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Predicting Organization Performance Changes: A Sequential Data-Based Framework.

Meiqi Song1, Xiangling Fu1, Shan Wang2

  • 1Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.

Frontiers in Psychology
|June 6, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a sequential data framework to predict company performance changes and risks. The model aids investors in identifying business decline and recovery, outperforming static models with 92.3% accuracy.

Keywords:
a sequential data-based frameworkbi-directional long short-term memory (Bi-LSTM)business declinebusiness recoverynews sentimentorganization performance changesrisk warning status

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Area of Science:

  • Business and Economics
  • Data Science
  • Financial Markets

Background:

  • Increasing business environment uncertainty due to technological disruption, global economic shifts, and the COVID-19 pandemic.
  • Challenges for investors in predicting company performance changes and associated risks.
  • Limitations of existing models focusing solely on risk warning prediction.

Purpose of the Study:

  • To propose a sequential data-based framework for predicting organization performance changes.
  • To aggregate diverse data sources, including structured and unstructured data, for enhanced prediction.
  • To assist investors in predicting risks and identifying investment opportunities by forecasting business decline and recovery.

Main Methods:

  • Development of a sequential data-based framework utilizing data from China's early risk warning system.
  • Aggregation of multi-source data, encompassing both structured and unstructured information.
  • Prediction of a portfolio of organization performance changes, including business decline and recovery.

Main Results:

  • Achieved a 92.3% macro-F1 value on real-world data from listed companies in China.
  • Demonstrated superior performance compared to existing static models.
  • Successfully predicted business decline and recovery patterns.

Conclusions:

  • The proposed framework effectively predicts organization performance changes and associated risks.
  • Sequential data incorporation enhances prediction accuracy for business dynamics.
  • The model offers valuable insights for investors navigating market uncertainties and seeking opportunities.