One Step Ahead: A Framework for Detecting Unexpected Incidents and Predicting the Stock Markets

Unexpected incidents can be destructive or even disastrous, affecting the financial markets. Incidents such as the 9/11 attacks (2001), the Fukushima nuclear disaster (2011), and the COVID-19 outbreaks (2019, 2020) severely shocked both local and global markets. For investors, it is crucial to quantify the key facts that affect the incidents' impacts, and to estimate the reactions of the markets accurately and efficiently for global event-driven investment strategies. Though Web data and other alternative data allow such a possibility, it is still very challenging to mine noisy and often biased heterogeneous data sources, and construct a unified framework for modeling global markets across across time and regions. As a first attempt, we build a framework that extracts incident facts globally based on a deep neural network, feeds them into models built on a global event database complemented with novel socioeconomic datasets (e.g. nightlight data from satellites), and predicts stock market directions in a simulated real-world setting with interpretable results that outperform various baselines. Specifically, we study terrorist attacks in three countries for over 20 years on average, as a first effort to systematically quantify the impact on stock markets at a large scale using novel indicators.