Narratives such as news, messages and stories can affect people’s thinking and become significant factors in decision-making. Shiller (2017) uses the term “narrative economics” to describe a new economic subfield that studies how stories people tell each other affect economic fluctuations. This thesis considers the economic and political role of narratives in newspapers and social media. It consists of three chapters. Chapter 2 examines how information credibility affects the asset pricing of stocks and cryptocurrencies under news shocks. We first apply well-trained multiclassification algorithms in machine learning to analyse data drawn from newspapers and social media. Then we calculate the credibility of news shocks and study the abnormal returns on the corresponding market. Our results provide empirical evidence of the relationship between financial markets and news shocks in media narratives. Chapter 3 tackles the problem of measuring political and economic relations and uncertainty in China’s attitudes towards the United States based on a supervised computational linguistic analysis of an official newspaper of the Communist Party of China. We show that our indices outperform other text-based indicators in some situations, particularly in the financial market. Chapter 4 examines how nationalism interacts with the news censorship system and affects the political and economic relations between two conflicting parties and the negotiation process. We first introduce perceived payoffs to illustrate the role of nationalism in bilateral national negotiations. Based on a computational linguistic analysis of newspapers and social media in the US and China, and using the 2019 US-China trade war as a natural experiment, we show that nationalism in China can be suppressed by the government. Specifically, the party with weaker bargaining power may prefer to reach a negotiation agreement by suppressing its nationalism to maximise its utility.
|Date of Award||26 Jun 2020|
|Supervisor||Xiangdong WEI (Supervisor) & Cheng Ze Simon FAN (Supervisor)|