Category: Your Money

Introducing the Global Macro Dashboard

It is all local until it is not

World markets just witnessed a spiraling sell-off that caught most investors off-guard. The problem is that for most of the time, markets are local. Except for those times when they aren’t and correlations go to 1.

We tried singling out different factors to check if they could act as leading indicators of market sell-offs:

It is not one thing and it is never the same thing

The problem is that, statistically, no one global indicator is going to be a perfect canary in the coal mine. However, once the number of “meaningful” events crosses a threshold, correlations tend to 1.

But what exactly defines “meaningful?” Is it 1-sigma or 2-sigma? Should it be change in price or price itself? What should be the number of periods over which these statistics are calculated? The answers to these questions are going to take a while to figure out. In the meantime, we decided to create a dashboard that lets investors choose some of these filters.

You can play with our Global Macro Dashboard here: StockViz/GlobalDashboard

Welcome to glocal markets.

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Index Update 14.02.2016

MOMENTUM

We run our proprietary momentum scoring algorithm on indices just like we do on stocks. You can use the momentum scores of sub-indices to get a sense for which sectors have the wind on their backs and those that are facing headwinds.

Traders can pick their longs in sectors with high short-term momentum and their shorts in sectors with low momentum. Investors can use the longer lookback scores to position themselves using our re-factored index Themes.

You can see how the momentum algorithm has performed on individual stocks here.

Here are the best and the worst sub-indices:

index momentum best 365 2016-02-12 png

index momentum best 50 2016-02-12 png

index momentum worst 365 2016-02-12 png

index momentum worst 50 2016-02-12 png

Relative Strength Spread

NIFTY_500 relative-spread-index 50 2016-02-12 png

Refactored Index Performance

50-day performance, from December 03, 2015 through February 12, 2016:

Equally Weighted Market Cap. Weighted
GROWSECT 15 (EQUAL) -6.95% IT (CAP) -3.90%
QUALITY 30 (EQUAL) -9.73% PHARMA (CAP) -4.29%
FMCG (EQUAL) -10.08% GROWSECT 15 (CAP) -4.52%
CONSUMPTION (EQUAL) -11.07% QUALITY 30 (CAP) -7.22%
AUTO (EQUAL) -11.17% ENERGY (CAP) -8.90%
PHARMA (EQUAL) -11.26% FMCG (CAP) -10.06%
ENERGY (EQUAL) -11.53% COMMODITIES (CAP) -10.30%
MNC (EQUAL) -12.24% CONSUMPTION (CAP) -10.41%
COMMODITIES (EQUAL) -12.32% SERV SECTOR (CAP) -11.79%
IT (EQUAL) -13.54% VALUE 20 (CAP) -12.03%
MEDIA (EQUAL) -15.13% MEDIA (CAP) -12.18%
VALUE 20 (EQUAL) -15.16% MNC (CAP) -12.86%
SERV SECTOR (EQUAL) -16.18% PSE (CAP) -14.25%
PSE (EQUAL) -17.26% METAL (CAP) -14.56%
CPSE (EQUAL) -19.21% AUTO (CAP) -15.14%
FIN SERVICE (EQUAL) -20.29% CPSE (CAP) -15.57%
LIQ 15 (EQUAL) -20.52% FIN SERVICE (CAP) -16.78%
INFRA (EQUAL) -21.81% INFRA (CAP) -16.79%
BANK (EQUAL) -22.99% BANK (CAP) -18.51%
METAL (EQUAL) -23.62% LIQ 15 (CAP) -20.52%
REALTY (EQUAL) -25.92% MIDCAP LIQUID 15 (CAP) -23.99%
MIDCAP LIQUID 15 (EQUAL) -25.97% REALTY (CAP) -26.29%
PSU BANK (EQUAL) -38.49% PSU BANK (CAP) -36.73%

Trend Model Summary

Index Signal % From Peak Day of Peak
NIFTY AUTO LONG
22.25
2015-Jan-27
NIFTY BANK LONG
32.03
2015-Jan-27
NIFTY COMMODITIES LONG
42.67
2008-Jan-04
NIFTY CONSUMPTION LONG
14.40
2015-Aug-05
NIFTY ENERGY LONG
37.45
2008-Jan-14
NIFTY FIN SERVICE LONG
28.84
2015-Jan-28
NIFTY FMCG LONG
17.79
2015-Feb-25
NIFTY INFRA LONG
63.65
2008-Jan-09
NIFTY IT LONG
89.19
2000-Feb-21
NIFTY MEDIA LONG
26.02
2008-Jan-04
NIFTY METAL LONG
72.78
2008-Jan-04
NIFTY MNC LONG
22.21
2015-Aug-10
NIFTY PHARMA SHORT
19.88
2015-Apr-08
NIFTY PSE LONG
43.76
2008-Jan-04
NIFTY PSU BANK LONG
63.39
2010-Nov-05
NIFTY REALTY LONG
93.12
2008-Jan-14
NIFTY SERV SECTOR LONG
23.80
2015-Mar-03
FMCG/Consumption provided some “flight-to-safety” benefits. But the overall 50-day performance of different sub-sectors have been pretty grim.
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Macro Volatility and the NIFTY 50

This post is a continuation of our exploration of trying to use macro market indicators to time the NIFTY 50. See World Markets and the NIFTY 50 and India VIX vs. SPX VIX.

Perhaps the problem with using price moving averages was that the major moves were already done before we could short the NIFTY. What if we used volatility instead? Here is how the median of 10-day volatility of major world indices looks like:

macro.volatility

What if we went long only when volatility was below the median and went short otherwise?

macro.trade.a

Looks like the strategy works only in avoiding the 2008 crash. Using observed volatility to time trades doesn’t work. One more to the reject pile.

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World Markets and the NIFTY 50

global.etf.performance.2015-12-30.2016-02-08

Global markets have sold off in tandem so far this year. The question on everyone’s mind is if markets are correlated during sell-offs, then is it possible to construct a “world markets indicator” that will allow traders to short the NIFTY?

Constructing an SMA index

There are about 40 world index ETFs listed in the NYSE that provide a dollar based proxy to world equity markets. Using historical data, one can construct an index that tracks the fraction of these markets that are trading above their simple moving averages (SMAs.) If the index dips below a certain level, it could mean that a macro sell-off is in progress and one could proceed to short the Nifty. We constructed a 5- 10- and 50-day index of global market ETFs:

macro.sma

Using the SMA index to trade

So what if we used the SMA indices to go long and short the NIFTY? What we did below was to go short the NIFTY if the fraction of world markets trading below their SMAs were below their historical medians and long otherwise.

macro.trade

Between 2007 and now, the 5- and 10-day SMAs (black, red) under-perform a buy-and-hold (blue) strategy. However, the 50-day strategy (green) helped short the 2008 crisis and the current sell-off.

But let us zoom into the period between 2011 and 2014:

macro.trade2

This is where macro under-performs buy & hold (negative returns vs. positive.)

Year-wise returns of the different strategies:

Year 5-day SMA 10-day SMA 50-day SMA Buy and Hold
2007
50.58%
7.01%
12.03%
54.77%
2008
-2.51%
65.04%
69.88%
-51.79%
2009
-20.12%
-37.06%
18.47%
75.76%
2010
-4.11%
-23.86%
-19.11%
17.95%
2011
-24.00%
-16.30%
7.68%
-24.62%
2012
14.09%
16.66%
1.22%
27.70%
2013
-19.15%
6.02%
-8.94%
6.76%
2014
1.07%
-1.97%
-1.17%
31.39%
2015
13.56%
-4.39%
25.32%
-4.06%
2016
-4.77%
-4.60%
-6.13%
-7.04%

Conclusion

  • Given the volatile nature of the SMA World Indices, expect to take a fairly large hit on trading costs.
  • The 50-SMA based strategy under-performed buy and hold in 6 out of 9 years.
  • The 50-SMA based strategy under-performed buy and hold in 3 consecutive years – 2012, 2013 and 2014 – making it a hard strategy to be faithful to.

Related: India VIX vs. SPX VIX

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India VIX vs. SPX VIX

The VIX index is considered a gauge of fear in the markets. A high VIX entails high option implied volatility and occurs when traders bid up options. Given the recent market turbulence where pretty much all asset classes and equity markets across the board tanked, we wanted to check if VIX indices across markets had any relationship with each other.

VIX since 2009

The India VIX index was introduced only in 2009. Let’s start by plotting it vs. the S&P 500 (SPX) Vix to see if it passes a visual sniff test.

vix.historical

We can see at least five instances where India Vix popped without a corresponding move by SPX VIX.

Distribution

The problem with trying to nail down a relationship between the two indices is that, as one would expect, India VIX is way more dispersed than its SPX counterpart. Here’s the density plot.

india vix - spx vix density plot

India vs. spx vix density plot by year.

They might share the same suffix, but they are two completely different beasts.

Cross-correlation

The plots above look at VIX levels. Maybe we should check if the changes in VIX are related. Here’s the cross-correlation plot.

india vix and spx vix cross-correlation plot

India vix and spx vix cross correlation by year.

Conclusion

We cannot really use the two Vix indices to predict each other’s moves.

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