Author: shyam

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.

Are Stop-Losses Worth It? Part II

We saw how, in aggregate, stop-losses do not add any value to a momentum portfolio after taxes and transaction costs (Are Stop-Losses Worth It?) When you dig a little deeper into the actual positions that get stop-lossed and analyze their subsequent returns, we find that, on average, subsequent returns are not negative enough to justify trading costs.

During mid-2016 through April-2019 Bull and Bear phases

Here’s how the Momo (Relative) v1.1 Theme’s stop-lossed positions behaved 20-days after they got booted out, mid-2016 through April-2019:

Momo (Relative) v1.1.density-T20.2016-08-23.2019-04-24

Summarized statistics across different holding periods:
Momo (Relative) v1.1.density-table.2016-08-23.2019-04-24

Now let’s partition the total period into two. The first part covers the “bullish” market of mid-2016 through Dec-2017 and the second Jan-2018 through April-2019.

During the mid-2016 through Dec-2017 Bull

Momo (Relative) v1.1.density-T20.2016-08-23.2017-12-29

Summarized statistics across different holding periods:
Momo (Relative) v1.1.density-table.2016-08-23.2017-12-29

During the Jan-2018 through April-2019 Bear

Momo (Relative) v1.1.density-T20.2018-01-01.2019-04-24

Summarized statistics across different holding periods:
Momo (Relative) v1.1.density-table.2018-01-01.2019-04-24


When looked at from the perspective of a single position, a stop-loss removes red ink and is out-of-sight/out-of-mind. However, it is only when you look at their subsequent returns in the aggregate, that you realize that peace-of-mind comes at a cost.

During the bull phase, when the whole market was shooting higher, stop-lossed positions recovered from their losses. Note how the skew is slightly positive. Stop-losses here were a definite drag after taking costs into account.

During the bear phase, it does look like stop-losses helped – the subsequent returns of stop-lossed positions were skewed left. However, as we saw in our previous post, in aggregate, they did not add value after taking costs into account.