Metaorders + reconstructing true trader intent

1:42:43I could just put in an order for 10. Or I could be like, "Okay, I want to buy two and then I'm going to buy three and then I'm going to buy two and then I'm going to buy three." That's four different little child orders for one big meta order, >> right? Which is which is that you bought 10. >> Yeah. Yeah. And um in normal in normal sort of trading in the data that you get if you want to analyze this kind of stuff, you only have access to the little orders. you don't have access to the guy who's right because each of these each of these um each of these people have like a virtual server. So each of the each of the little orders is might have like a virtual server ID and so it's going to be like pretty impossible to reconstruct who did >> which one. Right. >> Right. >> Okay. So this is where the Sato Kanazawa

1:43:25study >> that was just published in PRL um in 2025 in December 2025 comes in strict universality. They show that this is a very strict universality and it's a complete survey of the Tokyo Stock Exchange. Okay, so the Tokyo Stock Exchange what they did was they got a bunch of data from that stock exchange from 8 years. So from 2012 all the way to 2019 and the key innovation is they figured out a way to stitch together the effective trader IDs, so the big meta orders from these virtual server IDs. >> They kind of deanonymized it basically. >> Yes. Yeah, it's still anonymous, but they can pull them together

1:44:07>> to recognize a single trader. And the way they did it, the reconstruction method is like actually pretty pretty cool. Okay, so here's what they did. They developed a heristic that said, okay, I'm going to aggregate ID such that if server A submits an order and then server B cancels that order, the only way that's possible is if both of these servers are connected to the same trader. So every other time [laughter]