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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages536-537
Number of pages2
ISBN (Electronic)9781450362436
DOIs
Publication statusPublished - 2019 Sep 10
Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
Duration: 2019 Sep 162019 Sep 20

Publication series

NameRecSys 2019 - 13th ACM Conference on Recommender Systems

Conference

Conference13th ACM Conference on Recommender Systems, RecSys 2019
CountryDenmark
CityCopenhagen
Period19-09-1619-09-20

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Computer Science Applications

Cite this

Tsai, Y. C., Chen, C. Y., Ma, S. L., Wang, P. C., Chen, Y. J., Chang, Y. C., & Li, C. T. (2019). FineNet: A joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items. In RecSys 2019 - 13th ACM Conference on Recommender Systems (pp. 536-537). (RecSys 2019 - 13th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/3298689.3346968