Financial-Networks

TL;DR for operators A portfolio can look diversified on a holdings report and still behave like one very crowded trade. The paper behind this article proposes a way to detect that crowding more rigorously: build a signed, weighted stock correlation network using statistical validation, then extract the largest module where every pair is strongly connected and every signed triangle is structurally balanced.1 ...

TL;DR for operators A recent paper, Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints, proposes a portfolio engine that first turns the S&P 500 into a dependency network, then strips that network down to a minimum spanning tree, then selects the five most central stocks, then allocates capital using risk-aware weights, and finally uses ARIMA or neural autoregressive forecasts to decide whether those positions deserve exposure on a given day.1 ...