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

Published: 10 September 2019 Publication History

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 financial items, i.e., abrupt soaring or diving stocks, in financial time series, and recommending the corresponding stocks to support financial 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.

References

[1]
Wei Bao, Jun Yue, and Yulei Rao. 2017. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOS ONE 12 (2017), 1--24.
[2]
Andrea Gigli, Fabrizio Lillo, and Daniele Regoli. 2017. Recommender Systems for Banking and Financial Services. In Proceedings of ACM International Conference on Recommender Systems (Posters).
[3]
Yunchuan Sun, Mengting Fang, and Xinyu Wang. 2018. A Novel Stock Recommendation System Using Guba Sentiment Analysis. Personal Ubiquitous Comput. 22, 3 (2018), 575--587.
[4]
David Zibriczky. 2016. Recommender Systems meet Finance: A literature review. In Proceedings of 2nd International Workshop on Personalization and Recommender Systems in Financial Services.

Cited By

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  • (2024)Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision MarketingCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648346(461-470)Online publication date: 13-May-2024
  • (2024)A Two-stage Recommendation Optimization Algorithm Based on Item Popularity and User FeaturesHeliyon10.1016/j.heliyon.2024.e38195(e38195)Online publication date: Sep-2024
  • (2023)Stock Selection Using Machine Learning Based on Financial RatiosMathematics10.3390/math1123475811:23(4758)Online publication date: 24-Nov-2023
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Published In

cover image ACM Other conferences
RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems
September 2019
635 pages
ISBN:9781450362436
DOI:10.1145/3298689
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

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Author Tags

  1. CNN
  2. RNN
  3. anomalous financial items
  4. recommendation

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  • Demonstration

Conference

RecSys '19
RecSys '19: Thirteenth ACM Conference on Recommender Systems
September 16 - 20, 2019
Copenhagen, Denmark

Acceptance Rates

RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2024)Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision MarketingCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648346(461-470)Online publication date: 13-May-2024
  • (2024)A Two-stage Recommendation Optimization Algorithm Based on Item Popularity and User FeaturesHeliyon10.1016/j.heliyon.2024.e38195(e38195)Online publication date: Sep-2024
  • (2023)Stock Selection Using Machine Learning Based on Financial RatiosMathematics10.3390/math1123475811:23(4758)Online publication date: 24-Nov-2023
  • (2023)Semi-supervised Curriculum Ensemble Learning for Financial Precision MarketingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615251(3773-3777)Online publication date: 21-Oct-2023
  • (2023)A knowledge graph–GCN–community detection integrated model for large-scale stock price predictionApplied Soft Computing10.1016/j.asoc.2023.110595145:COnline publication date: 1-Sep-2023
  • (2022)FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable StocksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3079496(1-1)Online publication date: 2022
  • (2022)Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filteringExpert Systems with Applications10.1016/j.eswa.2021.116311192(116311)Online publication date: Apr-2022
  • (2022)Layer-Wise Optimization of Contextual Neural Networks with Dynamic Field of AggregationIntelligent Information and Database Systems10.1007/978-3-031-21967-2_25(302-312)Online publication date: 9-Dec-2022
  • (2021)Towards Layer-Wise Optimization of Contextual Neural Networks with Constant Field of AggregationIntelligent Information and Database Systems10.1007/978-3-030-73280-6_59(743-753)Online publication date: 7-Apr-2021
  • (2021)The Impact of Aggregation Window Width on Properties of Contextual Neural Networks with Constant Field of AttentionIntelligent Information and Database Systems10.1007/978-3-030-73280-6_58(731-742)Online publication date: 7-Apr-2021
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