We expect optimized portfolios of momentum stocks to perform better during market up-trends. During bears, we expect them to have lower drawdowns than the market. The Corona Virus Panic put these portfolios in through the wringer. Glad to report that they came out largely unscathed.
Our back-tests showed that optimized momentum portfolio would under-perform “raw” momentum during up-trends but should have lower drawdowns during down-trends.
Optimized momentum portfolios saved the investor about 3-4% in drawdowns compared to the “raw” momentum portfolio. May not sound like much in this instance but think about the cumulative effect over multiple market corrections when you invest for the long-term.
Overall, optimized portfolios delivered what they promised.
WhatsApp us at +91-80-26650232 if you are interested in knowing more about these strategies.
Typically, investors expect higher returns from high risk investments compared to low risk ones. However, realized returns are the opposite of what they expect.
This anomaly, where lower risk systematically results in higher returns, has spawned a number of “betting against beta” strategies. A common approach is to rank a universe of stocks by volatility and create a portfolio off the top-N. However, this approach could lead to a highly volatile portfolio if relative correlations are not considered.
1+1 = 0
Here are two stocks with their volatility plotted against time:
If you create a portfolio off these two stocks, what happens to portfolio volatility?
In the worst case scenario, where all the volatilities are correlated, portfolio volatility can end up being a sum of all component volatility.
If low-vol stocks can create a high-vol portfolio, can high-vol stocks create a low-vol portfolio? Yes! It all depends on how the volatilities are correlated.
In the best case scenario, portfolio volatility can be a very low constant value if the components are inversely correlated.
It doesn’t matter if individual volatilities are high or low. What matters is the correlation of volatilities.
A simple ranking of stocks will not help in creating a low-vol portfolio. What we need is a holistic approach that considers the correlation of volatilities and optimizes the entire portfolio.
One way to go about this is to use gradient descent. Start with a random portfolio and go in the direction that minimizes variance (min-var) or expected tail loss (min-ETL)
With a monthly rebalance, the chances of the portfolio getting trapped in a local-minima are low. And the backtest looks promising.
The NSE has a couple of strategy indices – the NIFTY Alpha 50 Index and the NIFTY100 Alpha 30 Index – based on historical CAPM alphas. The former selects 50 stocks from the largest 300 stocks whereas the latter selects 30 stocks from the NIFTY 100 index.
First, a look at a simple buy-and-hold strategy.
The alpha indices have out-performed the plain-vanilla NIFTY 50. However, what jumps out off the page is the sheer depth and length of the drawdows that these indices have made.
Even though they give vastly better returns than the NIFTY 50 index, the lived experience would be too painful for most investors. Is there a way to reduce these drawdowns while retaining most of the out-performance?
In a 2012 paper, Momentum has its moments, Barroso and Santa-Clara outline a way in which historical volatility could be used to reduce momentum crashes.
The basic idea is that momentum risk is time-varying and sticky. And, periods of high risk are followed by low returns.
To test this theory out on the Alpha indices, we first split the time series into halves. The first to “train” and the second to “test.” We need a training set because we are not sure what the appropriate look-back for calculating risk should be (we check 100- and 200-days). The test set is a check of out-of-sample behavior of the strategy.
The theory laid out in the paper, that periods of high risk is followed by periods of low returns, is true. Subsequent returns when std. dev. is in the bottom deciles show large negative bias. Also, perhaps indicating a bit of mean-reversion, returns after std. dev. is in the 7-9th decile, have fat right tails.
The next question is the appropriate lookback and deciles for calculating the std. dev. Running this on the training set, we find:
A strategy that goes long Alpha when std. dev. is in the 1-5 deciles side-steps severe drawdowns in the training set. Note, however, that it under-performed buy-and-hold during the melt-up of 2007.
Applying a 200-day lookback on both the indices over the test set, we find that the strategy continues to side-step drawdowns but no longer out-performs buy-and-hold by a large margin.
Using historical volatility (std. dev.) reduces drawdown risk in Alpha indices. But it comes at the cost of reduced overall returns over buy-and-hold over certain holding periods. However, given the magnitude of the dodge in 2008 and 2016, it is well worth the effort (and cost) if it helps keep the discipline.
Check out the code for this analysis on pluto. Questions? Slack me!
Indian mid-caps, represented by the NIFTY MIDCAP 100 TR index, has out-performed its large-cap peer, the NIFTY 50 TR index.
It has done so with higher volatility. Here is the rolling 200-week standard deviation of weekly returns of the two indices:
MIDCAP volatility has been persistently higher than NIFTY volatility in the past:
A portfolio of bonds and mid-caps should exhibit lower volatility than an all-equity portfolio. Here are the standard-deviation ratios for different allocations to bonds:
B05, for example, represents a portfolio of 5% short-term bonds and 95% MIDCAP index. As allocation to bonds increases, portfolio volatility decreases.
We see from the chart above that a 75% MIDCAP + 25% BOND portfolio has almost never seen volatility greater than an all NIFTY portfolio. So, what are we giving up in returns to reduce volatility? About 2% in returns:
On an annualized basis, the allocation portfolio gives up about 2% in returns compared to all MIDCAP portfolio and is on par with NIFTY’s.
After taxes and transaction costs, expect the allocation portfolio to under-perform buy-and-hold NIFTY.
NIFTY 50 volatility the last couple of years have been extremely low by historical standards. If you look at the rolling median of weekly returns over 50 weeks (about a year), you can see how the range has narrowed:
The standard deviation, a popular measure of volatility, has come down as well:
As the charts illustrate, the markets have been moving in tight ranges. And narratives have been built around it:
Global central banks (US, Europe, Japan) have been flooding the markets with liquidity, essentially writing a put on the market.
Markets have become less riskier thanks to increased regulations after the 2008 global financial crisis.
Investors have a new-found enthusiasm for “SIP it and forget it.” This, plus the NPS bid, has cushioned the NIFTY 50.
Increased liquidity in the derivatives market has allowed investors to buy volatility, thereby reducing the need to decrease risk by offloading equities in the cash market.
The majority government at the center has provided policy certainty and political scams have not paralyzed decision making.
Narratives can change overnight. And if the last few months have taught us anything, the market drives the narrative. Also, new investors have only seen a low volatility environment and think it to be “normal.” So any reversion to the old volatility regime would be a rude awakening. Are we really in a new world or is volatility about to revert to its longer-term mean?