Abstract: Accurate prediction of future returns and risks in the stock market not only helps rational investors to invest more reasonably and effectively,but also provides useful guidance for policy makers and investors.Applying financial news headlines,this paper constructs an investor sentiment index that takes into account the cumulative effects of news using text analysis methods such as word embedding and machine learning.The Shanghai Composite Index is used as an example,and the empirical analysis decomposes the index’s fluctuation data into various inherent modes using the variational mode decomposition(VMD) method.Finally,the bidirectional gated recurrent unit(BiGRU) is introduced as a deep learning model for price fluctuation prediction.The results show that the investor sentiment index significantly affects the fluctuation of the Shanghai Composite index,and the influence of positive emotions and negative emotions is asymmetric.Considering the investor sentiment indicators and conducting the signal decomposition can effectively improve the prediction performance of stocks,and improve the prediction effect by up to 20%.In the benchmark scenario,the performance of VMD-BiGRU models is better than that of multiple econometric models and machine learning models,with higher accuracy and effectiveness,and the general performance of yield and volatility prediction is improved by more than 40%.The performance of model promotion in three stocks,Wuliangye,Industrial and Commercial Bank of China and IFLYTEK,maintain the same stable and accurate prediction effect.

Key words: Stock prediction, Investor sentiment, News media information, Signal decomposition, Gating unit