Nonlinear Tensor Completion Using Domain Knowledge: An Application in Analysts' Earnings Forecast

Ajim Uddin, Xinyuan Tao, Chia Ching Chou, Dantong Yu

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

Abstract

Financial analysts' earnings forecast is one of the most critical inputs for security valuation and investment decisions. However, it is challenging to utilize such information for two main reasons: missing values and heterogeneity among analysts. In this paper, we show that one recent breakthrough in nonlinear tensor completion algorithm, CoSTCo [1], overcomes the difficulty by imputing missing values and significantly improves the forecast accuracy in earnings. Compared with conventional imputation approaches, CoSTCo effectively captures latent information and reduces the tensor completion errors by 50%, even with 98% missing values. Furthermore, we show that using firm characteristics as auxiliary information we can improve firms' earnings prediction accuracy by 6%. Results are consistent using different performance metrics and across various industry sectors. Notably, the performance improvement is more salient for the sectors with high heterogeneity. Our findings imply the successful application of advanced ML techniques in a real financial problem.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
EditorsGiuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherIEEE Computer Society
Pages377-384
Number of pages8
ISBN (Electronic)9781728190129
DOIs
StatePublished - Nov 2020
Event20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy
Duration: Nov 17 2020Nov 20 2020

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2020-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
CountryItaly
CityVirtual, Sorrento
Period11/17/2011/20/20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

Keywords

  • Convolutional Neural Network
  • Finance
  • Firm Earnings Forecast
  • Nonlinear Tensor Factorization
  • Sparse Tensor Completion

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