Sometimes, you many not be able to directly buy an index or access a strategy because of regulatory hurdles, mandates etc… In these situations, replicating it using a basket of accessible securities might make sense.
For example, you can replicate the S&P 500 index using Indian indices: NIFTY 50, MIDCAP SELECT and NIFTY BANK.
In fact, you can do for any reference timeseries (strategies/funds) and apply leverage as desired.
Also, the loadings give you and idea of the shifting relationship between the reference asset and the basket over time.
There are a number of ways to construct low-volatility portfolios. You could either use a bottom-up approach of selecting individual stocks that have low-volatility or you could you could run them through a portfolio optimizer (Low Volatility: Stock vs. Portfolio) to get target weights. However, if you are trading a single index, then you could use its own volatility to scale your exposure up and down. The advantage here is that if you trade index futures, you can set the volatility and leverage dials to the risk that you are most comfortable with.
Let’s take our own NIFTY 50, for example. Calculate the std-dev of daily returns over a sensible window. The index exposure is simply the ratio of the median std-dev vs. the current std-dev. Use a scaling factor (tvf) to further fine-tune the risk. To reduce transaction costs, rebalance once a week.
A tvf of 0.25 has roughly half the returns of buy & hold but with superior risk metrics that makes it receptive to leverage.
The problem with this approach is that the weights are continuous. What if you want them discrete so that it directly maps to how many lots of NIFTY you need to trade?
Here, we bucket the std. dev. into quintiles and use that to set our exposures in discrete steps.
At 2x leverage, you will outperform buy & hold by 5% with only half its drawdown.
The same for NIFTY MIDCAP SELECT looks like this:
The stats for this index looks worse than buy & hold. However, volatility sizing has resulted in lower drawdowns.
Stats and charts for different indices and code are on github.
Previously, we discussed how overnight volatility is not necessarily the scary boogeyman that it is made out to be. However, what maybe true for the NIFTY may not be true for other indices. For example, commodity stocks could carry larger overnight risks than, say, FMCG stocks.
If you look at the median volatilities of the two indices, commodity stocks have larger close-close volatility than FMCG stocks. However, FMCG stocks have lower volatility in general, so not sure if the differences are meaningful.
What about QQQs (US tech) and XLE (US Energy)?
Most energy related reports are released during US market hours while earnings reports are not. That could explain why XLE relative overnight volatility is lower than QQQ’s? Also, weekend risks averaging less than daily and overnight risks is surprising as well.
Open sourced by Meta back in 2017, Prophet is a procedure for forecasting time series data. Here, we give it two years of monthly returns and use it to forecast returns one month forward. The forecasts are ranked and a portfolio is constructed for the forward month.
The results are not as bad as fitting a simple linear model but not that great either.
More often than not, simple models outperform complicated ones. Inspired by some recent academic research that showed that linear regressions yielded better momentum performance, we did a quick backtest to check if building a linear model through recent 12 and 1/3/6-month performance and creating a portfolio using its next-month predictions made sense.
Counter-intuitively, a naïve momentum strategy outperformed linear models.
This is not our first run-in with linear regressions. Our Dynamic Linear Model strategy simply regresses prices to a 45* line and ranks them based on goodness of fit.
Most of the time, of all the different ways to skin the cat, the simplest is the best one.
