Asset allocation is powerful: the famous Brinson, Hood, and Beebower study showed that asset allocation is responsible for 91.5% of pension funds’ returns. Not stock selection, not market timing.
Also, I need to put my money to work. I don’t have time for frequent trading. I don’t trust my fundamental analysis, and I know that if I don’t have a quantitative, rule based system my emotions will get the best of me and I will make bad decisions.
Asset allocation should be easy these days, with low-cost, liquid ETFs tracking everything from gold to international REITs.
The the million dollar questions is, as always, how do we determine how much of our money to allocate to what asset classes?
Adaptive Asset Allocation
I decided to implement what’s known as Adaptive Asset Allocation, an intuitive extension of the traditional Markowitz mean-variance model. Essentially, it makes traditional portfolio optimization more “adaptive” by using shorter term metrics as inputs instead of long run averages/standard deviations.
The portfolios are rebalanced monthly. There is only a universe of 10 ETFs (gold, bonds, REITs, equities, the usual). So trading and actually implementing these portfolios should be easy.
A strategy’s ease of use is worthless if it doesn’t make money. So how does it perform? To help answer that question, I tested several portfolio construction methods to use as comparison. Here are the (incomplete) results:
Equal Weighted Portfolio
Where all 10 ETFs are given an equal weight.
tl;dr: compared to equal weighted there is a higher CAGR, slightly higher Sharpe, much worse max draw down.
Where only the top 5 ETFs ranked by momentum are selected to be traded (equal weighted). The momentum effect has been shown to exist across asset classes and countries.
Risk Parity Portfolio
tl;dr: compared to equal weighted, CAGR is slightly higher, max draw down is smaller, Sharpe Ratio is higher
Where all 10 ETFs are included in the universe, but are weighted such that each position contributes the same amount of volatility to the portfolio (the entire portfolio has 100% exposure, i.e. the sum of the position weights equals one).
Momentum and Risk Parity Portfolio
tl;dr: compared to equal weighted, there is a much higher CAGR, smaller max draw down, much better Sharpe Ratio
Where only the top five ETFs are selected every rebalance based on their momentum, and the weighted according to risk parity.
Momentum and Minimum Variance Portfolio
Where the top five ETFs are selected by momentum, then weighted with a minimum variance optimization (weights that minimize the variance of the portfolio).
To be continued