Author: shyam

Why Anomalies Persist

Academics label momentum as an “anomaly.” Multiple studies have shown that this anomaly has persisted over long periods of time and across markets (AA). Based on this insight, quite a few quantitative momentum funds sprung up. And since nothing good is ever left alone at Wall Street, a whole bunch of momentum factor ETFs launched to ride the wave during the recent bull market. Currently, there are more than 40 momentum ETFs listed in the US.

So, does this mean that the momentum anomaly has been arbitraged out? After all, with over $12 billion in momentum ETFs alone, shouldn’t the strategy have topped out? We posit that it is unlikely to happen anytime soon. Why? Because investors just can’t help themselves.

Consider the asset under management (AUM) of these momentum ETFs. If, for an ETF, the price is down 10% and its AUM is down 20% over the same period, it means that there has been a net outflow of 10%. If you run this math on all the momentum ETFs traded in the US since October this year, you end up with about $2 billion in outflows in 3 months. That is roughly 11.5% of momentum assets on the 1st of October.

It has been well documented that investors chase performance, often piling into “hot” funds and strategies and exiting on the slight whiff of under-performance. We are seeing this in action on momentum ETFs. And as long as investors are caught in this doom-loop, momentum (and by extension, value, investment and volatility anomalies) will persist.

Also read: Investor education is a waste of time (Aug, 2014)

Code and data are on github.

Categories Tags ,

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.

Categories Tags

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.

Categories Tags

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.

Categories Tags ,

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.

Categories Tags