Author: shyam

The Path Dependency of SIP Returns

Our previous post on Lumpsum vs. SIP returns showed how, given the way returns are statistically distributed, lumpsums tend to perform better than SIPs. The analysis side-stepped a lot of issues with using a single market-price time-series by fitting the weekly returns of the index into a Generalised Lambda Distribution and then using that model to run a simulation. This may not seem “real” to most investors. Even through the weekly returns obtained by querying the model is from the same distribution as that of the index, it may not reproduce the exact path that the index took. In this post, we will present a simpler analysis that should be more intuitive.

Random sampling

Most SIP/DCA investors setup a monthly purchase and let it run for a period of time. So we will mimic that process by using monthly returns for our analysis. Moreover, instead of building a statistical model, we will just randomly shuffle the observed set of monthly returns to obtain a return series for our simulation. Each simulation will then end up having the same monthly returns but in a different (random) order. We then calculate SIP/DCA returns for each of those and plot them as a histogram.

Analysis

Here is how NIFTY 50’s randomized monthly SIP/DCA looks like:
NIFTY 50.monthly sip random shuffle
What the above chart means is that both SIP/DCA and lumpsum actual returns are path dependent. A re-ordering of the same monthly returns end up giving vastly different results. This also shows why even if a particular investment gives superior returns, individual investors can still end up with poor returns because of path dependency.

Below are the charts for MIDCAP and SMALLCAP indices:
NIFTY MIDCAP 100.monthly sip random shuffle
NIFTY SMLCAP 100 monthly sip random shuffle

*Key assumption here is that monthly returns are randomly distributed. But trend-followers would disagree on that point.

Code is on github.

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Mutual fund portfolio overlap in UpSet charts

Mutual funds come in different shapes and sizes. Very often, fund investors end up being exposed to the same set of stocks even if they invest in different funds. And not only can there be significant overlap in mutual fund portfolios, some fund managers could be “hugging” the benchmark index. For example, a mid-cap fund could have too much of an overlap with the mid-cap index, effectively making it a more expensive version of an index fund. Therefore, it makes sense for investors to check the portfolio overlap of funds that they are considering not only with each other but also with index constituents.

A common way to visualize overlaps is with a Venn diagram. However, when you have more than five sets (portfolios,) a Venn diagram becomes hard to read. Enter UpSet charts. Here is an UpSet chart that our Overlap Tool creates for the HDFC Mid-Cap Opportunities Fund and SBI Magnum Mid-Cap Fund portfolios:
HDFC Mid-Cap Opportunities Fund and SBI Magnum Mid-Cap Fund portfolio upset chart

Key regions of the chart:

  • Bottom-left: size of each portfolio.
  • Bottom-right: the intersection under consideration.
  • Top-right: the size of the intersection.
  • Titles: Funds being analyzed and their portfolio disclosure dates.

In this example, to see the overlap between the two funds, search for the connection between the 1st and the 4th rows in the bottom-right region and look up – you will see the size of the intersection to be 4. i.e., the funds only have four stocks in common even through each one has more than 50 stocks in its portfolio.

We have included some common indices to help investors identify index-hugging as well. You can run the Overlap Tool here. For an intro to the other tools available on StockViz Tools, please read this post.

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Is Skewness a Timing Signal?

We recently came across a post titled “IMPROVING THE ODDS OF VALUE” (link,) where the author uses the skewness of one-year daily returns to time the value factor. Here, we try to replicate/extend the original backtest.

Key differences

We had to make some tweaks to simplify the task:

  1. The original uses a one-year lookback period, we use a 220-day (trading days) lookback. [minor]
  2. The original uses the S&P 500 index, we use the SPY ETF. Our prices are adjusted for dividends. [minor]
  3. The original constructs a long-short value portfolio. i.e., an academic alpha portfolio. We use it to time the IVE ETF which tracks the S&P 500 Value Index. [major]

We expected the major premise of the original post – that you can go long value during periods of positive skewness and go into cash otherwise – to hold. And perhaps provide some marginal advantage while using it to time a long-only value ETF.

Results

We observed the exact opposite result. Going long IVE during periods of positive skewness under-performed going long IVE during periods of negative skewness. In the cumulative returns chart below:

  • the black and red lines are buy&hold IVE and SPY,
  • the green and blue lines are IVE returns when being long during periods of positive and negative skewness, respectively,
  • the cyan and purple lines are SPY returns when being long during periods of positive and negative skewness, respectively,

cumulative returns using skewness for timing (IVE/SPY)
Note how buy&hold vastly out-performs the timing portfolio.

Further, if you rotate into a liquid fund (earning risk-free returns) instead of going into cash (earning zero), the SPY returns being long during periods of negative skewness beat buy&hold SPY returns:
cumulative returns using skewness for timing with risk-free rate (IVE/SPY)

Perhaps, we are seeing these totally different results because we are long-only and the original back-test was long-short? We are not entirely sure.
Also, the out-performance we observed on SPY failed to replicate on the NIFTY 50 and NIFTY MIDCAP 100 indices.

Code, charts and backtest results for NIFTY 50 and NIFTY MIDCAP 100 indices are on github.

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Charts: Yield Curves

A yield curve is simply a term structure of interest rates – something you get when you plot rates (y-axis) against the term (x-axis.)

When you lend money to someone, you are taking two types of risk: rate risk and credit risk. This shows up in the chart as a curve that slopes up and two the right (to compensate for the former) and as a higher spread for riskier credit (to compensate for the latter.) But the shape is not a “universal truth” that always needs to be. For example, here are Indian zero coupon rates over the last 5 years:
India zero coupon yield curve
Note how in 2014, the curve was “inverted” – short-term rates were higher than long-term rates. This happens more often than one might think. One way to chart it is as a difference between the 10-year yield and the 2-year yield. If it dips below zero, you know the term-structure was inverted:
India 2s10s

Bonds are not “boring.” Note the volatility in the 10-year yields, not just in Indian bonds but in US and Euro area as well:
Indian 10y yields

US 10y yields

Euro 10y yields

Countries in the Euro area have different sovereign credit ratings. So the ECB publishes two rates: one is an aggregation of the whole Euro area and the other that is an aggregation of only the AAA country bonds. The chart above shows how right until the credit crisis hit, AAA and ALL were right on top of each other and then blew out after that point. This is the market realizing that not all Euro area countries are the same.

Although Indian rates have never been negative, the same cannot be said for others. Here is how the Euro yield curve looked like on October 15, 2018:
Euro area yield curve on 2018-10-15
And until very recently, short term rates in the US were zero:
US yield curves

Corporate bond investors need to be compensated for the credit risk that they take (over and above rate risk.) So corporate bonds tend to trade at a spread over the govvies. They are typically broken up by their credit rating and quoted as a spread:
US corporate spreads

And the market further differentiates between “developed market” and “emerging market” corporates even within the same credit rating. Here is a chart of AAA spreads for US and EM corporate credit:
US/EM corporate credit spreads
The spikes in spreads are the credit crisis rippling through the system.

We publish updated rate, credit and currency charts that all investors should track on our Macro Dashboard. Do check it out.

Code and more charts on github.

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Book Review: The Fish that Ate the Whale

The Fish that Ate the Whale (Amazon,) is a biography of Sam Zemurray, the Banana Man. The author, Rich Cohen, tracks the rise and fall and rise and fall of the banana company that Sam built through the late 19th and early 20th century.

The sheer magnitude of the rags to riches story that starts with Sam trading discarded bananas and ends with him running the United Fruit Company is completely breathtaking. Oh, and it also involves overthrowing a Latin-American government that was about to turn hostile to the banana trade. Incredible.

The book could easily be about any business in the perishable commodities business. We now take American apples and Australian kiwis in our supermarket for granted. However, there was a time before cool chain and cold storage when most produce had to be sourced within a certain geographic radius. Ships had to be unloaded, by hand, onto steam-powered trains. Most of the produce would rot and go to waste. Hemmed-in by these constraints, businesses had to work with what they had. If it involved bribing bureaucrats or overthrowing governments to ensure zero taxes and union-free labor, then so be it.

The book is also the story about most corporations. Even though, technically, a company could live on forever, most have finite lifespans. Founders who know the business inside-out inevitably age. They are replaced by “professional management” who may keep things going for a while. But eventually, most companies that die end up being run by bureaucrats far removed from the ground with very little of the spirit that shaped the company at the beginning.

Sam Zemurray’s life could very well be an exemplification of the American dream but the banana company that he built ended up being an exemplar of the worst of American business’ colonial instincts.

Recommendation: Read it now!

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