The purpose of this research is to introduce an innovative decision architecture to assess corporate risks by utilizing accounting narratives and to further examine the association between those risks and operating performance. We run text mining algorithms to identify the types of risks from narratives and incorporate them with a readability measure (i.e., linguistic cue(s)) to conjecture a firm manager’s attitude toward corporate risk. We consider a two-stage network data envelopment analysis (TNDEA) with the benefit of opening up the black-box of a production process via considering internal activities. The outcome derived from TNDEA is determined by users who decide “a priori” what the specification of the model should be, without considering any alternatives. The study further incorporates the fuzzy rough set theory (FRST) with TNDEA by considering inclusion/exclusion or different combinations of inputs, intermediates, and outputs so as to realize a corporate’s underlying business situation. Decision attributes taken from the outcome of FRST-TNDEA and condition attributes gathered from annual reports are jointly inserted into an artificial intelligence (AI) technique to establish the forecasting model. The integrated circuit (IC) industry has long been viewed as an essential backbone of Taiwan’s economy, and therefore it is taken as our research target. The results show that the introduced linguistic cues have positive and considerable impacts on firm performance. Overall, the findings herein provide direct support for recent regulators that require corporates to add a new section on risk factors in accounting narratives so as to prevent users from making biased judgments.
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Altman, E. I. (1968). Financial ratios discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
Amado, C. A. F., Santos, S. P., & Marques, P. M. (2012). Integrating the data envelopment analysis and the balanced Scorecard approaches for enhanced performance assessment. Omega, 40(3), 390–403.
Anderson, T. R., Hollingsworth, K. B., & Inman, L. B. (2002). The fixed weighting nature of a cross-evaluation model. Journal of Productivity Analysis, 18(1), 249–255.
Ahn, J. J., Oh, K. J., Kim, T. Y., & Kim, D. H. (2011). Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Systems with Applications, 38(4), 2966–2973.
Ahmed, S., Hasan, M. Z., Laokri, S., Jannat, Z., Ahmed, M. W., Dorin, F., Vargas, V., & Khan, J. A. M. (2019). Technical efficiency of public district hospitals in Bangladesh: A data envelopment analysis. Cost Effectiveness and Resource Allocation, 17, 15.
Banker, R. D. (1993). Maximum likelihood, consistency and data envelopment analysis: A statistical foundation. Management Science, 39, 1265–1273.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.
Basso, A., Casarin, F., & Funari, S. (2018). How well is the museum performing? A joint use of DEA and BSC to measure the performance of museums. Omega, 81, 67–84.
Bauer, T. N., Maertz, C. P., Jr., Dolen, M. R., & Campion, M. A. (1998). Longitudinal assessment of applicant reactions to employment testing and test outcome feedback. Journal of Applied Psychology, 83(6), 892–903.
Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.
Beattie, V., McInnes, W., & Fearnley, S. (2004). A methodology for analysing and evaluating narratives in annual reports: A comprehensive descriptive profile and metrics for disclosure quality attributes. Accounting Forum, 28(3), 205–236.
Beneish, M. D., Miller, B. P., & Yohn, T. L. (2015). Macroeconomic evidence on the impact of mandatory IFRS adoption on equity and debt markets. Journal of Accounting and Public Policy, 34(1), 1–27.
Bernardo, M., Souza, M. A. M., Lopes, R. S. M., & Rodrigues, L. F. (2020). University library performance management: Applying zero-sum gains DEA models to resource allocation. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2020.100808
Blanco, B., Dhole, S. (2017). Financial statement comparability, readability and accounting fraud. working paper. University of Adelaide.
Blei, D., Ng, A., & Jordan, M. (2003a). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Blei, D., Griffiths, T., Jordan, M., Tenenbaum, J. (2003b). Hierarchical topic models and the nested Chinese restaurant process. Neural Information Processing Systems 16.
Bordo, M. D., & Haubrich, J. G. (2017). Deep recessions, fast recoveries, and financial crises: Evidence from the American record. Economic Inquiry, 55(1), 527–541.
Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Chakravarthy, B. S. (1986). Measuring strategic performance. Strategic Management Journal, 7, 437–458.
Chang, T. M., Hsu, M. F., & Lin, S. J. (2018). Integrated news mining technique and AI-based mechanism for corporate performance forecasting. Information Sciences, 424, 273–286.
Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272.
Chen, Y., & Zhu, J. (2004). Measuring information technology’s indirect impact on firm performance. Information Technology and Management, 5(1–2), 9–22.
Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research, 196(3), 1170–1176.
Chuang, J., Roberts, M. E., Stewart, B. M., Weiss, R., Tingley, D., Grimmer, J., Heer., J. (2015). TopicCheck: In teractive alignment for assessing topic model stability. In HLT-NAACL, 175–184.
Cinca, C., & Molinero, C. (2004). Selecting DEA specifications and ranking units via PCA. Journal of Operational Research Society, 55, 521–528.
Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1–4.
Cook, W. D., Liang, L., & Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, 38(6), 423–430.
Cook, W. D., Zhu, J. (2015). DEA cross efficiency, International Series in Operations Research & Management Science, In: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 2, pp. 23–43,
Cook, W. D., Ramón, N., Ruiz, J. L., Sirvent, I., & Zhu, J. (2019). DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans. Omega, 84, 45–54.
Cron, W., & Sobol, M. (1983). The relationship between computerization and performance: A strategy for maximizing economic benefits of computerization. Information and Management, 6, 171–181.
DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding. Poetics, 41(6), 570–606.
Färe, R., & Primont, D. (1984). Efficiency measures for multi plant firms. Operations Research Letters, 3, 257–260.
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Feldman, T. (2010). Portfolio manager behavior and global financial crises. Journal of Economic Behavior and Organization, 75(2), 192–202.
Frydman, H., Altman, E. I., & Kao, D. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance, 40, 269–291.
Gajzler, M. (2010). Text and data mining techniques in aspect of knowledge acquisition for decision support system in construction industry. Technological and Economic Development of Economy, 16(2), 219–232.
Gong, Y., Zhu, J., Chen, Y., & Cook, W. D. (2018). DEA as a tool for auditing: Application to Chinese manufacturing industry with parallel network structures. Annals of Operations Research, 263, 247–269.
Goel, S., Gangolly, J., Faerman, S. R., & Uzuner, O. (2010). Can Linguistic Predictors Detect Fraudulent Financial Filings? Journal of Emerging Technologies in Accounting, 7(1), 25–46.
Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250.
Graneheim, U. H., & Lundman, B. (2004). Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105–112.
Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing and Management, 54(6), 1292–1307.
Halkos, G. E., Tzeremes, N. G., & Kourtzidis, S. A. (2014). A unified classification of two-stage DEA models. Surveys in Operations Research and Management Science, 19(1), 1–16.
Hao, P. Y., Kung, C. F., Chang, C. Y., & Ou, J. B. (2021). Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane. Applied Soft Computing, 98, 106806.
Hsu, M. F. (2019a). A fusion mechanism for management decision and risk analysis. Cybernetics and Systems, 50(6), 497–515.
Hsu, M. F. (2019b). Integrated multiple-attribute decision making and kernel-based mechanism for risk analysis and evaluation. Journal of Intelligent and Fuzzy Systems, 36(3), 2895–2905.
Hsu, M. F., Chang, T. M., & Lin, S. J. (2020). News-based soft information as a corporate competitive advantage. Technological and Economic Development of Economy, 26(1), 48–70.
Hsu, M. F., & Lin, S. J. (2021). A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-021-01331-7
Hou, C., Lu, W., & Hung, S. (2019). Does CSR matter? Influence of corporate social responsibility on corporate performance in the creative industry. Annals of Operations Research, 278, 255–279.
Hu, K. H., Hsu, M. F., Chen, F. H., & Liu, M. Z. (2021). Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment. Financial Innovation., 7, 10.
Huang, A., Zang, A., & Zheng, R. (2014). Evidence on the information content of text in analyst reports. Accounting Review., 89, 2151–2180.
Huang, H., Wei, X., & Zhou, Y. (2018). Twin support vector machines: A survey. Neurocomputing, 300, 34–43.
Jayadeva, K., & R., Chandra, S. . (2007). Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 905–910.
Kamei, T. (1997). Risk management (in Japanese). Tokyo: Dobunkan.
Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.
Kao, C. (2014). Network data envelopment analysis: A review. European Journal of Operational Research, 239(1), 1–16.
Kao, C., & Liu, S. T. (2021). Group decision making in data envelopment analysis: A robot selection application. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.05.013
Kim, S. Y., & Upneja, A. (2014). Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models. Economic Modelling, 36, 354–362.
Khemchandani, R., Saigal, P., & Chandra, S. (2018). Angle-based twin support vector machine. Annals of Operations Research, 269, 387–417.
Kohut, G. F., & Segars, A. H. (1992). The president’s letter to stockholders: An examination of corporate communication strategy. Journal of Business Communication, 29(1), 7–21.
Kumar, B., & Gupta, D. (2021). Universum based Lagrangian twin bounded support vector machine to classify EEG signals. Computer Methods and Programs in Biomedicine, 208, 106244.
Lewis, H. F., & Sexton, T. R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers and Operations Research, 31(9), 1365–1410.
Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics, 45(2–3), 221–247.
Li, H., Chen, C., Cook, W. D., Zhang, J., & Zhu, J. (2018). Two-stage network DEA: Who is the leader? Omega, 74, 15–19.
Li, Y., Wang, Y., & Cui, Q. (2015). Evaluating airline efficiency: An application of virtual frontier network SBM. Transportation Research Part e: Logistics and Transportation Review, 81, 1–17.
Liu, L., Chu, M., Yang, Y., & Gong, R. (2020). Twin support vector machine based on adjustable large margin distribution for pattern classification. International Journal of Machine Learning and Cybernetics, 11, 2371–2389.
Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55, 643–653.
Lo, K., Ramos, F., & Rogo, R. (2017). Earnings management and annual report readability. Journal of Accounting and Economics, 63(1), 1–25.
Lu, W. M., & Hung, S. W. (2011). Exploring the operating efficiency of technology development programs by an intellectual capital perspective-A case study of Taiwan. Technovation, 31(8), 374–383.
Lu, W. M., Kweh, Q. L., & Wang, C. W. (2019). Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03233-y
Lu, W., Kweh, Q. L., & Yang, K. (2020). Multiplicative efficiency aggregation to evaluate Taiwanese local auditing institutions performance. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03592-x
Löthgren, M., & Tambour, M. (1999). Productivity and customer satisfaction in Swedish pharmacies: A DEA network model. European Journal of Operational Research, 115(3), 449–458.
Lozano, S., & Gutiérrez, E. (2014). A slacks-based network DEA efficiency analysis of European airlines. Transportation Planning and Technology, 37(7), 623–637.
Mahmoudabadi, M. Z., Azar, A., & Emrouznejad, A. (2018). A novel multilevel network slacks-based measure with an application in electric utility companies. Energy, 158, 1120–1129.
Mahmoudabadi, M. Z., & Emrouznejad, A. (2019). Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis. International Review of Economics and Finance, 64, 359–376.
Magnusson, C., Arppe, A., Eklund, T., Back, B., Vanharanta, H., & Visa, A. (2005). The language of quarterly reports as an indicator of change in the company’s financial status. Information & Management, 42(4), 561–574.
Nemati, M., Kazemi Matin, R., & Toloo, M. (2020). A two-stage DEA model with partial impacts between inputs and outputs: Application in refinery industries. Annals of Operations Research, 295, 285–312.
Nowak-Brzezińska, A., & Wakulicz-Deja, A. (2019). Exploration of rule-based knowledge bases: A knowledge engineer’s support. Information Sciences, 485, 301–318.
Odom, M., Sharda, R. (1990). Neural networks model for bankruptcy prediction, In: Proceedings of the IEEE International Conference on Neural Network, 2, 163-168
Ohlson, J. (1980). Financial ratio and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18, 109–131.
Pavlinek, M., & Podgorelec, V. (2017). Text classification method based on self-training and LDA topic models. Expert Systems with Applications, 801, 83–93.
Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99–109.
Peters, M. D., Wieder, B., Sutton, S. G., & Wakefield, J. (2016). Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage. International Journal of Accounting Information Systems, 21, 1–17.
Prieto, A. M., & Zofío, J. L. (2007). Network DEA efficiency in input–output models: With an application to OECD countries. European Journal of Operational Research, 178(11), 292–304.
Schroeder, M. (2002). SEC proposes rules to improve disclosure by public companies. Wall Street Journal.
Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2013). Partial least square discriminant analysis for bankruptcy prediction. Decision Support Systems, 54, 1245–1255.
Seiford, L. M., & Zhu, J. (1999). An investigation of returns to scale in data envelopment analysis. Omega, 27(1), 1–11.
Sexton, T. R., Silkman, R. H., Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. In: Silkman, R. H. (ed) Measuring efficiency: an assessment of data envelopment analysis, 32, pp. 73–105.
Shafiee, M., Lotfi, F. H., & Saleh, H. (2014). Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach. Applied Mathematical Modelling, 38(21–22), 5092–5112.
Shao, Y. H., Wang, Z., Chen, W. J., & Deng, N. Y. (2013). A regularization for the projection twin support vector machine. Knowledge-Based Systems, 37, 203–210.
Shen, F., Zhao, X., Li, Z., Li, K., & Meng, Z. (2019). A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation. Physica A: Statistical Mechanics and Its Applications, 526, 121073.
Sun, J., Li, H., Fujita, H., Fu, B., & Ai, W. (2020). Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Information Fusion, 54, 128–144.
Tang, J., Tian, Y., Wu, G., Li, D. (2017). Stochastic gradient descent for large-scale linear nonparallel SVM. WI '17: Proceedings of the International Conference on Web Intelligence, pp. 980–983, https://doi.org/10.1145/3106426.3109427.
Tsai, B. H., & Li, Y. (2009). Cluster evolution of IC industry from Taiwan to China. Technological Forecasting and Social Change, 76(8), 1092–1104.
Thangavel, K., Karnan, M., & Pethalakshmi, A. (2005). Performance analysis of rough reduct algorithms in mammogram. International Journal on Global Vision and Image Processing, 5(8), 13–21.
Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143, 32–41.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(116), 243–252.
Wang, Y., Pan, J., Pei, R., Yi, B. W., & Yang, G. (2020). Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2020.100810
Wang, Z., Shao, Y. H., Bai, L., Li, C. N., & Deng, N. Y. (2018). Insensitive stochastic gradient twin support vector machines for large scale problems. Information Sciences, 462, 114–131.
Wang, C. H., Gopal, R. D., & Zionts, S. (1997). Use of data envelopment analysis in assessing information technology impact on firm performance. Annals of Operations Research, 73, 191–213.
Wei, L., Li, G., Li, J., & Zhu, X. (2019). Bank risk aggregation with forward-looking textual risk disclosures. The North American Journal of Economics and Finance, 50, 101016.
Xu, Q., Fernando, G. D., & Tam, K. (2018). Executive age and the readability of financial reports. Advances in Accounting, 43, 70–81.
Zhu, W., Zhang, Q., & Wang, H. (2019). Fixed costs and shared resources allocation in two-stage network DEA. Annals of Operations Research, 278, 177–194.
Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124.
Zhou, X., Wang, Y., Chai, J., Wang, L., Wang, S., & Lev, B. (2019). Sustainable supply chain evaluation: A dynamic double frontier network DEA model with interval type-2 fuzzy data. Information Sciences, 504, 394–421.
The authors would like to thank the Ministry of Science and Technology, Taiwan for financially support this research project under Contracts No. 108-2410-H-034 -056-MY2 and No.110-2410-H-034 -009.
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Hsu, MF., Hsin, YS. & Shiue, FJ. Business analytics for corporate risk management and performance improvement. Ann Oper Res (2021). https://doi.org/10.1007/s10479-021-04259-x
- Risk management
- Text mining
- Data envelopment analysis