Research on the Construction of Macro Assets Price Index based on Support Vector Machine

In this paper, a new macro assets price index (MAPI) is constructed based on support vector machine. In fact, 12 indicators, which can represent the macro economy well in both economically and statistically, are chosen to build our new index. Here, different from traditional econometric method, a novel machine learning method support vector regression machine (SVR) is employed to product the predictor of consumer price index (CPI) in China. In addition, in the experiment part, we also compare the result of SVR with that of least square regression (LSR) and vector autoregressive (VAR) impulse response analysis. The comparison shows that the latter two methods are hard to satisfy the requirement in both economically and statistically. On the contrary, SVR gives a good predictor of CPI and exhibits a manifest leading of CPI. In other words, our index can forecast the trends by 4 to 6 months, which is useful for investment and policy making