Beyond the Bell: Leveraging Off-market Data for AI-enabled Stock Directionality Forecast
Files
Date
2025-01-07
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
1193
Ending Page
Alternative Title
Abstract
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.
Description
Keywords
Data Science and Machine Learning to Support Business Decisions, ai-enabled directionality forecast, extended hours trading, post-market data, pre-market data
Citation
Extent
10
Format
Geographic Location
Time Period
Related To
Proceedings of the 58th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.