Author(s)

Yong Fang

School of Statistics and Mathematics, Shanghai Finance University, Shanghai, China.

This paper tried to utilize Ensemble Empirical Mode Decomposition (EEMD) to explore the correlations between investor sentiment and stock index and macro economy, as well as the prediction capacity of the short-term fluctuation, medium-term fluctuation and long-term trend of investor sentiment in the future stock market return. Firstly, dynamic factor model (DFM) was used to extract sentiment factors from 5 proxy variables of investor sentiment. Then, characteristics comparison and lead-lag relationship analyses were made on the short-term fluctuation, medium-term fluctuation and long-term trend of investor sentiment index, Shanghai Stock Index and macro index. Finally, whether the original signals of investor sentiment and each component can predict the size and the direction of future market returns was tested. Results indicated that high-frequency sentiment signals had a significant reverse prediction capacity on the short-term and medium-term future market returns. Moreover, low-frequency sentiment signals had a stronger prediction capacity in the direction of future market returns than original sentiment signals, high-frequency sentiment signals and residual signals.

Investor Sentiment, Stock Index, Macro Economy, DFM, EEMD

Fang, Y. (2015) A Study on the Correlations between Investor Sentiment and Stock Index and Macro Economy Based on EEMD Method. Journal of Financial Risk Management, 4, 206-215. doi: 10.4236/jfrm.2015.43016.