simulated to start. The y-axis shows the relative performance of the Geometric strategy (against the All Stock strategy).

Each portfolio ran until June 30, 2020. Portfolios that started later had a shorter period to run. As you move right on the graph, results probably become less meaningful.

Compared to the Fixed Combo strategy, the maximum out-performance was close to 4%. Again, about the same as the result for the experiment. The maximum under-performance was negative 2%. These are meaningful differences in returns, but a much tighter range than for the All Stock comparison.

I’m not sure it makes sense to generalize any of these results. The measurement period (6 months) is short. The experiment took place during a period of very high market volatility.

Perhaps the takeaway is that if your investment objective is absolute investment returns, geometric balancing may not be your first pick.

Results: volatility – material reduction

This is where the Geometric strategy seems to be most effective.

The implied annual volatility for each of the strategies:

• All Stock: 44%
• Fixed Combo: 24%
• Geometric: 11%

The first building block of reduced volatility was diversification. The Fixed Combo strategy already achieved material volatility reduction. Its portfolio was kept fixed at 66% stocks, 16% bonds, 16% gold and 2% cash. This level of diversification was enough to reduce volatility by about half (versus the stock market’s volatility).

The second building block, frequent re-balancing, further reduced volatility. The re-balancing Geometric strategy takes volatility down by another 50% or so.

The starting date didn’t seem to have much impact on volatility for the All Stock and the Geometric strategies.

For the Fixed Combo, it mattered a lot. Different starting dates had different cash allocations (which stayed the same throughout the investment period). Portfolios that started with higher cash allocations had much lower volatility. This is shown in the table below. The March 20, 2020 starting date Fixed Combo strategy had 60% in cash, and a much lower volatility.