کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6861819 | 1439258 | 2018 | 41 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improving stock market prediction via heterogeneous information fusion
ترجمه فارسی عنوان
بهبود پیش بینی بازار سهام از طریق همگام سازی اطلاعات ناهمگن
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کلمات کلیدی
رسانه های اجتماعی، همبستگی سهام، تخمین تانسور، پیش بینی سهام، 00-01، 99-00،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In this work, we extract the events from Web news and the users' sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsic relations among the events and the investors' sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 143, 1 March 2018, Pages 236-247
Journal: Knowledge-Based Systems - Volume 143, 1 March 2018, Pages 236-247
نویسندگان
Xi Zhang, Yunjia Zhang, Senzhang Wang, Yuntao Yao, Binxing Fang, Philip S. Yu,