Category: Collections

Curated list of posts on a specific topic.

Global Equities Momentum

A brief introduction to Global Equities Momentum and a look at various alternative scenarios.
Read: Part I

Could it work on value indices?
Read: Part II

Swapping momentum at the final step boosts returns significantly.
Read: Part III

Averaging out returns over different formation periods boosts returns and reduces drawdowns.
Read: Part IV

Track the virtual portfolios we setup using these strategies and follow the trades on our Slack channel.
Details: Trades and Portfolio


Can global “macro” data be used to model local markets?

Correlation and linear models

A series of posts exploring the (lack of) linear relationship between USDINR, OIL and the NIFTY 50. Part I, Part II and Part III.

Machine learning

Using an SVM over dollar indices to trade the NIFTY

An SVM can be tuned with different parameters. Principal among those is the kernel to be used. Part I looks at the difference in predictive power when different kernels are used to train an SVM over the same dataset.

Part II tries to further tune the most promising kernel from Part I – the polynomial kernel. However, we find out that there is no silver bullet.

So, we take all the dollar indices (including USDINR) and observe the predictive power of an SVM using a polynomial kernel with varying degree parameters. We find that only two out of four indices are actually useful and that going too far back in history while training the SVM is counterproductive (Part III.)

Part IV trains an SVM using the two promising indices and their respective best-performing degree parameters from Part III. An ensemble model that chains the predictions looks promising.

While performing the experiments above, we noticed that none of these models side-step the 2018 drawdown. Their principal limitation is that they are “macro.” They will not handle local events well. So we combine the model from Part III with a Simple Moving Average and find that it leads to lower drawdowns in long-short portfolios. Part V also leads us to conclude that using just one of the indices is sufficient to meet our twin goals of shallower drawdowns and higher long-short returns.



Historical volatility and implied volatility.
Read: Part I, Part II

Nifty volatility

Density plots of historical volatility over different horizons.
Read: NIFTY Volatility, Historical Perspective
Charts that are updated often: Volatility and VIX Charts

Dollar ETF volatilities

When you convert Indian indices to dollars, their volatility profile changes.
Read: INDA vs. SPY Observed Volatility

VIX – Implied Volatility Index

Do VIX indices across markets have any relationship with each other?
Read: India VIX vs. SPX VIX

Can a simple VIX based trading strategy avoid market losses?
Read: Macro Volatility and the NIFTY 50, VIX and Equity Index Returns, Part I, Part II.

Can VIX be predicted using a simple model?
ARMA + GARCH to Predict VIX

Asset Allocation


How does an equity/bond 2-asset portfolio look like?
Read: Allocating a Two-Asset Portfolio

A three asset portfolio

Indian midcaps + bonds with Nasdaq-100 ETF. Is there a benefit to using portfolio optimization algorithms after taxes and transaction costs are taken into account?
Read: Allocating a Three-Asset Portfolio, Equal Weighted and Allocating a Three-Asset Portfolio, Optimized

Adding gold into the mix

Does gold have a role to play in a systematic, diversified portfolio?
Read: Allocating a Four-Asset Portfolio

Investing in a systematic, diversified portfolio

A ready-to-invest Theme, the EQUAL-III, that takes care of keeping track of everything.
Read: The EQUAL-III Theme

Expected Returns

What are the range of expected SIP returns under prudent asset allocation schemes?
Read: SIP: Expected Returns