Mathematical Finance and Probability Seminars (Since covid these events are taking place online.)

This work presents a way to select macroeconomic factors important for the returns of individual assets from the financial data available. The factors selected are statistical, found using an unsupervised learning technique based on projection pursuit methodology. The factors we find are discovered directly from the past history of asset prices available on the market. The indices selected are based on monthly return data spanning 2000 to 2004 and the resulting model is tested on an independent set of returns for a different time period (2005 - 2007). We compare our approach with the Fama and French (1993) three factor model and with the Ross (1976) Arbitrage Pricing Theory. We discover that the procedure performs surprisingly well when compared with the traditional methods, as assessed by standard statistical measures.

Speaker: Ionut Florescu, Stevens Institute of Technology