Market statistics and technical analysis

Read it years ago when I was deep in the volume-price relationship rabbit hole. The core argument stuck with me, volume carries information that price alone misses. Their model basically says traders with better info trade more aggressively when they think they're right, so volume spikes can signal informed flow even before price moves. It makes sense in the abstract but applying it to actual algos is where it gets murky. The paper is from 94 so the market microstructure they describe is ancient history compared to HFT and fragmented liquidity now. Still, the intuition holds up if you think about it as a framework rather than a literal recipe. Worth reading if you're building anything volume based, just don't expect to copy their methodology and print money.

Laughing at the money printing comment! I’ve been struggling with including volume as an input to models. I can’t tell if it helps or not. This paper encourages me to look at including it again.

I totally get the struggle – volume alone never gives a clean signal without context. In my experience the trick is to transform it: use signed volume (buy minus sell) or normalize against a rolling volume profile. That usually surfaces the informed flow the paper talks about without getting lost in the overall trend.

agreed, the intuition ages way better than the actual microstructure assumptions do

read it a while back. the point that holds up is that price alone can't tell you how good the information behind a move was, volume carries that, so in their model looking at volume is rational. what it never gives you is a rule that survives costs.

Agreed. I think that I'll try adding volume back into my HMM fits.

worth trying. feed it as log volume over its own 20 day average rather than raw, otherwise the states end up sorting by the multi year volume trend instead of by regime.

Thank you! I'll do that. I did a whole state parameter sweep from 2 to 10, and found 3 to be best for just 1 minute log gain inputs. If I add volume, I may need to add another state. Also, I'll be sure to use a full covariance.

3 states for 1-min log returns sounds right, past that the extra states mostly split noise. with volume in, I'd check the state means are still stable on a later chunk of data before trusting the new one.

From the paper:

Volume is typically defined as the number of shares of the risky asset that are traded. Since every trade involves a buyer and a seller, volume could be calculated by simply adding up all buy orders or all sell orders. An equivalent approach in a Walrasian equilibrium is to sum the absolute value of traders demands and divide by two.

Looks like the authors are not clear on how they are supposed to calculate the volume. Many traders use the Volume from the OHLCV indicator that sums all transactions per unit of time. The colour of the Volume bar depends on the difference between the Open price and the Close price. The most clear definition I've read is from the more fundamental law of supply and demand (implied by the price definition). The graphs representing supply or demand shifts (see them on the Wikipedia page) suggests the existence of 3 dimensions: quantity, price and time. I think the volume should really be considered altogether with the price and the time but we really lack scientific consensus over these 3 concepts and how to calculate them the right way.

I think the authors’ definition of volume is consistent with the volume column included with ohlcv bar data. They are adding the buy fill shares and the sell fill shares and dividing by two to avoid double counting.

adding the buy fill shares and the sell fill shares

I'm confused because they wrote ***or***, not ***and***.

Their wording is confusing. The “or” sentence is saying that you can use the sum of the buys or the sum of the sells since the two are matched.

The paper argues volume shows information price alone misses which is why confirming a moon call with volume makes sense