Author: shyam

MSCI Country Index Correlations

All stocks are correlated to one-another. In times of crisis, these correlations explode higher. The same is true for country indices. For example, if you look at the rolling 5-year monthly return correlations between the MSCI INDIA index and other country indices, Jordan is the least correlated and Hong Kong is the most correlated.

Least Correlated:
least correlated with INDIA

Most Correlated:
mostcorrelated with INDIA

The annual return charts show the zig/zag nature of these markets:
MSCI.INDIA-JORDAN-HONG.KONG.annual.returns

So, does it make sense to construct a 50/50 portfolio between INDIA and JORDAN? In theory, the resulting portfolio should have lower draw-downs and lesser volatility than either taken alone.
MSCI.INDIA-JORDAN.cumulative

In contrast, here is the 50/50 INDIA/HONG KONG portfolio:
MSCI.INDIA-HONG.KONG.cumulative

A take-away from this is that diversification within the same asset class (in this case equities,) does not help with drawdowns. Nor does it necessarily lead to higher returns. It is way of protecting yourself from mistakes that are only apparent in hindsight. Just ask investors who were invested 100% in Jordan the last decade.

Code and charts on github.
Related: Stock and Bond Correlations and Volatility

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Strategy Capacity

A recent Verdad Capital newsletter, Dan Rasmussen points out that it is impossible to scale value investing as defined in academia. Almost all of “value” ETFs and actively managed funds are completely avoiding the cheapest stocks (high book-to-market) while instead owning primarily stocks that are more expensive (low book-to-market).

And what explains this puzzle? Strategy capacity.

The cheapest stocks are disproportionately small in terms of size and volume. This means that an active manager looking to choose, say, the best 40 of these stocks would be unable to manage more than $200M or so. The average small value fund tracked by Morningstar has $1.3B of assets under management. It is close to impossible to deploy that amount of capital exclusively in the cheapest two deciles of the stock market.

This is true for Indian mutual funds as well. Most managers claim to be “value” investors while actually hugging the index with a GARP/momentum tilt. Given the size of most of these funds, there is no way they can invest in value.

Strategy capacity should be one of the questions investors should ask of their fund managers/advisers. Especially advisers of direct equity portfolios who do not know the aggregate exposure that their subscribers have across portfolios.

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Book Review: Everybody Lies

In the book Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are (Amazon,) author Seth Stephens-Davidowitz provides a peek into how “big data” can help us understand the world around us provided we know how to ask the right questions. And that, depressingly, people are consistently lying to themselves.

The author goes on to show how some of the most successful companies in recent memory are based on capitalizing on the difference between what people say they are and what they really are.

The book also touches on a common problem faced by quants who are trying to use big data for building trading models: the curse of dimensionality. We discussed this in our GEM and SMA series of articles (GEM, SMA) – there is no single “best” look-back period for calculating momentum or moving averages. There are trade-offs involved and there is always risk. These issues tend to snowball when using large, multi-dimensional data-sets to a point where it is hard to discern signal from noise.

The principal take-away from this book is that big-data is useful to answer questions typically raised in the social sciences and public policy. But a poor fit where the underlying data is heteroscedastic or the system itself is complex-adaptive.

Recommendation: must read!

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Is the Low Volatility Regime Breaking?

NIFTY 50 volatility the last couple of years have been extremely low by historical standards. If you look at the rolling median of weekly returns over 50 weeks (about a year), you can see how the range has narrowed:
median weekly returns
The standard deviation, a popular measure of volatility, has come down as well:
standard deviation of weekly returns

As the charts illustrate, the markets have been moving in tight ranges. And narratives have been built around it:

  1. Global central banks (US, Europe, Japan) have been flooding the markets with liquidity, essentially writing a put on the market.
  2. Markets have become less riskier thanks to increased regulations after the 2008 global financial crisis.
  3. Investors have a new-found enthusiasm for “SIP it and forget it.” This, plus the NPS bid, has cushioned the NIFTY 50.
  4. Increased liquidity in the derivatives market has allowed investors to buy volatility, thereby reducing the need to decrease risk by offloading equities in the cash market.
  5. The majority government at the center has provided policy certainty and political scams have not paralyzed decision making.

Narratives can change overnight. And if the last few months have taught us anything, the market drives the narrative. Also, new investors have only seen a low volatility environment and think it to be “normal.” So any reversion to the old volatility regime would be a rude awakening. Are we really in a new world or is volatility about to revert to its longer-term mean?

Code and additional charts on github.
Also read our Volatility Collection.

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