The power of complex systems' information-based networks to make predictions: from finance to fashion industry

Data are everywhere and they carry information. Using, understanding and filtering such information has become a major activity across science, industry and society at large. It is therefore important to have tools that can analyse this information and that reduce complexity while keeping the integrity of the dataset and that can provide meaningful information that can be used for prediction.  

In this talk I will show how tools based on network theory combined with recommendation methods can be used to build information filtering networks that retain the relevant part of the data-interdependency structure. Two applications to complex financial systems and fashion industry based on google-trend data will be presented. Topological constrains to financial networks can meaningfully identify industrial activities and structural market changes. They can be used for risk management and portfolio optimization, for forecasting, stress testing and risk allocation. Collaborative filtering procedures combined with recommendation methods also provide significant and robust predicting results of cobranding partnerships and cobranded products in the fashion industry. Results are general and can be applied also outside the financial and marketing sector.