Category: Investing Insight

Investing insight to make you a better investor.

Macro: Is there a relationship between US Treasury and NIFTY 50 returns?

shyam November 28, 2018

We often hear pundits talk about moves in US treasuries when discussing the NIFTY. Is there a connection? And more importantly, is there a connection that can be exploited?

Two-year US Treasury weekly returns vs. subsequent NIFTY 50 returns

Ten-year US Treasury weekly returns vs. subsequent NIFTY 50 returns

It doesn’t look like there is a link between the two on a weekly basis. Maybe it works in subtler ways (“sentiment”) but it doesn’t look like moves in US Treasuries can be used to trade the NIFTY 50.

Categories Investing Insight Tags macro, ust

Macro: Computing DTWEXM before the FRED

shyam November 27, 2018

In our posts on using currency indices published by the FRED to predict the NIFTY (collection), we assumed that the currency indices will be available to us on Friday before NIFTY closes. This is problematic on two counts:

The FRED publishes the currency indices only on Monday afternoons, or the next business day if they were closed.
The dataset uses Friday index values which is Friday New York time. NIFTY would have closed for the weekend a good 24 hours before that.

In this post, we will get around the #1 problem by computing the currency index we are interested in (DTWEXM) by using the underlying the currencies themselves and the weights used by the FRED. Here are the results:

Apart from a few outliers, the approach looks to be good enough.

For those who are curious about how this was done, the code is on github.

Categories Investing Insight Tags macro

SIP: Expected Returns

shyam November 26, 2018

“A monthly Rs. 5,000/- SIP will make you a crore-pathi!” Screamed a Facebook ad. Really? Is it that simple? Lets break it apart and find out the assumptions baked into that bold assertion.

Historical annualized mid-cap returns

The NIFTY MIDCAP 100 index has given an annualized return of ~21% between 2002 and 2016. If you assume a 20% return over the next 20 years, then a monthly SIP of Rs. 5,000 will indeed result in a Rs. 1,21,94,282/- corpus, making you are crore-pathi.

But what happens if you were 100% invested in Midcaps (20% returns!) and retired in 2007? All your crore-pathi dreams would have gone poof (Midcaps crashed 60% in 2008.)

Static Asset allocation

One way to mitigate blow-up risk is to diversify away from equities into bonds. A popular allocation scheme is the 60/40 equity/bond allocation. So how much should you expect a 20 year, Rs. 5,000/- monthly SIP to return under this scheme?

It is a range of returns because it looks at a 10,000 random samples of 20×12 monthly (historical) returns to reflect the path-dependency of SIPs.

Now, what about the size of the final corpus under this scheme?

On average, you should not expect more then Rs. 60 lakhs as the final corpus under a static 60/40 allocation scheme. You could get unlucky and end up with Rs. 30 lakhs or get very lucky and end up with Rs. 1 crore. But this is the expected range of returns.

If you still want to get to the magic one-crore mark, you will have to “step-up” the SIP contributions.

The final corpus if the SIP is Rs. 5k a month for the first 5 years and 10k, 15k and 20k for the next 5 year blocks, under a static 60/40 allocation:

Glide-path Asset allocation

The 60/40 split would still leave you exposed to market downturns towards the end. One way to eliminate most equity market risks is to adopt an asset allocation scheme that starts with 100% equity, 0% bonds and “glides” to 5% equity and 95% bonds over a period of 20 years. This way, you will have progressively less exposure to equity as you near the end. Under a fixed Rs. 5,000/- a month SIP, your final corpus is likely to be:

And what if you stepped it up?

Take-away

The kind of “straight-line” thinking that the ad propagates will set you up for disappointment if you don’t understand the underlying assumptions that went into it. Prudent asset allocation is much more than raw performance. It is making sure that you will have the money when you need it. And it inevitably compresses returns, so it requires you to save more.

Next time you see such an ad, you can respond by saying “Give me one crore now and I will give you 5k every month for the next 20 yrs.”

Notes:
Rebalanced once a year.
Allows the allocation to drift by 5% before triggering a rebalance.
Assumes a 10% tax on returns at every rebalance.
“0_5” is the total return index of the 0-5 year government bond.

Code and additional charts (with returns using the NIFTY 50 index) are on github.

Categories Investing Insight Tags allocation, sip

Macro: Using Currencies to Predict NIFTY, Part V

shyam November 21, 2018

Please read Part I and Part II for an introduction. Part III extends the treatment to other dollar indices. Part IV looked at an ensemble model from our learnings from Part III.

In this final part of our series on training a simple Support Vector Machine on currency indices to predict the NIFTY, we will incorporate a Simple Moving Average into our decision matrix. While our model in Part IV would go long/long-short based purely on the predictions of the SVM, the model we use here will go long only if both the prediction is positive and the NIFTY is above a 50-day SMA and go short only if both the prediction is negative and the NIFTY is below its 50-day SMA. Think of the SMA as a regime signifier.

Results

Cumulative returns:

BH: buy & hold
L0: Long-only using SMA 50 alone
LS0: Long-short using SMA 50 alone

L1: Long-only using DTWEXB SVM and SMA 50
LS1: Long-short using DTWEXB SVM SMA 50

L2: Long-only using DTWEXM SVM and SMA 50
LS2: Long-short using DTWEXM SVM SMA 50

L: Long-only using ensemble SVM and SMA 50
LS: Long-short using ensemble SVM SMA 50

The SVM that is only based on DTWEXM (LS2) give better returns than the one based on the ensemble model (LS.) However, the ensemble model had a slightly lower drawdown of the two. Also, when compared to the earlier version that did not use the SMA, LS2 under-performs by about 10%. However, the key difference is in the drawdowns. Using the SMA filter reduced drawdowns significantly, especially the one occurring in 2018.

Without SMA filter:

With SMA filter:

Conclusion

An SVM with a 4th degree polynomial over the DTWEXM currency index in conjunction with a 50-day SMA seems to be the winning combination.

Code and charts are on github.

Categories Investing Insight Tags machine learning, macro, NIFTY, support vector machine

Macro: Using Currencies to Predict NIFTY, Part IV

shyam November 20, 2018November 21, 2018

Please read Part I and Part II for an introduction. Part III extends the treatment to other dollar indices.

In this part of our ongoing series on using SVMs on dollar indices to predict the NIFTY 50, we create an ensemble of two models. We combine one on DTWEXB, using an 8th degree polynomial kernel and another on DTWEXM, using a 4th degree polynomial kernel, to create long-only and long-short portfolios.

Results

Here are the cumulative returns of the standalone models and the ensemble:

The standalone model over DTWEXM (LS2) seems to outperform all other models, including buy and hold. However, the ensemble model (LS) has lower drawdowns and comes in at second place. Here are the list of drawdowns deeper than 5% over the test dataset:

Buy and hold drawdowns

Ensemble drawdowns

In the next (and final) post, we will explore if we can add a simple technical signal into to mix to boost returns and reduce drawdowns.

Code and charts are on github.

Categories Investing Insight Tags machine learning, macro, NIFTY, support vector machine