- Wealth PMS (50L+)
A review of the Capitalmind Momentum Portfolio, how it did in 2021 and thinking about 2022.
The portfolio had a strong December with +7%, comparing favourably to the NIFTY +2.2% and CNX 500 +2.4%. Overall, for 2021, Capitalmind Momentum did +83% compared to 24% and 30%, respectively, for the two market indices.
We have been using smallcase published NAVs (Net Asset Values) to discuss performance of the Momentum portfolio. This has worked well to show portfolio movement against benchmarks on a day-to-day basis. However, we observed that the returns calculated using smallcase NAVs seemed stretched compared to realised returns for most investors. We wrote about this in detail a few months ago, even highlighting the likely difference in realised return versus the smallcase-reported CAGR.
We are now adding our internal calculation (CM NAV in the charts in this post) to how we talk about performance. The CM NAV calculation assumes closing prices as effective entry and exit prices on the day of the rebalance. We will retain the smallcase return calculation to explain how the two differ.
The chart shows Capitalmind Momentum smallcase returns versus the NIFTY 50 and the CNX 500.
Monthly returns in 2021 compared. Click on individual months for comparison.
Overall, Capitalmind Momentum had a productive year compared to the benchmarks.
The chart shows performance (annualized returns, annualized volatility, and maximum drawdown from peak) since inception in January 2019.
Reading this chart: Annualised Returns, higher the better (obviously), Volatility: lower the better, and Maximum Drawdown: measured as falls from the previous peak, lesser the better, i.e. the smallest negative value, the best possible value is zero only possible for FDs.
The Momentum portfolio tries to outperform the NIFTY while suffering lower drawdowns in corrections. The smallcase version of the portfolio has been live since Jan 2019, and even with adjustment for realistic returns as shown by the CM NAV metrics, it has comfortably outpaced the benchmarks with lower volatility.
Summer 1980: Company A set up by a pioneering computer programmer, dominates the market for operating systems for personal computers running on Intel microprocessors. They are approached by Company B, the largest computer maker in the world, looking to launch its first personal computer. The rest is history.
Company B, you’ve no doubt guessed, is IBM. Company A is not Microsoft.
When IBM decided it was time to enter the still nascent but fast-growing personal computer market, they set themselves a stiff deadline. To meet that deadline, they decide to outsource the operating system for their first PC. They first go to Company C, who, not being in the business of operating systems, refers them to Company A, the maker of the market-leading personal computer operating system. The IBM team flies to Seattle to meet with Company A
There are varying historical accounts of what happened at the meeting, but the two do not arrive at an agreement. Under pressure to launch in a few short months, the IBM team flies back to Company C asking if they can deliver an Operating System in time. Even though they had never built one, Company C founders agree.
They acquire the rights to a look-alike of Company A’s product, tweak it, rename it as PC-DOS. It launches with the IBM PC.
Company C, which should now be clear, is Microsoft. Company A is Digital Research, its founder Gary Kildall was one of the first to recognize what microprocessors could do to personal computing.
Imagine if that 1980 IBM-Digital Research meeting had gone differently.
When asked how much of his success he would attribute to luck, Bill Gates allowed that it played “an immense role.” “Our timing in setting up the first software company aimed at personal computers was essential to our success,” he noted. “The timing wasn’t entirely luck, but without great luck, it wouldn’t have happened.”
Luck plays a role in all our outcomes. We know this intuitively. But except at the extremes, we don’t know how much it drives any given outcome. You could place any activity and its outcome on a continuum from pure luck to pure skill.
Winning the lottery is clearly based on Luck. Luck, however, won’t help you beat Magnus Carlsen at chess or Novak Djokovic, vaccinated or not, on a tennis court. That needs Skill—lots of it.
Most outcomes, though, don’t lend themselves to be placed at the extremes. There is a, let’s call it, “the fuzzy middle”, where most things we do lie, where a combination of Skill and Luck determines the outcome.
Where does Investing fall on the luck-skill continuum?
Is Investment performance all SKILL?
Investment managers riding a hot streak will tell you, and themselves, that their “excess” return over the benchmark is all skill and no luck.
That notion is easy to debunk.
Let’s assume investment returns relative to their benchmark is completely about the manager’s skill.
Think of some of the most skilful investors in history: George Soros, Peter Lynch, Paul Tudor Jones, Warren Buffett. If you don’t agree with these names, pick your own.
While they’ve outperformed over their careers, they’ve all underperformed the market at times, sometimes by a lot. We can safely assume they never wanted to underperform even in the short term, yet they often did.
Either their skill levels varied greatly from one year to the next, which would be antithetical to the concept of skill, or luck was involved to some extent in their performance.
Funnily, the same investors who most discount the role of luck in great performance say the market just doesn’t get it yet, when things go the other way. In other words, they say they were unlucky.
Luck plays a role in investment performance, sometimes for, sometimes against.
Is Investment performance all LUCK?
Advocates of passive investing say active management can only underperform net of costs. That is a way of saying investment performance is mostly luck and not much skill.
How likely is that skill has no bearing on investment outcomes?
Michael Mauboussin, in his incredible book, The Success Equation: Untangling Skill and Luck, applied this unintuitive test:
If you can not lose at something on purpose, then it’s all luck, else there is some skill involved.
You can not lose on purpose at Roulette, or the lottery, which makes them all about luck. You can lose on purpose at Poker, which means there has to be some skill involved.
Note, being able to lose on purpose only proves an activity is not all about luck, that some skill is involved, not how much.
Can you “lose” at investing, i.e. underperform the market, on purpose?
Investing: Combination of luck and skill
It seems Investing falls somewhere in that fuzzy middle on the Luck-Skill continuum.
Mauboussin explains the difference between assessing activities that are mostly Skill versus those that have a generous dose of Luck:
“When an activity is mostly skill, we need not worry much about the size of the sample unless the level of skill is changing quickly. For activities with a good dose of luck, skill is very difficult to detect with small samples. As the sample increases in size, the influence of skill becomes clearer.”
“Larger samples do a better job of revealing the true contributions of skill and luck.”
For a sportsperson, larger samples mean more games. For investment performance, larger samples mean more time.
Consider the chart below summarising “alpha” or outperformance over NIFTY TRI of 37 Flexicap funds over 1-year / 3-year / 5-year timeframes.
If we look back one year, the median Flexicap fund underperformed the NIFTY by 1.7%. The best-performing fund outperformed by 11.1%, while the worst-performer underperformed by a whopping 20%. That is a massive range between the best (Parag Parikh Flexicap) and worst (Motilal Oswal Flexicap) performers.
If that difference represents the gap in skill between the best and worst fund managers, it should persist over longer durations. Remember, the chart tracks the difference from the passive benchmark, not absolute return.
But as the time horizon increases, the range of best to worst-performers contracts. From 31% best-worst range over one year to 20% over three years to 13.5% over five years.
The funds with extreme positive or negative outperformance see their distance to the median shrink as the role of luck relative to skill diminishes over longer time frames.
The chart below is a visual albeit unscientific representation of how to attribute the contribution of Luck and Skill to investment performance.
Over short time horizons, performance (alpha over a passive benchmark) is explained more by luck than skill. As the time horizon increases, skill contribution to cumulative performance increases relative to luck. Note the changing attribution applies to the cumulative, i.e. compounded performance, and not performance between any two arbitrary dates. However, luck as a factor of investment performance never goes to zero; else, skilful investors, assuming they exist, would never see phases of underperformance, and absolute novices would not see bumper returns some of the time.
The shorter the time period, the less certain you can be whether luck or skill played the major role in portfolio performance. The longer the time period, the harder it is to deny the role of skill in portfolio performance.
When comparing portfolios, all else being equal, a 10-year track record with 5% annualised outperformance is a stronger indicator of fund-manager skill than a 1-year track record with 25% outperformance.
Capitalmind Momentum has been around for three years now in its current shape and form. With a generous dose of luck, it has outperformed market indices by a distance. Base rates suggest that magnitude of outperformance rarely persists indefinitely. This is why we spend an inordinate amount of energy tempering expectations (see Further reading section):,
Our objective remains to outperform net of costs.
2022, Bring it on.