Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Stock directionality forecasts are extremely useful in the financial market aiding in more informed trading decisions. However, it is difficult due to the highly volatile nature of the stock market. The majority of the stock trading takes place during the regular market hours whose data is mostly used for forecasts. Trades are also executed before the market opens (pre-market) and after the market closes (post-market). This off-market trading data is often ignored due to its minute trading volume. Exploration of this data for stock market forecasting is in its nascent state. We forecast the directionality of the end-of-the-day price using this off-market along with regular market hour data. The proposed AI-enabled framework extracts useful features from the off-market data, and 15 technical indicators based on regular market data followed by a tree-based prediction approach. The obtained results show performance improvements of over 7% in closing price directionality forecast when the off-market hour-based features are incorporated.
Recommended Citation
Mukherjee, Himadri; Das, Suchismita; Bose, Kaustav; Kumar, Kundan; Paul, Souren; and Ghosh, Alo, "Beyond the Bell: Leveraging Off-market Data for AI-enabled Stock Directionality Forecast" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 6.
https://aisel.aisnet.org/hicss-58/da/data_science/6
Beyond the Bell: Leveraging Off-market Data for AI-enabled Stock Directionality Forecast
Hilton Waikoloa Village, Hawaii
Stock directionality forecasts are extremely useful in the financial market aiding in more informed trading decisions. However, it is difficult due to the highly volatile nature of the stock market. The majority of the stock trading takes place during the regular market hours whose data is mostly used for forecasts. Trades are also executed before the market opens (pre-market) and after the market closes (post-market). This off-market trading data is often ignored due to its minute trading volume. Exploration of this data for stock market forecasting is in its nascent state. We forecast the directionality of the end-of-the-day price using this off-market along with regular market hour data. The proposed AI-enabled framework extracts useful features from the off-market data, and 15 technical indicators based on regular market data followed by a tree-based prediction approach. The obtained results show performance improvements of over 7% in closing price directionality forecast when the off-market hour-based features are incorporated.
https://aisel.aisnet.org/hicss-58/da/data_science/6