Stock Market Prediction via Multi-Source Multiple Instance Learning
Year: 2018
Type: article
Abstract: Forecasting the stock market movements is an important and challenging task. As the Web information grows, researchers begin to extract effective indicators (e.g., the events and sentiments) from the Web to facilitate the prediction. However, the indicators obtained in previous studies are usually based on only one data source and thus may not fully cover the factors that can affect the stock market movements. In this paper, to improve the prediction for stock market composite index movements, we exploit the consistencies among different data sources, and develop a multi-source multiple instance model that can effectively combine events, sentiments, as well as the quantitative data into a comprehensive framework. To effectively capture the news events, we successfully apply a novel event extraction and representation method. Evaluations on the data from the year 2015 and 2016 demonstrate the effectiveness of our model. In addition, our approach is able to automatically determine the importance of each data source and identify the crucial input information that is considered to drive the movements, making the predictions interpretable.
Source: IEEE Access
Authors: Xi Zhang, Siyu Qu, Jieyun Huang, Binxing Fang, Philip Yu
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,075
Awards: III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, G4593569228, MRI: Acquisition of SABER: Shared Analytics and Big-data Enterprise Resource, III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery
OpenAlex ID: W2890354514