Tag: quant

The High Volume Return Premium

Gervais, Simon, Ron Kaniel, and Dan H. Mingelgrin, 2001, “The high‐volume return premium,” The Journal of Finance, unearthed a market anomaly quite similar to momentum where stocks that traded with a higher than average volume went on to give higher returns.

We did a quick test on the top 200 stocks by free-float market cap on the NSE between 2014 and 2024 to check if it makes sense to pursue this further.

A long-only strategy that rebalanced once a month with a 50-day reference period underperformed the NIFTY 100 TR index by a wide margin until the August of 2023. Nine years of underperformance vs. a year of parabolic liftoff doesn’t really speak to the stability of the anomaly.

We’ll look for further publications in this area and report back. In the meantime, you can read the paper and have a look at our code here: github.

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Trend Factor

Han, Yufeng and Zhou, Guofu and Zhu, Yingzi, A Trend Factor: Any Economic Gains from Using Information over Investment Horizons? (SSRN), outlines the construction of a trend factor for equities.

In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. 

Does the paper’s claim hold true for Indian equities? Not really.

The Long-only Trend Factor underperformed a naïve momentum strategy and its corresponding benchmark. The Long-short Trend factor returns was negative.

Even after “tuning” the look-back periods, the Trend Factor failed to beat momentum.

Constructing a portfolio of stocks using trend following seems to be a dead end. Our previous attempts at this — Dynamic Linear Model v1.0 and Dynamic Equity Trend-following — have yielded similar results as well.

Momentum beats Trend-Following.

Code and charts are on github.

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Standard breakout strategy

The book Following the Trend: Diversified Managed Futures Trading, Andreas Clenow, describes a simple “breakout” strategy:

If today’s close is higher or equal to the highest close in the past 50 days, we buy tomorrow; if the close is below or equal to the lowest close for the past 50 days, we sell open tomorrow and go short. A similar logic is used to get out of positions, where a long trade is sold when the close reaches the lowest point in 25 days and a short trade is covered when the price makes a 25-day high.

The book was published more than a decade ago and traders would’ve moved on from the basic strategy it described. However, we were curious if it ever worked at all on Indian indices. So, we ran a backtest.

Turns out, it never did.

Layering a trend filter seems to help a little.

While the strategy avoids some really steep drawdowns, the backtest doesn’t consider trading costs, taxes, etc.

While you could data-mine and get to a bunch of parameters that might work for “trading breakouts”, there is no reason why it should continue to work in the future.

Code and charts for other indices are on github.

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Intraday Momentum, an Update

Back in 2016, we ran a sniff test on Intraday Momentum: The First Half-Hour Return Predicts the Last Half-Hour Return (pdf). We promised an update so here it is (eight years later).

We ran the strategy with both the first 15min and 30min formations with and without considering gaps. It continues to not work with the three indices we used: NIFTY, BANK NIFTY and MID SELECT. Here’s the one for the NIFTY. The rest are on github.

Some strategies may benefit from becoming well known. However, a vast majority of them don’t. This one belongs to the former.

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Replication

Sometimes, you many not be able to directly buy an index or access a strategy because of regulatory hurdles, mandates etc… In these situations, replicating it using a basket of accessible securities might make sense.

For example, you can replicate the S&P 500 index using Indian indices: NIFTY 50, MIDCAP SELECT and NIFTY BANK.

In fact, you can do for any reference timeseries (strategies/funds) and apply leverage as desired.

Also, the loadings give you and idea of the shifting relationship between the reference asset and the basket over time.

Code and images are on github.

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