MA — Market Abuse Detection
14 February 2022 Market Abuse. Results for the reduction of false positives alarmsRF MA imbalance FP |
In this last post we finally present if the Random Forest trained on the past activity of the compliance officer is able to classify an alarm as false positive or not.
11 February 2022 Market Abuse. Classification with high imbalanced datasetRF MA imbalance |
In this second post of the series, we introduce: the dataset used for the classification problem, the ML approach that better manages the high-imbalanced dataset and finally the statistical metrics used to measure the goodness of the results.
09 February 2022 Market Abuse. Is it possible to reduce false positive alarms?RF MA |
In this series, we explore the possibility to use classical supervised learning techniques to classify alarms generated by a system that implements the Market Abuse Regulation MAR. If successful, this machine learning application could help Compliance Officers to reduce the effort spent in the analysis and classification of the alarms generated.
21 September 2020 Market Abuse, Recommendation Systems and Anomaly DetectionRecSys AD MA |
As the first thread of our blog, we start with a rather ambitious project: applying Recommendation Systems techniques to create an anomaly detection tool. The approach is quite general, but here we will tell you specifically about our first experiment in the Market Abuse field. In this first post of the series we will briefly describe the terms of the matter.
