How does information credibility, a subjective judgment of investors, affect empirical asset pricing in financial markets? Traditional economic theories are inadequate for interpreting market responses driven by people's subjective thinking, as these cognitive processes are not encompassed by the concept of utility. We explore these effects by using computational linguistics and deep structured learning algorithms to analyze financial newspapers and social media posts. After controlling for factors related to content and market momentum in our narrative based credibility indicator, we find that news credibility is positively correlated with the returns on assets preferred by experts and negatively correlated with assets preferred by gamblers. Based on this finding, we point out that the efficient-market hypothesis (EMH) is not appropriate in the dominant market of gamblers in the short-term. In the long-term, however, investment motivation does not significantly affect the validity of the hypothesis.

This chapter examines the use of artificial intelligence (AI) techniques in natural language processing (NLP) for risk management, with a particular focus on applications in the field of political economics. The aim of this analysis is to identify and measure potential political risks by conducting a textual analysis of newspapers and social media, using sentiment scores as proxies for nationalism. The study uses the 2019 US-China Trade War as a natural experiment to evaluate the impact of international disputes on political risks. One significant finding is the positive effect of the trade war on sentiment in China’s media about the US, which is attributed to the Chinese government’s efforts to mitigate the negative impact of the trade war on international relations. The study also reveals a negative impact on bilateral imports due to the conflict. Furthermore, the study employs a Difference-in-Difference (DID) model to investigate the impact of news censorship on media during the trade war. It is found that China’s regulators attempted to soften domestic anti-US sentiment, while the US media reported more negatively about China during the conflict. Overall, this analysis demonstrates how NLP technology can be effectively used to identify changes in the management of political risks by analysing news and other media.

In this paper, we develop a computational linguistic approach based on supervised machine learning using the People’s Daily to measure Chinese official relations and political uncertainty towards the US. In the first step, we create training samples by asking experts to manually annotate news articles. In the second step, we use supervised machine learning algorithms to adjust our single neural network and support vector machine classifiers to better fit our training data. Finally, we combine our two individual classifiers and a dictionary approach to automatically detect whether an article in the newspaper sample is relevant. Using all of the relevant textual data, we then apply the computational linguistic approach to generate state-of-the-art indices and show that our indices outperform similar current textual indicators in some situations, particularly in the financial market.