Network analysis and data science for finance: from traditional markets to decentralised exchanges

Thesis

Research on financial markets often confines itself to in-depth analyses of time series of asset prices, despite we are now in an era of unprecedented wealth of data that offers boundless opportunities for wider investigations. This thesis aims at broadening our understanding of traditional and decentralised market ecosystems, by taking indeed advantage of “unconventional data”. The latter are labelled as such either for their origin (i.e. being alternative data), or for their extensiveness (e.g. spanning multiple asset classes). Given the inherent higher complexity of our data, we leverage data science advancements to analyse them thoroughly. Recurrent techniques employed in this work include network science for capturing relationships among entities of interest, and clustering methods for dimensionality reduction and aggregation of information. Within traditional finance ecosystems, we investigate three sources of possible novel market insights, which indeed lead to alternative risk-monitoring tools. The first source lies in institutional investors’ holdings, which are found to signal crowding in trades, after aggregating the bipartite network of funds and their assets. Then, we consider a corpus of economic news with available timestamps. By modelling and clustering the interlinkage of concepts discussed within such news, we discover the major narratives of interest over time and map entropy in their state to market dislocations. Otherwise, we study returns of an heterogeneous set of indices belonging to multiple asset classes, and characterise their network of evolving correlations to identify market regimes that are found to have distinguishable macroeconomic features. Within decentralised finance ecosystems, we instead take direct advantage of the extensive and meticulous data-recording of blockchains. The trading activity of agents on multiple tokens is used to construct a network of transactions for each one of them, and clustering the set of such graphs allows us to identify interpretable “species” of traders. Lastly, we analyse data on liquidity provision, consumption, and price formation on competing decentralised exchange venues, to find a model for the prediction of incoming trading volume at block-level.

Authors: Miori, D

• DOI: 10.5287/ora-kobrggabo
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: machine learning; alternative data; financial markets; graph theory
• Subjects: Graph theory; Machine learning; Finance; Blockchain; Quantitative finance