Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Application of Artificial Intelligence in Predicting Financial Dissatisfaction of Managers in Financially Distressed Companies of the Tehran Stock Exchange

Document Type : Research Paper

Authors
Davood Yousefi Jenaghard 1
Keyhan Azadi Hir 2
Mahdi Homayounfar 3
1 PhD Candidate, Department of Acounting, Rasht Branch, Islamic Azad University, Rasht, Iran
2 Assistant Professor, Department of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran
3 Associate Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
Abstract
Financial dissatisfaction is a critical factor influencing corporate success or failure. This study introduces two artificial intelligence (AI)-based models to predict factors contributing to financial dissatisfaction among managers of financially distressed firms. In the first phase, relevant data were gathered through a literature review. In the second phase, additional data were collected via questionnaires and interviews (Delphi method) with senior managers to ensure theoretical saturation. The Altman Z-score was then used to identify financially distressed companies. The study’s statistical population included 166 senior managers from 50 financially distressed firms listed on the Tehran Stock Exchange. Data were collected through a structured questionnaire covering seven dimensions and 54 indicators. Two AI models—Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)—were applied to analyze the predictive power of financial dissatisfaction factors. The first model employed a multilayer perceptron (MLP) neural network with a hyperbolic tangent activation function, while the second used an ANFIS framework with a five-layer Sugeno-type neural network containing 143 neurons in the hidden layer and trained using the Levenberg-Marquardt algorithm. Comparative analysis revealed that both models effectively identified key financial dissatisfaction factors, but the ANN model outperformed ANFIS, achieving a mean correlation coefficient (R²) of 0.98 (training) and 0.96 (validation), with a lower mean squared error (MSE) of 0.002120 and 0.003953, respectively. These findings highlight ANN’s superior predictive accuracy, making it a valuable tool for assessing financial distress among executives.
Keywords
Dissatisfaction
Financial Distress
Artificial Intelligence
Tehran Stock Exchange
Subjects
Abbasi, S., & Choukolaei, H. A., (2023). A systematic review of green supply chain network design literature focusing on carbon policy. Decision Analytics Journal, 100189.
Alexandropoulos, S.-A. N., Aridas, C. K., Kotsiantis, S. B., & Vrahatis, M. N. (2019). A Deep Dense Neural Network for Bankruptcy Prediction. Paper presented at the International Conference on Engineering Applications of Neural Networks.
Al-Hadi, A., Chatterjee, B., Yaftian, A., Taylor, G., Hasan, M., 2016. Corporate social responsibility performance, financial distress and firm life cycle: evidence from Australia. Account. Finance, 59 (2), 961–989.
Aliahmadi, A., Nozari, H., Ghahremani-Nahr, J., & Szmelter-Jarosz, A. (2022). Evaluation of key impression of resilient supply chain based on artificial intelligence of things (AIoT). arXiv preprint arXiv:2207.13174.
Altman, E. I., Iwanicz -Drozdowska, M., Laitinen, E. K., & Suvas, A. (2015). Financial and non financial variables as long -horizon predictors of bankruptcy. Available at SSRN 2669668.Ashraf, S., Félix, E. G. S., & Serrasqueiro, Z. (2019). Do traditional financial distress prediction models predict the early warning signs of financial distress? Journal of Risk and Financial Management, 12(2), 55-69.
Botshekan, M., Salimi, M. & Falahatgar Mottahedjoo, S. (2018). Developing a hybrid approach for financial distress prediction of listed companies in Tehran stock exchange. Financial Research Journal, 20(2), 173-192.
Broby, D. (2022). The use of predictive analytics in finance. The Journal of Finance and Data Science, 8, 145-161.
Bruynseels, L., & Willekens, M. (2012). The effect of strategic and operating turnaround initiatives on audit reporting for distressed companies. Accounting, Organizations and Society, 37(4), 223–241.
Buachoom, W., Kasemsan, M.L.K. (2011). Business Failure Prediction by Using the Hybrid Technique of GA and ANFIS Based on Financial Ratio: Evident from Listed Companies in the Stock Exchange of Thailand, Journal of Financial Studies and Research.
Daneshvar, A., Homayounfar, M., & Farahmandnejad, A. (2020). Developing an Intelligent Multi Criteria Clustering Method Based on PROMETHEE. Journal of Industrial Management Perspective, 9(4), 41-61.
Ebrahimkhani, H., Kazemi, M., Alireza, P., Mohammad Fakur Saghieh, A.M. (2019), Forecasting the growth of small and medium-sized companies by combining artificial neural networks and meta-heuristic algorithms, 60, 205-220.
Fallah, M., & Nozari, H. (2021). Quantitative analysis of cyber risks in IoT-based supply chain (FMCG industries). Journal of Decisions and Operations Research, 5(4), 510-521.
Gameel, M., & El-Geziry, K. (2016). Predicting financial distress: multi scenarios odeling using neural network. International Journal of Economics and Finance,11(8), 159-168.
Ghane, F., Gilaninia, S. & Homayounfar, M. (2016). The effect of cloud computing on effectiveness of customer relation management in electronic banking industry: a case study of eghtesad novin bank. Arabian Journal of Business and Management Review (Kuwait Chapter), 5(8), 50-61.
Gholamizadeh, K., Zarei, E., Yazdi, M., Rodrigues, M. A., Shirmohammadi-Khorram, N., & Mohammadfam, I. (2023). An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidents. Decision Analytics Journal, 9, 100357.
Heidari, M., Mansourfar, G. and Ghasemzade, M. (2018). Determinants of Capital Structure and Moderating Role of Financial Distress; Structural Equations Modeling (SEM) Approach. Financial Accounting Research, 10(2), 23-44.
Homayounfar, M., Baghersalimi, S., Nahavandi, B. & Izadi Sheyjani, K. (2018). Agent-based Simulation of National Oil Products Distribution Company’s Supply Network in the Framework of a Complex Adaptive System in Order to Achieve an Optimal Inventory Level. Industrial Management Journal, 10(4), 607-630.
Inam, F., Inam, A., Mian, M. A., Sheikh, A. A. & Awan, H. M. (2019), Forecasting -Bankruptcy for organizational sustainability in Pakistan: Using artificial neural networks, logit regression, and discriminant analysis. Journal of Economic and Administrative Sciences, 35 (3), 183-201.
Jansen, J. L., Bruggeman, R., Kiers, H. A. L., Pijnenborg, G. H. M., Castelein, S., Veling, W., Visser, E., Krabbendam, L., & Koerts, J. (2024). Financial dissatisfaction in people with psychotic disorders: A short report on its prevalence and correlates in a large naturalistic psychosis cohort. Journal of Psychiatric Research, 170, 302–306.
Khajavi, S. and Amiri, F. S. (2012). Recognition of Efficient Factors Affecting in companies’ bankruptcy using TOPSIS_AHP. Empirical Studies in Financial Accounting, 10(38), 69-90.
Kheradyar, S., Gholizadeh, M.H., & Lotfi, F. (2018), Predicting financial distress using the combined ANFIS-PCA method and the meta-heuristic pigeon swarm optimization algorithm. Quarterly Journal of Financial Engineering and Securities Management, 37, 133-157.
Matsumaru, M., Kaneko, S., Katagiri, H., & Kawanaka, T. (2019). Bankruptcy Prediction for Japanese Corporations using Support Vector Machine, Artificial Neural Network, and Multivariate Discriminant Analysis. International Journal of Industrial Engineering, 1(1), 79-97.
Mselmi, N. Lahiani, A. & Hamza, T. (2017). Financial distress prediction: the case of French small and medium-sized firms, Int. Rev. Financ. Anal, 50, 67–80
Nozari, H., Bakhshi-Movahed, A., & Bakhshi-Movahed, A. (2024). Smart processes in the data-driven supply chain. International Scientific Hub, 18-35.
Raeeszadeh, S.F., Gilaninia, S., & Homayounfar, M. (2016). The effects of knowledge management components on marketing performance: a case study of educational centers located across Guilan province. Arabian Journal of Business and Management Review (Kuwait Chapter), 5 (9), 23-31.
Salahi, F., Daneshvar, A., Homayounfar, M. & Pourghader Chobar, A. (2023). Presenting an integrated model for production planning and preventive maintenance scheduling considering uncertainty of parameters and disruption of facilities. Journal of Industrial Management Perspective, 13 (1), 105-140.
Soufi, M., Homayounfar, M., & Fadaei, M. (2020). Developing an optimal method for financial distress prediction of the firms (Case study: Tehran Stock Exchange). Journal of Investment Knowledge, 9(35), 85-100.
Ugural, M., & Aksoy. H. (2020). Prediction of Corporate financial distress in an emerging market: the case of Turkey. Cross Cultural Management: An International Journal, 25, 45-73.
Vaghfi, S. H. and Darabi, R. (2019). Validation of Artificial Intelligence Algorithms in Predicting Financial Distress in the Industrial and Mining Sector with Emphasis on the Role of Macroeconomic, Financial, Managerial and Risk. Iranian Journal of Trade Studies, 23(91), 213-243.
Vaghfi, S. H., mamsalhi, P., fayaz, A. & khajezade, S. (2019). Management Approach in Analysis of Financial Distress in the Industrial and Mine Industries of Iran by Using Machine Learning Methods (NSGA-II, ABC). Commercial Surveys, 17(96), 38-55.
Waqas, Hamid, and Rohani Md-Rus. 2018. Predicting Financial Distress: Importance of Accounting and Firm-Specific Market Variables for Pakistan’s Listed Firms. Cogent Economics & Finance, 6, 1545739.

  • Receive Date 19 August 2024
  • Revise Date 23 November 2024
  • Accept Date 24 December 2024