Previously, we saw how buying the best performing sector and holding it for a month didn’t quite pan out. What if, we bought the worst performing sector instead? The “Dogs of Sector Spiders,” if you will.
While introducing S&P 500 sector ETFs, we showed how the cross-correlations between them were unstable. This makes developing simple strategies challenging. One common momentum strategy is to simply go long whatever worked best in the previous period.
Rules of Rotation
For ETFs: XLY, XLP, XLE, XLF, XLV, XLI, XLB, XLK, XLU, and SPY
Calculate rolling returns over n months. Where n = 1, 3, 6, 12.
For the n+1th month, go long the ETF that had the highest return in Step 1.
In Step 2, if the selected ETF has -ve returns, stay in cash and earn zero.
We split the dataset into Before 2010 and After 2011.
Pick your Fighter
The Before 2010 dataset shows rotation by all look-back periods to be better than buying-and-holding the S&P 500.
Probably because of the prolonged dislocation caused by the GFC in 2008 and 2009, all rotation strategies based on the rules above exhibited great stats.
The SPY Rope-a-Dope
In boxing parlance, a “Rope-a-Dope” is
When you maintain a defensive posture on the ropes in an attempt to outlast or tire your opponent. It is most recognized and was actually given that name by Muhammad Ali when he employed the technique to defeat George Foreman.
State Street Global Advisors (SSGA) is known for its monster S&P 500 SPY ETF. With roughly $390 billion in AUM, SPY is one of the largest ETFs out there. Apart from SPY, they are also known for their SPDR “Spider” sector ETFs.
Materials Select Sector SPDR Fund
Energy Select Sector SPDR Fund
Health Care Select Sector SPDR Fund
Industrial Select Sector SPDR Fund
Consumer Discretionary Select Sector SPDR Fund
Consumer Staples Select Sector SPDR Fund
Financial Select Sector SPDR Fund
Utilities Select Sector SPDR Fund
Technology Select Sector SPDR Fund
Real Estate Select Sector SPDR Fund
Communication Services Select Sector SPDR Fund
These ETFs allow investors to take a concentrated bet on a specific sector and are often used as a benchmark for professionals who specialize in those sectors.
Markets go through cycles where some sectors do well and some don’t. Some sectors are extremely volatile and some barely move.
Cross-correlations are all over the place.
Given unstable cross-correlations and volatility, a naïve use of optimizers is a bad idea. For example, if you use these as inputs to generate an “efficient” portfolio, the weights wary widely from year to year.
Consumer Staples (XLP) ends up gobbling up most of the allocation, followed by Utilities (XLU.) The performance of such a portfolio depends on what you are looking for.
The efficient portfolio, given its large exposure to staples and utilities, has experienced lower drawdowns than both equity-weighted and S&P 500. This shows up in their Sharpe Ratios.
Momentum investing strategies have historically produced high returns in Indian equities. The biggest problem with them has been deep drawdowns when the markets enter bear territory.
A number of risk management strategies like using moving averages, trailing stop-losses, and hedging have been discussed on this blog before. These strategies, either standalone or in combination with each other, have provided investors with significant protection against momentum crashes. These are “exogenous” techniques, i.e. they are not part of the strategy itself but is imposed by portfolio management infrastructure. The advantage of these techniques is that the default is to be always invested in the market. It is risk-management’s job to control exposure.
Alternatively, endogenous risk-management techniques are those that are baked into the investment strategy itself. Our All Star strategy is a prime example of a momentum strategy that reduces exposure to equities by design. If enough stocks are not hitting their all-time-highs, it simply sits in cash. When you combine this with one of the exogenous risk-management techniques, you end up with a high Sharpe portfolio.
The advantage of high Sharpe strategies is that you can use leverage to amplify returns. However, if you are a “cash-and-carry” investor then it might be too conservative. Is there a momentum strategy that sits between All Star and traditional momentum?
High-to-Price (HTP) Momentum
A new paper, Büsing, Pascal and Mohrschladt, Hannes and Siedhoff, Susanne, Decomposing Momentum: Eliminating its Crash Component (SSRN,) outlines a new way to slice the 52-week momentum strategy to avoid crash risk. It describes a ranking system based on High-to-Price (HTP) where HTP = ln(Phigh/P0) where Phigh is the stock’s 52-week high price and P0 is its price at the beginning of the period.
A monthly rebalanced HTP long-only portfolio looks promising. It sidestepped quite a few whipsaws and has a better drawdown profile than NIFTY 50.
There have been periods where the strategy couldn’t find 25 stocks to go long and it only had a handful of positions or was all cash. However, the degree of overlap between constituents in consequent months is quite high indicating that the portfolio is likely to experience very low churn. In this aspect, it is very similar to the All Stars strategy.
At a line-item level, there have been instances where some stocks have tanked more than 30% in the month. However, the skew is, by-and-large, positive.
Investing in HTP Momentum
The real world doesn’t line-up perfectly to match the end-of-the-month rebalancing activity outlined in our backtest. To make this strategy investible, it needs to have some risk-management strategy in place on a clearly defined universe of stocks and has to be dynamically rebalanced.
We present our High-to-Price (HTP) Momentum Theme that consists of a portfolio of 25 stocks selected from the top 300 stocks by market-cap that rank high on their HTP scores. A 10% trailing stop-loss ensures that errant positions don’t drag down the whole portfolio. It is ideal for investors who can accept a bit more risk than All Stars for potentially higher returns.
The performance of the US portfolio has been gangbusters. It sidestepped the Corona Crash of 2020 and has been on a tear since then. The Indian experience, however, has been disappointing.
The Indian portfolio suffered from its inability to go into cash/bonds during crashes. Being fully invested took a bite out of its overall performance.
The Indian version comes up short even if you compare its stats with its component factor portfolios.
The intuition behind the Radar Plot above is that the larger the area under the points, better the strategy. Model Momentum is in pink and it pales in comparison to most of its constituents. Surprisingly, the Financial Strength Value Theme (light green,) that is rebalanced annually, beat out everything else.
What explains the underperformance?
Not being able to go into cash/bonds meant a larger hill to climb during recoveries. However, cash is a double-edged sword. If you get the timing wrong, you might end up going into cash after the bottom and watch the market recover helplessly. Unless the trend formation period is really short, cash is not a viable option.
High transaction costs can also be playing a role here. The difference between Gross and Net returns is ~15%. Not as high as a pure momentum strategy but not trivial either. Also, US portfolios do not incur STT and brokerages are essentially zero.
Maybe 20-months is too short a window to judge such a slow-moving strategy. The research looks solid and maybe all we need is to give it some time?