Trade tensions between China and the United States have created volatility in the global stock market in recent years, but their effects are difficult to quantify. News items related to tariffs and other factors, including trade barriers between the two countries, have made the analysis of these effects particularly challenging. For this reason, the literature has thus far mainly analysed case studies that refer to specific episodes with a time horizon of just a few days.
We contribute to the literature in three ways. First, we introduce a new trade sentiment index (TSI) that captures, in a more continuous fashion, the tone regarding trade in Chinese media, and examine its ability to explain the behaviour of global stock markets. Second, we analyse the effects of the China-US trade sentiment on stock market prices at the country, sectoral and firm level during the period January 2018–June 2019. Third, we disentangle the stock market effects of trade sentiment deriving from social media (ie media articles on the web, forums and multi-purpose social media platform WeChat) from those from traditional media (ie newspapers and magazines).
No equity market benefits from worsening China-US trade sentiment, and Asian markets tend to be more negatively affected. In particular, we find that sectors most affected by tariffs – such as those related to information technology – are particularly sensitive to the tone in trade tension. The TSI accounts for about 10% of the model’s capacity to explain stock price movements in countries significantly exposed to the China-US value chain, with social media accounting for the majority of the sentiment (9%) and traditional media only modestly (1%).
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.
JEL Codes: F13, F14, G15, D80, C45, C55
Keywords: stock returns, trade, sentiment, big data, neural network, machine learning