Abstract
The determination of the firm life cycle has been carried out in relation to the establishment of corporate strategy in the field of accounting or management. The life cycle prediction based on financial information is long because it is determined based on the financial performance of the entity over a year. This study sought to lay the foundation for overcoming this by using news articles to predict the life cycle of a company. In the process of quantifying news article data and predicting the firm life cycle, the method of selecting keywords that can represent the firm life cycle is presented, and the life cycle prediction model is verified with four machine learning techniques using selected candidate keywords. In this study, all four machine learning techniques showed a predicted static classification rate of nearly 60%, demonstrating the availability of news articles, which are unstructured text data, in predicting the corporate life cycle.
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References
C.P. Stickney, P.R. Brown, J.M. Wahlen, Financial reporting and statement analysis: a strategic perspective, South-Western Publishing, Mason, Ohio, 2004.
M. Gort, S. Klepper, Time paths in the diffusion of product innovations, Econ. J. 92 (1982), 630–653.
J.H. Anthony, K. Ramesh, Association between accounting performance measures and stock prices: a test of the life cycle hypothesis, J. Account. Econ. 15 (1992), 203–227.
E.L. Black, Life-cycle impacts on the incremental value-relevance of earnings and cash flow measures, J. Finan. State. Anal. 4 (1998), 40–57.
V. Dickinson, Cash flow patterns as a proxy for firm life cycle, Account. Rev. 86 (2011), 1969–1994.
Y.D. Kwon, Impact of firm’s life cycle and book value components on the security valuation, Korean Account. Rev. 21 (1996), 45–73.
J.M. Choi, Corporate life cycle and debt choice, Chungju Univ. Res. Inst. Bus. Econ. 10 (2017), 143–153.
S.M. Baik, D.C. Yang, J.M. Choi, J.B. Kim, Corporate life cycle and real earnings management, Korean Assoc. Bus. Educ. 26 (2011), 441–470.
H.S. Choi, J.I. Jang, S.C. Shin, The relative value-relevance of earnings and cash flow measures in each life-cycle stage, Korean Manage. Rev. 35 (2006), 1339–1360.
S.Y. Kwon, B.Y. Moon, Decomposed return on equity, future profitability, and value relevance over the firm life cycle, Korean Manage. Rev. 39 (2009), 1231–1249.
D.A. Bens, V. Nagar, M.H. Franco Wong, Real investment implications of employee stock option exercises, J. Account. Res. 40 (2002), 359–393.
H. DeAngelo, L. DeAngelo, R.M. Stulz, Dividend policy and the earned/contributed capital mix: a test of the life-cycle theory, J. Finan. Econ. 81 (2006), 227–254.
W. Park, S.K. Park, Value relevance of earnings and equity: role of corporate life cycle, Korean Manage. Rev. 39 (2010), 1451–1476.
W.K. Joo, Automatic classification method for atypical texts that include structure information using deep learning, Chungnam National University Graduate School, 2018.
One-Hot Encoding, 2020, Available from: www.sciencedirect.com.
H.P. Luhn, The automatic creation of literature abstracts, IBM J. Res. Dev. 2 (1958), 159–165.
K.S. Jones, A statistical interpretation of term specificity and its application in retrieval, Document Retriev. Syst. (1988), 132–142.
T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781, 2013.
H.J. Shin, B.T. Zhang, Y.T. Kim, Feature selection with nonlinear PCA in text categorization, Korean Inst. Inform. Sci. Eng. 26 (1999), 146–148.
H. Uğuz, A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm, Knowl. Based Syst. 24 (2011), 1024–1032.
D. Tsarev, M. Petrovskiy, I. Mashechkin, Using NMF-based text summarization to improve supervised and unsupervised classification, Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS), IEEE, Melacca, Malaysia, 2011.
S.M. Kim, A study on the investigating the changes and performance of the program provider market: an analysis based on generalized entropy index and panel data, Korea Univ. Seoul, Korea, 2015.
W. Jang, Y. Suh, Identifying abnormal accidents using local outlier factor and decision tree algorithms, J. Korean Inst. Ind. Eng. 45 (2019), 329–340.
W.I. Park, K.H. Kim, E. Han, S.M. Park, I.S. Yun, Study on the characteristics of bus traffic accidents by types using the decision tree, Int. J. Highway Eng. 18 (2016), 105–115.
S. Vijayarani, S. Dhayanand, Kidney disease prediction using SVM and ANN algorithms, Int. J. Comput. Bus. Res. 6 (2015), 1–12.
R. Moraes, J.F. Valiati, W. P. Gavião Neto. Document-level sentiment classification: an empirical comparison between SVM and ANN, Expert Syst. Appl. 40 (2013), 621–633.
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Lee, S.Y., Oh, S.Y., Lee, S. et al. The Firm Life Cycle Forecasting Model Using Machine Learning Based on News Articles. Int J Netw Distrib Comput 9, 1–9 (2021). https://doi.org/10.2991/ijndc.k.201218.002
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DOI: https://doi.org/10.2991/ijndc.k.201218.002