Author: shyam

How long is long term?

Most new equity investors think “long term” is three years. Some think its five. This leads to expectations that are setup to fail. We wrote about projecting future returns recently where we showed how we expect 20-year returns to be statistically distributed. In the simulations that we ran for that article, we also projected returns for 10- and 30-year horizons. We reproduce the charts below.

10-year S&P 500 return distribution

SP500.GLD 10-year

20-year S&P 500 return distribution

SP500.GLD 20-year

30-year S&P 500 return distribution

SP500.GLD 30-year

As your investment horizon grows larger, the probability of you facing severe losses come down and the overall probability of positive outcomes increase.

Fama and French agree

In a recent paper, Volatility Lessons for the Financial Analysts Journal, Eugene F. Fama and Kenneth R. French pretty much arrive at the same result. Here are the charts from their paper:

And they conclude:

The high volatility of monthly stock returns and premiums means that for the three- and five-year periods used by many professional investors to evaluate asset allocations, the probabilities that premiums are negative on a purely chance basis are substantial, and they are nontrivial even for 10- and 20-year periods.

Basically, long-term is ~30 years, anything less that is prone to be influenced by noise (luck.)

Country Equity Index Volatility

Previously, we saw how different country indices performed relative to their deepest drawdowns. Peak drawdowns only tell half the story. Here, we look at historical volatility. To keep things simple, we will define volatility as the standard deviation of daily returns. i.e., close-to-close volatility.

The country-ticker key can be found here.

2004 through 2018

NASDAQOMX.volatility

Year-wise

Bar plot:
NASDAQOMX.volatility.yearwise
Heat map:
NASDAQOMX.volatility.yearwise.heat

Thoughts

  1. The year 2017 was uniformly a low-volatility year. So were 2005 and 2014.
  2. Some countries, Greece (NQGRT) for example, have been extremely volatile. Some, Malaysia (NQMYT) for example, have been surprisingly less.
  3. India (NQINT) has been middle of the pack.

Code and charts on github.

Source: NASDAQOMX data from Quandl.

Of GURUs and MOATs

If we had to point to one financial innovation that upended classical asset management over the last decade, it would be Exchange Traded Funds (ETFs.) At first, there were a handful of them. They were pure market cap weighted funds that mirrored a popular broad market index, the S&P 500 index for example. But over the last decade, the number of ETFs and the strategies they allow investors to access have exploded. Currently, there are over 2,200 ETFs listed in the US. Most of them are cap-weighted, some are “smart-beta”, some are traditional momentum/value, etc. But there are a few of them that are completely bonkers. Here are two of them.

The GURU ETF

Hedge funds, in the US, are supposed to disclose their holdings that cross a certain threshold to the SEC. The GURU ETF parses those filings and creates a portfolio of “highest conviction” ideas. From its website:

GURU allows everyday investors to access the high conviction investments of some of the largest, most sophisticated hedge funds in the world. Traditionally, investing with a hedge fund requires paying an ongoing 2% management fee and 20% of profits. GURU has an expense ratio of 0.75%, potentially allowing for greater cost efficiency, while providing access to hedge fund ideas.

On the face of it, it is a ridiculous idea. Hedge funds are much more than just long-only equity. Besides, the filings are done months after those funds have built a position in those stocks. So surely, it should be a disaster?

Compared to the broad-market Russell 1000 ETF from Vanguard, it is not so bad. Annualized returns for GURU and VONE were 12.09% and 13.40%, respectively. It seems to have out-performed initially then suffered a deep drawdown from which it staged a middling recovery. Is it a case of the rising tide of a bull market lifting all boats? Can’t say.

We still don’t like the idea but turns out that it was not a very bad one.

The MOAT ETF

All value investors ever want are “attractively priced companies with sustainable competitive advantages.” MOAT promises that for 48bps. Surely, it can’t be that obvious?

Annualized returns for MOAT and VONE were 12.73% and 11.67%, respectively. Back in May 2018, Elon Musk thought “moats are lame.” Not so lame, it turns out.

Epilogue

There are strong reasons for Indian investors to open a US brokerage account and diversify their holdings into dollar assets. ETFs in the US are cheap and cover a wide array of investment strategies – there are at least two for every one that you can think of. Make you move now!

Charts above were created using our Compare Tool. Check it out.

Projecting Future Returns

Saving is a form of deferred consumption. You save today in order to consume tomorrow. Equities are a great place to park those savings when that “tomorrow” is measured over multiple years. Equity investments yield higher returns over bank deposits or bonds primarily because they are riskier. This risk shows up as a bigger variance in returns. When you save for a distant future, estimating this variance is important, lest you fall short of funds when that future finally arrives. Earlier, we had written about how path-dependency affects your eventual returns in The Path Dependency of SIP Returns (part of our Lumpsum or SIP? collection.) There, we used a simple trick used in statistics to show how by merely changing the arrival of returns changes the final IRR of a systematic investment plan (SIP, aka dollar cost averaging, DCA.)

The standard disclaimer on most investment products reads: Past Performance is Not Indicative of Future Results. While it is foolhardy to extend past returns infinitely into the future, you can use some of past returns’ statistical properties to model a range for scenarios for your investments. In the projections you see below:

  1. We used index data from 1991 through 2018, during which the markets have witnessed multiple boom-bust periods, scams and political events.
  2. We used monthly return series to avoid short-term daily and weekly variances.
  3. We used a Generalized Lambda Distribution to model those returns to avoid the pitfalls of using a normal distribution.
  4. We ran 10,000 simulations over a 20-year investment horizon – a typical saving period for retirement or children’s education.
  5. We looked at both lumpsum/onetime and SIP/DCA investment modes.

NIFTY 50 Rupee vs. Dollar

Most investors in India have a strong home-country bias and invest primarily in rupee assets. But if you are also considering future expenses that would require dollar-based funding (see: Funding Your Dollar Dreams), it makes sense to look at NIFTY 50 through a dollar colored lens. Here is how projected lumpsum/onetime 20-year investment on the NIFTY 50 looks like:
NIFTY50

Here is how projected lumpsum/onetime 20-year investment on the NIFTY 50 Dollar looks like:
NIFTY50DLR

Note that large difference in median returns between the two. This is primarily driven by the depreciation of the rupee which is related our rate of inflation and capital account situation. Also, dollar returns are skewed left.

NIFTY 50 Dollar vs. S&P 500

Given the higher risk that Indian investors bear, you would expect it to out-perform the staid old S&P 500. But compare the projected median returns of the two. Is the additional risk appropriately compensated?

Here is how projected lumpsum/onetime 20-year investment on the S&P 500 looks like:
SP500.GLD

The projected returns are in line with what we saw when we plotted returns vs. largest drawdowns of different country equity indices here. The higher risk one bears for investing in an emerging market doesn’t seem to be appropriately compensated when, on average, NIFTY 50 is expected to pay only 1% above S&P 500’s returns.

Lumpsum/onetime vs. SIP/DCA

Most investors buy through a monthly SIP/DCA setup to avoid timing the market and better match their income stream. Our projections show that they win by having shallower fat tails. Here are how SIP/DCA returns look like for all three indices:
NIFTY50.GLD.SIP-DCA
NIFTY50DLR.GLD.SIP-DCA
SP500.GLD.SIP-DCA

Conclusion

First, higher inflation tends to boost gross equity returns. However, higher inflation also marks a weaker currency. So what you gain in gross returns, you lose on the real returns. The difference between projected NIFTY 50 and NIFTY 50 Dollar returns captures this dynamic. This is something to keep in mind when you look at historical long-term returns of Indian equities.

Second, if past is prelude, if a conservative investor had to choose between NIFTY 50 and the S&P 500, he would choose the latter. The fat left tails and small risk premiums of the former are deal breakers.

Third, emerging markets are all about optionality. Note the differences in the right tails. A fatter right tail indicates that presence of more opportunities to skew the portfolio towards higher returns. So an aggressive investor would pick an active NIFTY 50 investment over the S&P 500.

Code and charts are on github.