Tag: quant

Kalman vs SMA

This post is inspired by A Stochastic Model Against a Geometric Rule: the Kalman Filter and the 200-Day Line. We took the basic idea and translated it to Indian equity indices. Then we extended it to different SMA look-back periods (20-, 50- & 100-days) and different Kalman configurations (fixed, calibrated & adaptive.)

tl;dr: you are better off with moving averages (details).

NIFTY 50 and NIFTY BANK doesn’t trend on a daily time-frame so none of the trend-following approaches work on that. On MIDCAPs and SMALLCAPs, short moving averages reduce drawdowns. So if you can get cheap leverage or benchmark to NIFTY 50, it might make sense.

Code and charts are on github.

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Factor Momentum with Index Funds

Factor Momentum is an interesting concept – it posits that factor portfolios (value, quality, low-volatility, momentum, etc.) themselves exhibit momentum. If a certain factor worked in the past, it will continue working in the near-term. We had introduced this in Factor Momentum Everywhere and setup a couple of model portfolios – Model Momentum that uses our own factor models, and Factor Momentum that uses ETFs.

While discussing Factor MAX, we realized that there are now index funds that reference the basic factors that can be used instead of ETFs. ETF liquidity tends to be patchy and does not support large portfolios. Index funds are more convenient in that sense.

Here’s how factor momentum using just these basic factor indices looks like:

Like every investment/trading strategy, there will obviously be years where it under-performs.

However, it looks like this has held its own.

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Filtering and Momentum Signals

The paper Shenhao Zhang, A Low-Frequency Quantitative Trading Strategy Based on Trend Filtering and Momentum Signals: Empirical Evidence from A-Share Banking Stocks, published at ICDEIT 2025, is a small, low-frequency technical-strategy study on two Chinese bank stocks. We wanted to check if it generalizes to a larger universe of Indian and US stocks.

tl;dr: it doesn’t (summary).

For Indian stocks, you may want to give this a wide berth. From 2016 through now, the model barely beat a fixed deposit.

However, by the virtue of it side-stepping the 2008 drawdown, it managed to put a decent number on the board for US stocks.

We tested this out because the premise seemed promising. Sadly, it turned out to be another one of those narrow, overfitted papers with an SEO friendly title.

Code, charts, findings are on github.

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SEBI Surveillance Measures

SEBI introduced the Additional Surveillance Measures (ASM) and Graded Surveillance Measures (GSM) Framework in 2018. They serve as “early warning systems” to protect investors. ASM focuses on price volatility, while GSM focuses on weak corporate fundamentals.

We have been tracking these since they were introduced. They are helpful “no-go” zones while constructing portfolios primarily because the additional margin requirements with narrow price bands and the corresponding fall in volumes makes risk-management impossible.

The question is, can this black list be turned in to a white list? Is there alpha in trading the transitions of stocks in & out of this list?

Could there be an effect that can be exploited?

Nope.

You can read a more detailed introduction to surveillance and research results here.

Code and charts are on github.

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Turbulence in Financial Markets

Can the concept of turbulence in physics be applied to financial markets? It could be. We did a small test and it does appear to be helpful (summary).

There are some instruments for which it works in some regimes…

… and fail in trending markets.

The biggest problem with the entire approach was the sheer number of parameters that needed to be specified. There are no “right” ones and you will almost always overfit.

And walkforwards fell apart.

Its a promising approach to be revisited once we have more material to work with.

Code, charts and summaries on github.

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