We discuss a network-based methodology that models hedge fund strategies across the superordinate-subordinate dimension to gain new insights into their interrelation. This methodology uncovers considerable misbehavior among various hedge fund strategies from the network perspective. Firstly, we...

One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by an in-depth analysis of topological metrics of correlation networks applied to financial forecasting. While academic research...

The popular risk parity approach is based on volatility as the sole risk measure and therefore lacks the consideration of tail risk. This fact makes risk parity portfolios vulnerable to tail events. In this article the authors address this issue by showing how higher risk moment terms can be...

Persistent homology is the workhorse of modern topological data analysis, which in recent years becomes increasingly powerful due to methodological and computing power advances. In this paper, after equipping the reader with the relevant background on persistent homology, we show how this tool...

This paper introduces novel financial predictors that are derived from the interaction profile of financial markets. These predictors utilize network-based and topological information. Since these predictors are derived from the inner dynamics (microstructure) of financial markets, they can be...