Reference request: Quantitative approaches to market abuse detection

External question — Quantitative Finance Stack Exchange Author: Mehness Original post: https://quant.stackexchange.com/questions/31937 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. have been asked to look at some financial timeseries for potential suspicious activity. These are stocks (my background fixed income hybrids trading and not forensic analyst...) and most of the conclusions will be drawn from granular trade level data (spoofing etc). However, there exist general quantitative / statistical approaches to detection of patterns of activity and informational asymmetry and am wondering if anyone can give me some references (regulatory or otherwise). I found this general survey but would like pointers on journals / books / papers to consult if possible: www.consob.it/documenti/quaderni/qdf54en.pdf (also I think Algorithmic Trading by Cartea et al has some paragraphs on infomational asymmetry, which will explore). There must be a substantial body of research on this, am hoping some might be public domain, can anyone help? Thanks EDIT: There's some interesting pattern recognition work if interested: https://webdocs.cs.ualberta.ca/~zaiane/postscript/DSAA2014.pdf ; http://www.ijtef.org/vol7/503-FR00023.pdf

External answer — Quantitative Finance Stack Exchange Author: mperlow Original post: https://quant.stackexchange.com/a/31951 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I can't help as much with public literature, but I did see a talk with a member of the FINRA data science team responsible for exactly this (event link below - perhaps you can track down the speaker). I don't know the structure of your data, but the approach FINRA took was to develop trader-level attributes (not stock level) to create profiles for each trader (I.e. Number of trades, volume, etc.) and applied unsupervised clustering algorithms to the traders. Once they did so, they determined which neighborhood clusters seemed okay and which were large outliers / potentially malicious. You didn't mention if your data set had trader id's, but this would likely be my approach and FINRA seemed to have success. I hope this helps and good luck! http://d1ryye6yw47pmy.cloudfront.net/images/3651/2016_Trading_Event.pdf?1463507984

External answer — Quantitative Finance Stack Exchange Author: andrew.paul.acosta Original post: https://quant.stackexchange.com/a/31992 License: CC BY-SA 3.0 — https://creativecommons.org/licenses/by-sa/3.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I cannot speak specifically because I have proprietary insight into the issue, but one approach is to compare the timing of trading activity (volume, bids/offers on the book traded and withdrawn) with publicly-announced news. Think Granger Causality, and which event seems to cause the other.