Author: shyam

Country ETF Returns 2018

There are over 1300 equity ETFs listed in the US. Of these, a fair number are country specific market-cap ETFs. With 2018 almost over, here’s how various country ETFs performed:

Thankfully, there is also an “all country” ETF – VT – the Vanguard Total World Stock ETF. Here’s the chart of excess returns over VT:

Very few markets managed to stay positive this year. Interesting times indeed!

Code and charts are on github.

Annual Drawdowns and Subsequent Returns

All major world equity indices are hugely negative for this year. But is it really newsworthy? Here’s S&P 500 with all the 220-day max drawdown points marked:

You would soon run out of ink if you were to write up a report every time the market dropped the most in the past year. Here’s the one for the NIFTY 50:

And just because the index dropped doesn’t mean that subsequent positive returns are around the corner. Here are the charts for subsequent 220-day returns once a 220-day max drawdown has been made:

You have to squint really hard at the charts above to make a bull case after a drawdown.

We have systematically looked at these relationships quite extensively in the past. You can find them at our Buying the Dip and Market Timing collections. You will notice that the key ingredient is patience – it takes time for the odds to work in your favor.

Code and charts are on github.

SMA Distance, Part III – Backtest

In Part II, we saw that when the 50- and 100-day SMA Distance is in the first quintile, subsequent 20-day returns have smaller left tails. Can that observation be turned into a market-timing system?

The backtest

We setup two long-only portfolios: one that goes long S&P 500 if either of the 50-day or 100-day SMA Distance is in the first quintile and another that, in addition to the 50- and 100-day being in the first quintile, also makes sure that the 200-day SMA Distance is not in the first quintile. These are L1 and L2 in the chart below:

Using SMA Distance is a poor long-term strategy. However, it does help avoid deep drawdowns. It is not very useful as a standalone indicator but perhaps could be used to confirm other signals.

Code and charts are on github.

SMA Distance, Part II

Part I introduced the concept of SMA Distance – the distance between a Simple Moving Average and the index. What can the current SMA Distance tell us about future returns?

Distribution of future returns

To answer this question, we will bucket the SMA Distance into 5 bins (quintiles). The first quintile will have the lowest distance and the fifth one will have the highest. Since a rising market will have negative distances, the first quintile will mark a rising market and the last quintile will mark falling markets. We will bin 50-, 100- and 200-day SMA distances into quintiles, then plot the distribution of the subsequent 20-, 50- and 100-day returns for each.

50-day SMA distance quintiles vs. subseqent 20-, 50- and 100-day returns:

100-day SMA distance quintiles vs. subseqent 20-, 50- and 100-day returns:

200-day SMA distance quintiles vs. subseqent 20-, 50- and 100-day returns:

The negative tails on the 20-day returns are interesting. On the 50-day and 100-day charts, you will notice that the negative tails on 20-day returns are lot more on the 5th quintile than on the first – showing that in the short term, markets trending higher tend not to reverse course sharply. But if the market is way off its 200-day SMA (the last chart,) the first quintile seems to represent over-extended markets that are prone to steeper drops.

We will test this theory on data from 2006 onward in our next post.

Code and charts are on github.

SMA Distance, Part I

Simple Moving Averages (SMA) have been used since time immemorial to trigger trading and risk-management decisions. They are also used to divide a time-series into different regimes to study derivative properties (volatility, for example.) One way to go about it is to simple split it based on whether the index is trading above an SMA or below it. But the binary split maybe too crude to model. Another way to go about it is to measure the percentage distance between the SMA and the index.

The distance

The formula is pretty simple: distance = SMA(n)/index -1 where n is the look-back period (50, 100, etc…)

If you vary n and plot the distance over time for the S&P 500, you get:
SP500.sma.distance
And the same thing for the NIFTY 50:
NIFTY50.sma.distance
-ve distance => Index > SMA (index is trending higher)

On the face of it, it looks like there is some pattern to it. The next post in this series will dive deeper into it. See Part II.

Code and charts are on github.