Events like the 9/11 terrorist attacks can be destructive for global and local markets. Investors need to assess the sudden events' effects on a minute-by-minute basis to evaluate market reactions precisely for a better investment strategy. Despite the web crawler's availability, it is challenging to mine biased data; however, we build an artificial intelligence (AI) based model to make a better investment strategy that uses unexpected incidents like terrorist attacks and other natural disasters like floods and earthquakes. For this, we used Pakistan's real-time terrorist attacks, floods, and earthquakes data and Pakistan stock market KSE100 index data to feed the model which predicts swift market changes. We study unexpected real-world incident data and stock market data from 2001 to 2020.
Citation Request:
S. Aslam, A. Rasool, Q. Jiang and Q. Qu, "LSTM based Model for Real-time Stock Market Prediction on Unexpected Incidents," 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2021, pp. 1149-1153, doi: 10.1109/RCAR52367.2021.9517625.