Extracting the multi-timescale activity patterns of online financial markets

TL;DR

This paper introduces a non-negative tensor factorization method to analyze multi-timescale trading patterns in online financial markets, uncovering hidden behaviors and crisis-related anomalies in large-scale transaction data.

Contribution

The paper presents a novel application of NTF for multi-timescale analysis of financial trading dynamics, demonstrating its effectiveness on synthetic and real-world data.

Findings

Uncovered distinct trading strategies among banks.

Revealed crisis-related trading anomalies during 2008.

Validated methodology on large-scale interbank transaction data.

Abstract

Online financial markets can be represented as complex systems where trading dynamics can be captured and characterized at different resolutions and time scales. In this work, we develop a methodology based on non-negative tensor factorization (NTF) aimed at extracting and revealing the multi-timescale trading dynamics governing online financial systems. We demonstrate the advantage of our strategy first using synthetic data, and then on real-world data capturing all interbank transactions (over a million) occurred in an Italian online financial market (e-MID) between 2001 and 2015. Our results demonstrate how NTF can uncover hidden activity patterns that characterize groups of banks exhibiting different trading strategies (normal vs. early vs. flash trading, etc.). We further illustrate how our methodology can reveal "crisis modalities" in trading triggered by endogenous and exogenous system shocks: as an example, we reveal and characterize trading anomalies in the midst of the 2008 financial crisis.