Volume, Absolute Returns, and Volatility Clustering
Summary
The document examines a local relationship between daily stock returns and trading volume. The author ranks each day’s return and volume within a rolling 30-day window, then compares the resulting distributions for same-day observations and for next-day returns paired with current-day volume. The observed pattern is that locally high volume coincides with returns that are often large in either direction, and the pattern appears to persist into the following day.
One explanation is that volume and volatility both cluster over time: periods of unusually large trading activity tend to accompany large absolute price moves, and elevated volatility can persist. This explains persistence in move size, but does not predict whether the next return will be positive or negative. A second answer offers a market-participant interpretation in which crowded buying or selling takes time to dissipate. The document presents these as explanations rather than a formal test; it does not identify the stock, quantify predictive strength, or establish that volume causes subsequent returns.
Key ideas
- High volume can coincide with large returns in either direction because it often accompanies high volatility.
- Volatility clustering can help explain why large absolute returns persist into the next day.
- Persistence in return magnitude does not determine the sign of the next return.
- Crowd behavior is offered as an additional interpretation, not as a tested causal mechanism.
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Full text
# local price return and volume relationship # local price return and volume relationship I wanted to see how the stock price and volume relationship is locally. So I tried ranking both the daily return (at day t) and volume (at day t) base on a 30 day rolling window with historical daily stock data and plotted the distribution as shown in the below figure (top). The ranking is based on sorting values in ascending order, and as you can see, when volume is locally high the price return is either locally low or high. I have also plotted the distribution of ranking of the next day return (at day t+1) and volume (at day t) to see if this "shape" remains, as shown in the figure (bottom), and it does. Can anyone explain why this shape still remains for day t+1? This kind of price volume relationship must have been researched extensively... is there some classic papers that you quant experts would recommend? Thanks. A screen shot of the code is provided here. ## Answer by edba (score 5, accepted) https://quant.stackexchange.com/a/2996 When volatility is high, daily volume is high. And when volatility is high, daily returns are high. That's why when volume is high, the price returns are high. Volatility (like volumes) is autocorrelated. This is the phenomenon of volatility clustering (large changes tend to be followed by large changes, of either sign) and volume clustering (large volumes tend to be followed by large volumes). It explains why when return is high (in absolute value) at day t it's likely to remain high (in absolute value) for day t+1. But of course it doesn't give you the sign of the return... ## Answer by Darren Cook (score 4) https://quant.stackexchange.com/a/3003 The technical analysis point of view: an increase in volume (assuming the price has been in a downtrend) means the crowd are throwing in the towel, i.e. everyone is dumping the stock and assuming that hoped-for rise is now never going to happen. The same on the way up: everyone jumps on the bandwagon. In other words, high volume typically means crowd psychology dominated. And that takes time to work its way out. Out of a given crowd population, people who missed it at `t`, will try and get in/out at `t+1`, and those still left will get in/out at `t+2`, etc. until the crowd is exhausted.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.