Applications of Signature Methods to Market Anomaly Detection
This work addresses market anomaly detection for financial analysts, but it is incremental as it applies existing signature methods to new data.
The study tackled anomaly detection in time series data by proposing a signatures-based machine learning algorithm, achieving up to 88% F1 scores in identifying cryptocurrency pump and dump attempts, close to state-of-the-art supervised methods.
Anomaly detection is the process of identifying abnormal instances or events in data sets which deviate from the norm significantly. In this study, we propose a signatures based machine learning algorithm to detect rare or unexpected items in a given data set of time series type. We present applications of signature or randomized signature as feature extractors for anomaly detection algorithms; additionally we provide an easy, representation theoretic justification for the construction of randomized signatures. Our first application is based on synthetic data and aims at distinguishing between real and fake trajectories of stock prices, which are indistinguishable by visual inspection. We also show a real life application by using transaction data from the cryptocurrency market. In this case, we are able to identify pump and dump attempts organized on social networks with F1 scores up to 88% by means of our unsupervised learning algorithm, thus achieving results that are close to the state-of-the-art in the field based on supervised learning.