Category: Investing Insight

Investing insights to make you a better investor.

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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Reinforcement Learning for Finance

We combined books and research papers to create a knowledge base. We then used that to create a research program to check if they can be applied to Indian stocks/futures for profit. Our threshold for success was outperformance against naive momentum/trend strategies. RL implementations tend to be complicated and compute intensive. They least they can do is beat simple systems. Also, India is an expensive venue to trade so these tests included those costs as well.

Across every reward, cost, state, action, algorithm, universe, and venue we tried, no RL agent beat classical baselines net of costs out-of-sample.

Our research plan, and eli5 are good places to start. Or, you can jump right into the summary of the results.

This is by no means exhaustive and we chose simple RL systems to get our beaks wet. However, the lack of WOW! is concerning.

Code, charts, etc. are on github.

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Stop-losses or Stop-profits?

While we use trailing stop losses in our Rapid Fire themes, they are by no means the brahmastra they are made out to be. We audited two of the strategies in that theme: Momo (Velocity) v1.0 and Momo (Relative) v1.1. We would have made more money by doing nothing.

Some findings (details):

  1. The stock that was sold usually went up after. At 20-days, you were 1.5× more likely to miss a >5% rally than to avoid a >5% slide.
  2. The stock that was bought instead did about the same. A drift-positive basket was replaced with another drift-positive basket. The churn bought nothing and you paid transaction costs on top.

Trailing stop losses might be psychological crutches. But with everything else in finance that is designed to ease the pain of ownership, you end up paying for it.

Analysis, charts and code are on github.

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