Category: Investing Insight

Investing insight to make you a better investor.

Be Wrong. But Be Wrong With Confidence

I recently listened to this a16z podcast, A Guide to Making Data-Based Decisions in Health, Parenting… and Life, that got me thinking on how common personal finance and investing is with diet, exercise and parenting.

They all defy attempts at formulating universal laws.

Take the case of breast-feeding, for example. A truly scientific study on its benefits cannot be done. The data-set of identical or fraternal twins where the mother decided to breast-feed one and not the other for no particular reason (i.e., randomly) is simply not big enough to draw meaningful conclusions. We cannot isolate effects. What we observe are correlations. But correlation is not causation and most of them end up being spurious.

In parenting, oftentimes, we only have a sample-size of one. Most parents are guided by what their parents did and them their’s and so on. Most parenting “rules” are driven by societal norms rather than data. When I was a kid, the doctor advised by parents to substitute water with Coke or Pepsi when they traveled, which was quite often. The rationale was that water could be contaminated whereas bottled drinks are safer. Now, as a new parent, I find myself spending an inordinate amount of time trying to keep my kid away from food with added sugar. So, were my parents or doctor wrong? Should they have rolled the dice on possibly dirty water rather than sugar-water? There is simply no way to know for sure – there is no counterfactual. They simply made the best choice they could with the data that they had. Just like how I am doing with mine.

Try changing your diet. It is possibly the most difficult thing to do. We all know that vegetables are good, processed food is bad, and that we should stay away from sugar and deep-fried food. And yet, most of us will fail at being 100% healthy eaters. Why? Because a diet is a habit. In order to change it, you have to first change the environment, get rid of triggers and replace old shopping and consumption patterns with entirely new ones. A challenge that many of us find insurmountable.

Personal finance and investing have the same problems: more data is not necessarily better, there is no easy way to isolate effects, there is no counterfactual and people have hard but unstated preferences.

Your savings rate is like your diet. An advice to “save more” is as useful as “eat healthier.”

It is easier to believe that vaccination causes autism – a false but bold claim, made authoritatively – than to dive in to the messy data and statistics that back the benefits of vaccination. Just like it is easier to believe in “risk-free” trading strategies than to study one’s probabilities and sequence of returns.

Leading a healthy lifestyle only means that there is less of a risk that you will develop diabetes or get cancer decades later in life. Not zero. And neither is that risk quantifiable. Just like it takes decades for even the strongest investment strategies to play out.

It is also a lot like parenting. You will never know for sure whether you are making the “best” choice for your kids. You are only kind of sure of it once they grow up and start making their own choices.

But thankfully, there is a spectrum of good choices that can be made. The rest is up to the complex-dynamic beast of a system that we call markets… and life.

It Is Not Your Father’s NIFTY

Here’s something to keep in mind while using historical index returns to draw conclusions about the future: the composition of the index keeps changing. The shifting effects of industry dynamics and market cycles should not be ignored.

NIFTY 50 in 2009:
NIFTY 50 sector weights in 2009

NIFTY 50 in 2019 (10 years later):
NIFTY 50 sector weights in 2019

Banks and financials now have a bigger impact on index performance. If you are looking for a more diversified exposure, the NEXT 50 is a better option:
NEXT 50 sector weights in 2019

Code and charts on github.

SMA Strategy Transaction Cost Analysis

In our previous blog post on using SMAs to trade ETFs (SMA Strategies using ETFs,) we saw how using SMAs reduced drawdowns and boosted returns. We also saw how our Tactical Midcap 100 Theme out-performed mid-cap mutual funds even after taking into account STT and brokerage costs. Given the increased interest in our newly launched Tactical Midcap 150 Theme, we added transaction cost analysis to our backtests to give investors an idea of what gross and net returns of different SMA look-backs look like over buy and hold.

Annualized Returns

SMA Strategy Transaction Cost Analysis
transaction cost = 0.2%


1) SMA strategies on the NIFTY 50 index do not produce excess returns over buy-and-hold. However, the 200-day SMA did keep an investor out of the worst of the 2008 drawdown at a reasonable cost.

2) For other indices, perhaps counter-intuitively, 20-day SMA beat 10-day SMA both in Gross and Net returns.

3) SMA strategies will under-perform buy-and-hold when markets are generally trending up. However, they will out-perform when markets turn negative.
NIFTY MIDCAP 150 TR.20.cumulative
NIFTY MIDCAP 150 TR-20.annual

The RETFMID150 ETF tracking the NIFTY MIDCAP 150 index, continues to be well traded on the NSE. You can access the SMA(20) strategy shown above through our Tactical Midcap 150 Theme.

Code and additional charts on github.

Factor Holding Periods for Excess Returns

The NSE has different “strategy” indices that reflect some well known equity factors like low-volatility, quality, momentum and value. They are all shown to out-perform the NIFTY 50 TR index since inception:
cumulative returns of NSE factor 'strategy' indices

However, the excess returns of these indices, like everything else in equities, is unevenly distributed. As an investor, it could get frustrating to watch their “quality” factor investment under-perform the plain-old NIFTY 50 over many months. So broadly, for a given factor/strategy, what should the minimum holding period be for an investor to see only a positive excess return?

holding periods and excess returns for different factor/strategy indices

Factors take time to work. The longer the holding period, the less frustrating the experience. Low-volatility and Quality have the shortest holding periods of 5 years. The Alpha and Value indices require about 10 years for investors to see only positive excess returns. Also, given the lack of liquid, low-cost ETFs and index funds that track these factor indices, investors have to also contend with STT and capital-gains tax if they go the DIY route.

The edge that statistical factors have over market-cap based indices are measured over decades and require investors to be patient.

Charts and code on github.

Statistics don’t lie. Narratives do.

First, a headline: As U.S. fertility rates collapse, finger-pointing and blame follow (WaPo)

Fact-check: it is true!
Chart from World Bank:

Looks alarming! The government should respond! Mandatory paid maternity and paternity leave! Write-off education loans so that youngsters can afford to start families!

But… could the aggregate reduction in fertility rate be explained by lower teen-pregnancy rate?

And lower infant mortality rates?

Corrected narrative: women are having lesser kids because they expect all of their kids to make it to adulthood. And are having them later in life. Don’t panic.

Before subscribing to a narrative about a statistic, it is important to first figure out why the statistic was created in the first place. The raw fertility rate statistic was probably created to figure out how many midwifes to train/employ if the trend held up. While the second one was setup to measure the efficacy of sex education in schools and the last one to measure the effectiveness of primary healthcare.

It is only when we go beyond the narrative and seek data that falsifies that narrative that we get the full picture. This is the fundamental difference between hypothesis testing and data-mining.