From October 10th to October 15th, 2018, Associate Professor Fuwei JIANG was invited to participate in the 2018 International Conference of Financial Management Association (FMA) held in San Diego, USA, and to present his paper and attend academic discussions. Founded in 1970, the Financial Management Association (FMA) is a global leadership association that develops and disseminates financial academic knowledge. The title of the paper presented by Associate Professor Fuwei JIANG is "Real Time Macro Factors in Bond Risk Premium".

In addition, Professor Fuwei JIANG also attended lectures on machine learning econometrics, social media finance, text big data and finance, artificial intelligence finance and smart investment, and learned about new developing trends in the integration of asset pricing and behavioral finance, and in the integration of artificial intelligence and machine learning.

Real Time Macro Factors in Bond Risk Premium

Abstract:The notion that bond risk premium varies with business cycles is challenged once real time macro data are used. In this paper, we argue that the macro factors extracted by using the standard PCA are not the most relevant for forecasting bond risk premium, because the PCA factors are designed to explain the most variation of macro data instead of the variation of bond risk premium. With the latter objective in mind, we propose a scaled PCA (sPCA) approach, which incorporates the information in bond risk premium in the factor extraction procedure. The real time macro sPCA factors have much stronger predictive power than the PCA factors, both in- and out-of-sample, and generate sizeable utility gains. Alternative approaches, target PCA and PLS, obtain similar results. The sPCA factors also strongly nowcast macro data revision and forecast future macroeconomic conditions, consistent with implications of standard asset pricing theories, and the forecasting power appears countercyclical, with expected bond returns high in recessions and low in expansions.

Keywords: Bond Return Predictability, Real Time Macro Data, Vintage, PCA, Big Data, Machine Learning