Tag: quant

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.

The Omega Ratio

We are all aware of the Sharpe Ratio – the ratio between excess return and risk. Mathematically, it is (average return - benchmark return)/standard deviation of excess returns. The main drawback of the Sharpe Ratio is that market returns have fat tails, skews and kurtosis. Numerous performance ratios have been proposed to take care of these “higher moments.” One of them is the Omega Ratio.

The math is a bit hairy. I encourage inquisitive readers to go through Quantdare’s post on this topic. It also has a link to the original paper.

However, using R to calculate Omega is straightforward enough. Here is how a plot of rolling 5-year Omega of the S&P 500 and NIFTY 50 Dollar indices looks like:
Omega of S&P 500 and NIFTY 50 USD

Is it really a step up from the original Sharpe Ratio?

Sharpe Ratio of S&P 500 and NIFTY 50 USD

Code and charts are on github.

Streaks, Part II – Backtest

In Part I of this series, we saw that it is very rare for two consecutive down months to be followed by a third one. Here, we present a simple backtest that goes long NIFTY 50 for a month if the previous two months were negative.

backtest cumulative returns

The shallow drawdowns of this strategy makes it ideal for leveraged trades. NIFTY futures are about 7x levered. That should transform the 190% gross return to about 1330%, beating buy and hold by a wide margin. The MIDCAP 100 index behaves similar to this between the 2005 through 2018 time-frame. However, the results are not so great if you include data prior to 2005.

This looks like a case of severe data-mining and should be discounted as such. But it is an interesting result nevertheless.

Code and charts are on github.

Streaks, Part I

A streak of returns is an unbroken set of up or down days, weeks or months. For example, if the market went up on each of the last four days, then it is a streak of 4 daily returns. Can streaks predict the direction of subsequent returns? Before we answer that, let us look at the density plots of up and down streaks over different periods of time. In the charts below, green lines represent positive returns and red represent the negative ones.

Distribution of NIFTY 50 daily return streaks:

Distribution of NIFTY 50 weekly return streaks:

Distribution of NIFTY 50 monthly return streaks:

Looks like something could be done with monthly returns. Click through to Part II for a quick backtest!

Code and images are on github.

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.