The Accuracy of Financial Distress Prediction Models: Empirical Study on the World’s 25 Biggest Tech Companies in 2015–2016 Forbes’s Version
Every company has the possibility to go bankrupt. Bankruptcy begins with a condition of financial distress. Reliable and accurate models prediction are needed as the early warning system to anticipate financial distress. This research aims to find the predictor model of financial distress that are the most accurate in predicting the condition of financial distress at technology companies. The population of this research is the technology companies that were listed on the World’s 25 Biggest Tech Companies in 2015–2016 Forbes’s version. The total sample of this research was 30 tech company. The data in this research is totaled to 60 annual reports. Determination of the accuracy level was based on the calculation of the correct number of prediction divided by the total data and multiplied by 100%. This study compares seven predictors model of financial distress. The result indicate that if Grover is the most accurate model in predicting financial condition after the year predict. Grover model has an accuracy rate of 96.6%.
Keywords: financial distress, bankruptcy, model prediction, accuracy
 Agarwal, V. and Taffler, R. (2007). Twenty−five years of the Taffler z−score model: Does it Really have Predictive Ability? Accounting and Business Research, vol. 37, no. 4, pp. 285–300.
 Altman, E. and Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy. New Jersey: John Wiley & Sons.
 Arasu, B., Kumar, P., and Tamizhselvi. (2013). Applicability of Fulmer and Springate models for predicting financial distress of firms in the finance sector – An empirical analysis. ELK Asia Pacific Journals, vol. 4, no. 1, pp. 1–9.
 Ardiani, P. (2016). Financial Distress. Retrieved March 3, 2018 from Drs. J Tanzil & Associates.
 Claessens, S., Djankov, S., and Klapper, L. (1999). Resolution of corporate distress in East Asia (World Bank Policy Research Working Paper), vol. 2133, pp. 1–33.
 Gamayuni, R. (2009). Berbagai Alternatif Model Prediksi Kebangkrutan. Jurnal Akuntansi dan Keuangan, vol. 14, no. 1, pp. 75–89.
 Gelman, A. (2017, November 29). What’s the point of a robustness check? Retrieved from: Statistical Modeling, Causal Inference, and Social Science: https://andrewgelman.com/2017/11/29/whats-point-robustness-check/ (accessed on September 24, 2018).
 Gunawan, B., Pamungkas, R., and Susilawati, D. (2017). Perbandingan Prediksi Financial Distress dengan Model Altman, Grover dan Zmijewski. Jurnal Akuntansi dan Investasi, vol. 18, no. 1, pp. 119–127.
 Imanzadeh, P., Maran-Jouri, M., and Sepehri, P. (2011). A study of the application of Springate and Zmijewski bankruptcy prediction models in firms accepted in Tehran Stock Exchange. Australian Journal of Basic and Applied Sciences, vol. 5, no. 11, pp. 1546–1550.
 Platt, H. and Platt, M. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, vol. 26, no. 2, pp. 1844–199.
 Rado, M. (2013). Testing and Calibrating the Alltman Z-score for the U.K. Rotterdam: Department of Business Economics Erasmus University.
 Sallomi, P. (2018). 2018 Technology Industry Outlook, pp. 1–6. Deloitte Center.
 Vestari, M. and Farida, D. (2013). Analisis Rasio-Rasio Ukuran Keuangan, Prediksi Financial Distress, dan Reaksi Investor. AKRUAL, vol. 5, no. 1, pp. 26–44.