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.
Highlights
Social activity of individuals follows certain rhythms at different time scales
Given the similarity between social and financial temporal dynamics, justified by underlying human factors, and the fact that multiple activity cycles are present at different frequencies in human social activities, our question is whether similar rhythms exist in financial systems
As we show in this study of the Italian e-MID interbank market, various patterns and anomalies systematically emerge at different time scales, which should be taken into account in the design of financial regulations
Summary
Many of the individual activities, such as sending e-mails and making phone calls, are likely to be done in particular time intervals within a day (i.e., diurnal or circadian cycles), and the total daily activity could heavily depend on the day of the week (i.e., weekly cycles)[1,2,3,4,5,6] These social activity rhythms emerging at different time scales may be correlated with each other; for instance, it has been shown that face-to-face contacts between classmates in a primary school follow a common diurnal cycle driven by the daily class schedule[7,8], yet at the same time they would share activity rhythms at longer scales such as weekly and monthly, reflecting the annual school schedule. Non-negative tensor factorization (NTF) has been frequently used to extract temporal activity patterns in various social contexts, such as face-to-face contacts[8,16], Twitter posts[17] and players’ matches in online games[18] Data period No days Trading time Maturity length No banks (Italian banks) No total transactions Average no. daily participants Average no. daily transactions are dependent on each other, in which case banks exhibiting a given interday activity pattern are likely to follow a particular intraday pattern
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