Tag: quant

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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VIX and Trend-following

We are not big fans of using VIX for timing equities. Strategies that seem to work on US market data fall apart when applied to Indian equity indices. When Alpha Architect published their follow-on VIX and Trend Following results, we decided to test it on our own NIFTY large-, mid- and small-cap indices.

India VIX decides how much recent history to use:

  • When volatility is calm, rank the assets by their 10-month performance. This changes slowly and avoids reacting to short-term noise.
  • When volatility is elevated, use 3-month performance so the strategy can respond faster.
  • When volatility is very high, use only the latest month’s performance.

tl;dr: it kind of works – beats the NIFTY 50 after 25bps of costs post-COVID.

However, with an on-going 30% drawdown, it is not exactly self-recommending. Having said that, its pre-COVID results might be a better indicator of how the strategy manages to side-step large shocks.

It does a semi-decent job of switching between different market-cap indices.

The full-period metrics (2009 – 2026) are promising. Seems to hold its own against buy & hold – lower drawdowns with similar(ish) returns.

While 25bps of costs should cover transaction costs, the problem is surviving capital gains taxes require a much higher returns.

Details of the approach, descriptions of the strategies and code are on github.

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Liquidity Improvements and Momentum

In the paper Momentum Returns and the Role of Liquidity Improvements (Jeppe Bro, March 28, 2026, SSRN) argues that the momentum anomaly arises from cross-sectional liquidity dynamics rather than representing an independent risk premium. Past winners systematically improve in liquidity prior to the portfolio formation period, whereas past losers severely deteriorate (summary).

What if we used this LIQIM to setup a long-only portfolio of 20 Indian stocks?

It doesn’t quite work that way. So, we looked at the return stats of LIQIM quintiles and figured that we could use that to enhance momentum by filtering out stocks that had degraded liquidity (backtest).

Turns out, there is about 50 bps of performance that you can squeeze out of a typical long-only momentum strategy using this liquidity filter.

Code and charts are on github.

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