FineNet: A joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items

Yu Che Tsai, Chih Yao Chen, Shao Lun Ma, Pei Chi Wang, You Jia Chen, Yu Chieh Chang, Cheng Te Li

研究成果: Conference contribution

6 引文 斯高帕斯(Scopus)

摘要

Financial technology (FinTech) draws much attention in these years, with the advances of machine learning and deep learning. In this work, given historical time series of stock prices of companies, we aim at forecasting upcoming anomalous fnancial items, i.e., abrupt soaring or diving stocks, in fnancial time series, and recommending the corresponding stocks to support fnancial operations. We propose a novel joint convolutional and recurrent neural network model, Financial Event Neural Network (FineNet), to forecast and recommend anomalous stocks. Experiments conducted on the time series of stock prices of 300 well-known companies exhibit the promising performance of FineNet in terms of precision and recall. We build FineNet as a Web platform for live demonstration.

原文English
主出版物標題RecSys 2019 - 13th ACM Conference on Recommender Systems
發行者Association for Computing Machinery, Inc
頁面536-537
頁數2
ISBN(電子)9781450362436
DOIs
出版狀態Published - 2019 九月 10
事件13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
持續時間: 2019 九月 162019 九月 20

出版系列

名字RecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
國家/地區Denmark
城市Copenhagen
期間19-09-1619-09-20

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 資訊系統
  • 電腦科學應用

指紋

深入研究「FineNet: A joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items」主題。共同形成了獨特的指紋。

引用此