Category: Investing Insight

Investing insight to make you a better investor.

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.

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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.

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US vs. Indian Midcaps

According to investopedia, home country bias refers to the tendency for investors to favor companies from their own countries over those from other countries or regions. This tendency to invest in our own backyard is not unusual or surprising; it is a worldwide phenomenon. This bias is also understandable. After all, we are inclined to recognize and value domestic brands, and consequently, to trust in their solidity and ability to perform well on our behalf. Investors who exhibit home country bias with their investments tend to be optimistic about their domestic markets, and are either pessimistic or indifferent toward foreign markets.

Indian financial media will have us believe that investing in Indian midcaps is the road to riches. There is some truth in it. If you compare returns since early 2000’s, Indian midcaps trounced US midcaps:
US.IND.MIDCAP.2001

But something broke in 2010:
US.IND.MIDCAP.2010

The reasons are many. However, if you think back to 2008, Indian banks came out fairly unscathed from the credit crunch. Investors expected Indian markets to out-perform. But exactly the reverse happened. Year-on-year returns did not really call an early winner either:
US.IND.MIDCAP.annual

This is precisely why investors should diversify across geographies. When it comes to markets, anything can happen.


RUA: Russell 3000 Index tracks the performance of the 3,000 largest U.S.-traded stocks which represent about 98% of all U.S incorporated equity securities.
MID: S&P Mid-Cap 400 Index tracks a diverse basket of medium-sized U.S. firms.

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Russell 3000 and the Cap-Weight opt-out

At first, the US Russell 3000 Value index (RAV) out-performed Growth (RAG) for over 6 years. Then 2008 happened and everything got crushed. Since then, Growth has out-performed Value by a huge margin.
Russell 3000 growth vs. value

Look at the chart closely, however, and you will notice that plain-vanilla cap-weight (RUA) is bang in the middle. Sure, it trailed Growth by about 60% cumulated over 15 years. But equities are only a part of a diversified portfolio and going cap-weight doesn’t require you to choose between Growth and Value – an endless debate that even academics are divided over. Cap-weight is good enough.

Besides, Growth out-performed Value by a noticeable margin in only 4 out of 15 years. Otherwise, the returns have been more or less on par:
Russell 3000 growth vs. value annual returns

There will always be debate over which strategy is “better” but sometimes, given a choice between Chocolate Chip and Very Berry Strawberry, picking Vanilla and sticking with it over the long haul makes the most sense.

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S&P 500 SMA Regimes

In the post Mixture model over S&P 500 returns, we looked at how mixture models can be used to classify returns as belonging to “bull” or “bear” regimes. Unfortunately, we found that using it to trade the index itself was a losing proposition. This lead us to ask ourselves whether a mixture model was any better than a simple moving average based classifier.

Daily returns

If we split returns that occur over different moving averages (50-, 100-, 200-days) and plot their densities, we can see how losses are more frequent when the index is trading below some moving average:
S&P 500 simple moving average returns density plot

Avoiding being long the index when it is trading below a moving average seems to be a good idea. And a quick back-test shows the 200-day average is the one to watch:
S&P 500 long-only SMA returns

All the moving-average “systems” above out-performed the mixture-model based system.

Take-away

Simple beats complex, most of the time.

Code and charts are on github.

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