DP15682 Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media

Author(s): Marlene Amstad, Leonardo Gambacorta, Chao He, Fan Dora Xia
Publication Date: January 2021
Keyword(s): Big Data, Machine Learning, neural network, sentiment, Stock returns, Trade
JEL(s): C45, C55, D80, F13, F14, G15
Programme Areas: Financial Economics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15682

Trade tensions between China and US have played an important role in swinging global stock markets but effects are difficult to quantify. We develop a novel trade sentiment index (TSI) based on textual analysis and machine learning applied on a big data pool that assesses the positive or negative tone of the Chinese media coverage, and evaluates its capacity to explain the behaviour of 60 global equity markets. We find the TSI to contribute around 10% of model capacity to explain the stock price variability from January 2018 to June 2019 in countries that are more exposed to the China-US value chain. Most of the contribution is given by the tone extracted from social media (9%), while that obtained from traditional media explains only a modest part of stock price variability (1%). No equity market benefits from the China-US trade war, and Asian markets tend to be more negatively affected. In particular, we find that sectors most affected by tariffs such as information technology related ones are particularly sensitive to the tone in trade tension.