SIMPULAN DAN SARAN
6.1. Simpulan
Berdasarkan hasil analisis, pengujian terhadap validitas model,
interpretasi hasil penelitian, dan pengujian hipotesis, maka dapat disimpulkan bahwa :
6.1.1. Distress keuangan perusahaan publik sektor non keuangan Indonesia dapat diprediksi dengan menggunakan rasio-rasio keuangan berdasarkan analisis diskriminan dan analisis logit dengan model sebagai berikut :
Analisis Diskriminan : Zi = 0.53 + 0.42X4 - 1.36X5 + 3.38X6 + 1.38X7 + 3.21X9 - 0.19X11 - 1.50X15 Analisis Logit : Pi Ln(---) = -1.86 +0.67X4 +11.55X6 +3.35X8+20.99X9 -1.23X10 - 0.59X13 1 - Pi
6.1.2. Beberapa rasio keuangan dapat digunakan sebagai variabel prediktor untuk memprediksi distress keuangan perusahaan publik sektor non keuangan Indonesia yaitu :
1. Model Diskriminan :
X4 = Aktiva Cepat Cair/Kewajiban Lancar
X5 = Modal Kerja/Total Aktiva
X6 = Laba ditahan/Total Aktiva
X7 = Laba sebelum bunga dan Pajak/Total Aktiva
X9 = Laba Bersih/Total Aktiva
X11 = Total Ekuitas/Total Kewajiban
X15 = Total Kewajiban/Total Aktiva
2. Model Logit :
X4 = Aktiva Cepat Cair/Kewajiban Lancar
X6 = Laba ditahan/Total Aktiva
X8 = Penjualan Bersih/Total Aktiva
X9 = Laba Bersih/Total Aktiva
X10 = Laba Bersih/Total Ekuitas
X13 = Kewajiban Jangka Panjang/Total Ekuitas
6.1.3. Akurasi model analisis diskriminan dan analisis logit cukup tinggi dan layak sebagai model prediksi yang ditunjukkan oleh prosentase akurasi prediksi dan nilai Press’s Q sebagai berikut :
Tabel VI.1.
Tingkat Akurasi Prediksi dan Nilai Press’s Q
Model Diskriminan Model Logit Sampel Analisis : Kebenaran Klasifikasi 82.6% 86.2% Kesalahan Klasifikasi 17.4% 13.8% Nilai Press’s Q 46.25 57.26 Sampel Validasi : Kebenaran Klasifikasi 77.1% 81.7% Kesalahan Klasifikasi 22.9% 18.3% Nilai Press’s Q 31.94 43.68 6.2. Saran
Berdasarkan hasil interpretasi dan keterbatasan penelitian, maka saran yang dapat dikemukakan adalah :
6.2.1. Penggunaan model analisis diskriminan dan analisis logit untuk
memprediksi distress keuangan perusahaan publik sektor non keuangan Indonesia haruslah digunakan sebagai peringatan dini (early warning system) dan sebagai indikasi, bukan sebagai kepastian mutlak.
6.2.2. Untuk penelitian lebih lanjut diperlukan penentuan definisi dan kriteria distress keuangan yang lebih tajam, seperti bangkrut secara formal, untuk menggolongkan perusahaan yang distress dan non distress keuangan.
DAFTAR KEPUSTAKAAN
Agung Nur Fajar. 1997. Aplikasi Motode Logit untuk Memprediksi
Keberhasilan/ Kegagalan Pemberian Kredit pada Industri Tekstil. Grup Konsultan ACG, Management, Consultant, and Research Service. Jakarta. Paper pada Lokakarya Profil Industri (Hotel Le Meridien, Jakarta : 23 April 1997).
Åstebro, T. and J.K. Winter. 2001. More than a Dummy: The Probability of Failure, Survival and Acquisition of Firms in Financial Distress. Paper presented at the EFA Annual Meeting, (London : Augst, 2000).
_______. Badan Penyehatan Perbankan Nasional. Rencana Strategis 1999 – 2004.
Back, B., K. Sere, and M.C. van Wezel. 1996. Choosing the Best Set of Bankruptcy Predictors. Turku Centre for Computer Science. Finland. Back, B., T. Laitinen, K. Sere, and M. van Wezel. 1996. Choosing Bankruptcy
Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms. Turku Centre for Computer Science. Finland. TUCS Technical Report. (No 40 : September 1996.)
Back, B., T. Laitinen, J. Hekanaho, and K. Sere. 1997. The Effect of Sample Size on Different Failure Prediction Methods. Turku Centre for Computer Science. Finland. TUCS Technical Report. (No 155 : December 1997).
Brigham, E.F. and L.C.Gapenski.1996. Intermediate Financial Management. Fifth Edition.Orlando. Ther Dryden Press. (Ch. 26. pp. 891- 926). Cooper, D.R. and C.W. Emory.1995. Metode Penelitian Bisnis. Jilid 2.
Terjemahan. Edisi Kelima, 1999. Jakarta. Penerbit Erlangga. Jakarta.
Doumpos, M. and C. Zopounidis. 1999. A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece.
Multinational Finance Journal. (Vol. 3, no. 2, 1999, pp. 71–101). Fanning, K.M. and K.O. Cogger. 1994. A Comparative Analysis of Artificial
Neural Networks Using Financial Distress Prediction.International Journal of Intelligent Systems in Accounting, Finance, and Management. (Vol. 3 : pp. 241-252).
Foster, G. 1986. Financial Statement Analysis.. Second Edition. New Jersey. Prentice-Hall Inc. (Ch. 15 : pp. 533 – 572).
Gaspersz, Vincent. 1991. Ekonometrika Terapan. Jilid Satu. Edisi pertama. Bandung. Penerbit Tarsito.
Hekanaho, J., B. Back, K. Sere, and T. Laitinen. 1998. Analysing Bankruptcy Data with Multiple Methods. Department of Accounting and Finance. Vaasa. Finland. American Association for Artificial Intelligence.
Hornby, A.S. 1987. Oxford Advanced Learner’s Dictionary of Current English. 25th impression. New York. Oxford University Press.
Kahya, E. and P. Theodossiou. 1996. Predicting Corporate Financial Distress : A Time-Series CUSUM Methodology. Paper was presented at 3rd Annual Multinational Finance Conference. (June 1996). Review of Quantitative Finance and Accounting (Vol. 13:4, December, 1999) pp. 323-345. Kaiser, U. 2001. Moving in and out of Financial Distress: Evidence for Newly
Founded Service Sector Firms. Centre for European Economic Research, February 2001
Kolari, J., D. Glennon, H. Shin, and M. Caputo. 2000. Predicting Large U.S. Commercial Bank Failures. Economic and Policy Analysis Working Paper 2000-1.
Maholtra, N.K. 1993. Marketing Research : An Applied Orientation. Prentice- Hall Inc. Englewood Cliffs. New Jersey 07632. (Ch. 20 : pp. 589 – 616).
Spanos M., G. Dounias, N. Matsatsinis, and C. Zopounidis. 1999. A Fuzzy Knowledge-Based Decision Aiding Method for the Assessment of Financial Risks: The Case of Corporate Bankruptcy Prediction.Technical University of Crete. Dept. of Production Engineering and Management Financial Engineering Laboratory.
Mudrajad Kuncoro. 2001. Metode Kuantitatif. Edisi Pertama. Jogjakarta. UPP AMP YKPN.
Persons, O. S. 1999. Using Financial Information to Differentiate Failed vs. Surviving Finance Companies in Thailand: An Implication for Emerging Economies. Multinational Finance Journal. (Vol. 3, no. 2, 1999 : pp. 127–145).
Ross, S.A., R.W. Westerfield, and J. Jaffe. 1996. Corporate Finance. Fourth Edition.New Jersey. Irwin. The McGraw-Hill Companies, Inc. (Ch. 30 : pp. 808-823).
Shirata, C. Y. 1998. Financial Ratios as Predictors of Bankruptcy in Japan : An Empirical Research. Tsukuba College of Technology Japan. APIRA98 : The Zengin Foundation for Studies on Economics and Finance.
Shumway, T. 2001. Forecasting Bankruptcy More Accurately : A Simple Hazard Model. Jornal of Business. (Vol. 74). pp. 101-124.
Silalahi, Jasman. 1999. Kelayakan Analisis Diskriminan Untuk Memprediksi Kebangkrutan Bank-bank Swasta Nasional Di Indonesia. Tesis tidak diterbitkan. Magister Manajemen. Universitas Airlangga. Surabaya.
Steve Widjaja. 2001. Analisis Model Kebangkrutan pada Perusahaan Kontraktor di Semarang. Tesis tidak diterbitkan. Magister Manajemen, Universitas Airlangga. Surabaya.
Tirapat, S. and A. Nittayagasetwat.1999. An Investigation of Thai Listed Firms’ Financial Distress Using Macro and Micro Variables. Multinational Finance Journal. (Vol. 3, no. 2, 1999, pp. 103–125).
Tyree, E. W. and J. A. Long . 1996. Bankruptcy prediction models: probabilistic neural networks versus discriminant analysis and backpropagation neural networks, Dept. of Business Computing School of Informatics, City University, Northampton Square, London EC1V OHB, United Kingdom
Weston, J.F. and T.E. Copeland. 1992. Managerial Finance. Ninth edition. Orlando. Florida. The Dryden Press.. (Ch. 28 : pp. 1143-1182).