Optimizing Ivy Portfolio
Early on I posted a simple live version of GTAA strategy. It demonstrated the effectiveness of the Ivy Portfolio (M.Faber, 2009) rationale in recent market with a small sample. Again, the rationale is very simple and powerful: screen a wide range of asset classes each week/month, then invest in those that have shown the strongest momentum. Last time I tracked 39 ETFs’ 9-month SMA and equally allocated portfolio assets to the top 8. Although I got pretty good results, the sample was relatively small and those ETFs are quite different in terms of time of inception, liquidity, tracking error, etc.
And above all, equal allocation seems a bit, for lack of a better word, boring. This time I want to use a more general sample to see how we can improve this by implementing some optimization strategies I’ve shown in my previous post Backtesting Portfolio Optimization Strategies.
Some equipment check before we launch the test.
1. SPX Index: S&P 500 LargeCap Index
2. MID Index: S&P 400 MidCap Index
3. SML Index: S&P 600 SmallCap Index
4. MXEA Index: MSCI EAFE Index
5. MXEF Index: MSCI Emerging Markets Index
6. LBUSTRUU Index: Barclays US Agg Total Return Value Unhedged USD (U.S. investment grade bond)
7. XAU Curncy: Gold/USD Spot
8. SPGSCI Index: Goldman Sachs Commodity Index
9. DJUSRE Index: Dow Jones U.S. Real Estate Index
10. GBP Curncy: GBP/USD Spot
11. EUR Curncy: EUR/USD Spot
12. JPY Curncy: JPY/USD Spot
13. HKD Curncy: HKD/USD Spot
1. Rebalance monthly
2. Rank 12-month SMA; invest in the top 3
3. For each asset, minimum weight = 5%; maximum weight = 95%
4. Use CVaR optimization to construct the portfolio each month; confidence level = 1%
Fortunately, our test didn’t fall apart and crash into the Pacific Ocean. The CVaR model seems did a good job improving the original strategy. However, it has to be pointed out that not all optimization models are better than an equal-weighted one. As demonstrated below, the minimum-variance and maximum-sharpe ratio models didn’t make much difference.