A Multi-Asset, All-Weather Strategy for Stable Performance
Who could have imagined that as soon as the COVID-19 pandemic had subsided we would be in another crisis (Russia-Ukraine) that will threaten a slow global economic recovery? In fact, if you check out the last decade or two you shall realize that crises are much more frequent and painful than in the eighties or nineties. It is very important for an investor to diversify across asset classes and build an all-weather portfolio to reduce the pain of these shocks. In this article, I will discuss the approach of a quants-based multi-asset, the all-weather investment strategy for the Indian market offered by FinSharpe. I will cover the following:
- Asset Class and Investment Vehicle Selection
- Portfolio Creation and Backtesting
- Performance Evaluation
Asset Class and Investment Vehicle Selection:
As far as modern portfolio theory goes an investor should invest in all investable assets, across asset classes and also diversify within the asset class. However, as an investor, we normally follow bounded rationality and select the asset classes which satisfy and suffice (satisffice) our needs. While deciding, we should keep in mind that our selection of asset classes and respective investment vehicles should bring in diversification benefits. Considering these key parameters I decided to have Indian equities, Indian debt, and Gold ETF in our portfolio.
Now that we have three different asset classes finalized, let’s decide the weight range for each class and investment vehicle. The risk profile for the investor plays an important role in this process. In our case, we are looking for suitability for a moderate to low risk investor with stable return expectations over the long term hence equity will take the highest possible weight then debt, and finally gold. The table below provides detail in this regard.
I have chosen the Optimal Alpha strategy as an investment vehicle for Indian Equities. It is a multi-factor, diversified strategy offered by FinSharpe (please click the link to get details).
- The expected return — I have used mean returns for the last 2 quarters for each asset class.
- The covariance matrix — I have used Ledoit-Wolf shrinkage estimator.
- The variances of each asset class.
- The weight constraints — The minimum and maximum weight range for each asset class as specified in the above table.
Based on these inputs the MVO optimization will spit out the weight of each asset class. The weight we get for the Nifty 200 will be used for the Optimal Alpha portfolio. The weights corresponding to debt and gold will be used for Sbi Magnum Gilt Fund and Reliance ETF Gold BeES (GOLDBEES) investment vehicles.
Performance Evaluation:
Now the entire model is backtested with quarterly rebalancing using Backtrader for multiple time periods including 10 plus, 7, 5, 3, and 2 years to ensure that performance is consistent across time periods. I have shared below the backtesting results for 5 years from 6 January 2017 to 15 February 2022 against the Nifty 500 Index.
The performance results look impressive with a CAGR of more than 22% against 16% for the benchmark (Nifty 500). The Sharpe and Sortino ratios are much higher, Max Drawdown is just around 17% against almost 38% for the benchmark. The longest DD days are 269 against 497 for the benchmark. Annual volatility is 14.2% against 17.77%.
I have also shown a sample portfolio above. SBIGFG is depicted for Sbi Magnum Gilt Fund and NIGETF is depicted for Reliance ETF Gold BeES (GOLDBEES). There is no doubt that having a multi-asset portfolio has the potential to improve diversification, reduce the risk and provide consistent stable returns over the long term. As I mentioned above this is more suitable for a low to moderate risk investor who is looking for diversification and a shockproof portfolio.
Thank you!
PS: The sample portfolio is just for illustration purposes and not to be used for actual investment.
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