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A Machine Learning-based Model for the Asymmetric Prediction of Accounting and Financial Information

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Fintech with Artificial Intelligence, Big Data, and Blockchain

Part of the book series: Blockchain Technologies ((BT))

Abstract

A company’s information asymmetry is an unfixed and a variable phenomenon that constantly changes. It is essential to manage information asymmetry in a company because it directly or indirectly affects various business risks. Therefore, in this study, we propose a method of application of machine learning techniques for predicting information asymmetry. We also address the necessity of adopting eXtreme Gradient Boosting (XGBoost) as a method for predicting information asymmetry by deriving the main factors affecting it. This study also systematically reviews previous studies on information asymmetry in the financial market. The review results indicate relevant explanatory variables that could be applied in a machine learning algorithm for predicting information asymmetry. This study proposes artificial intelligence (AI) model for predicting information asymmetry of firms, which has been difficult to measure. It is expected to reduce information asymmetry in the global financial market with emerging information technology like blockchain.

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Author information

Authors and Affiliations

  1. College of Business Administration, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, South Korea

    Minjung Park & Sangmi Chai

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  1. Minjung Park
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  2. Sangmi Chai
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Correspondence to Sangmi Chai .

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Editors and Affiliations

  1. Ewha Womans University, Seoul, Korea (Republic of)

    Paul Moon Sub Choi

  2. The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Seth H. Huang

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Park, M., Chai, S. (2021). A Machine Learning-based Model for the Asymmetric Prediction of Accounting and Financial Information. In: Choi, P.M.S., Huang, S.H. (eds) Fintech with Artificial Intelligence, Big Data, and Blockchain. Blockchain Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-33-6137-9_7

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  • DOI: https://doi.org/10.1007/978-981-33-6137-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6136-2

  • Online ISBN: 978-981-33-6137-9

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