Economic indicators rarely move in isolation. Dynamic Factor Models help uncover the hidden common signal behind variables such as jobless claims, financial conditions, building permits, and copper prices. In this article, we intuitively explore how a DFM works, how it differs from traditional regression, and how the extracted economic factor can be used to forecast ISM Manufacturing PMI.
We’ve all seen stocks that seem to move in sync, but how do you know if that relationship is built to last? Simple correlation can be a mirage, leading you into ‘Value Traps’ and failed pairs trades. In this blog, we explore Cointegration and Granger Causality—the two statistical powerhouses that reveal which stocks are permanently ‘leashed’ together and which one is truly leading the dance. Stop trading on vibes and start trading on verified relationships.
Spurious correlations can fool even experienced traders. Two stocks may appear to move in perfect sync, but the relationship often comes from shared market trends rather than any real influence between them. This blog focuses on how to uncover those false signals. By applying prewhitening, we remove the common market noise and isolate the true shocks — the moments when one asset genuinely leads another. It’s about cutting through illusions and finding real, actionable relationships.
In this blog we explore what spurious correlation is and how prewhitening can be used to eliminate such mistakes in our analysis of time series data. This is most important for people who wants to see cross correlation relationships between two time series.