• Tidak ada hasil yang ditemukan

Agus Afiantara

N/A
N/A
Protected

Academic year: 2023

Membagikan "Agus Afiantara"

Copied!
4
0
0

Teks penuh

(1)

Modelling Banking Stability Index Page 111 of 144 Using Machine Learning Technique

Agus Afiantara REFERENCES

Adiningsih, S. (2007) ‘Indonesia: Ten years after the economic crisis’, IDS Bulletin, 38(4), pp. 45–58. doi: 10.1111/j.1759-5436.2007.tb00396.x.

Alfaro, R. and Drehmann, M. (2009) ‘Macro stress tests and crises: what can we learn?’, BIS Quarterly Review, (December), pp. 29–42.

Arzamasov, V. and Penikas, H. (2014) ‘Modeling Integral Financial Stability Index: A Cross- Country Study’, Ssrn. doi: 10.2139/ssrn.2530520.

Asfari, D. D. (2015) ‘Analisis Financial Stress Indicator Sebagai Alat Ukur’, BINA Ekonomi, 19(1), pp. 15–25.

Aspachs, O. et al. (2006) ‘Searching for a Metric for Financial Stability’, Lse Financial Markets Group Special Paper Series, 167, pp. 1–38.

Aydin, A. D. and Cavdar, S. C. (2015) ‘Prediction of Financial Crisis with Artificial Neural Network: An Empirical Analysis on Turkey’, International Journal of Financial Research, 6(4), pp. 36–45. doi: 10.5430/ijfr.v6n4p36.

Belinda, A., Wessiani, N. A. and Santosa, B. (2010) ‘Pengembangan model pendeteksian krisis keuangan di indonesia dengan pendekatan’.

Bloomer, C. and Rehm, G. (2014) ‘Using principal component analysis to find correlations and patterns at Diamond Light Source’, Proc. of IPAC, pp. 3719–3721. doi:

10.18429/JACoW-IPAC2014-THPME188.

Bowles, M. (2015) Machine Lerning in Python, Essential Techniques For Predictive Analysis. John Wiley & Sons, Inc.

Breiman, L. (1994) ‘Bagging Predictors’, (421).

Breiman, L. (1999) ‘No Title’, pp. 1–29.

Bunn, P. (2005) ‘Stress testing as a tool for assessing systemic risks’, Review Literature And Arts Of The Americas, (165), pp. 116–126.

Cassioli, A. et al. (2012) ‘Machine Learning for Global Optimization’, Computational Optimization and Applications, 51, pp. 279–303. doi: 10.1007/s10589-010-9330-x.

Devi, S. S. and Radhika, Y. (2018) ‘A Survey on Machine Learning and Statistical Techniques in Bankruptcy Prediction’, International Journal of Machine Learning and Computing, 8(2), pp. 133–139. doi: 10.18178/ijmlc.2018.8.2.676.

Diaconu, R.-I. and Oanea, D.-C. (2014) ‘The Main Determinants of Bank’s Stability.

Evidence from Romanian Banking Sector’, Procedia Economics and Finance. Elsevier B.V., 16(May), pp. 329–335. doi: 10.1016/S2212-5671(14)00810-7.

Diamond, D. W. (1997) ‘Liquidity , Banks , and Markets’, 105(5), pp. 928–956.

(2)

Modelling Banking Stability Index Page 112 of 144 Using Machine Learning Technique

Agus Afiantara Dr. Tarsidin, SE, M. et al. (2011) ‘Laporan Akhir Penyusunan Alat Analisis Risiko Sistemik Dan Stabilitas Sistem Perbankan’.

End, J. W. van den (2006) ‘DNB Working Paper’, DNB Working Paper, 276(50), pp. 289–

295. doi: 10.1007/s10693-014-0207-5.

Fatimah Zahra, S. and Huda, N. (2018) ‘Stability Measurement of Dual Banking System in Indonesia: Markov Switching Approach’, Journal of Islamic Economics)Journal of Islamic Economics). Jurnal Ilmu Ekonomi SyariahJournal of Islamic Economics, 10(101), pp. 25–53.

doi: 10.15408/aiq.v10i1.5867.

Faure, A. P. (2013) Money Market: An Introduction This.

Frankel, J. and Saravelos, G. (2012) ‘Can leading indicators assess country vulnerability?

Evidence from the 2008-09 global financial crisis’, Journal of International Economics, 87(2), pp. 216–231. doi: 10.1016/j.jinteco.2011.12.009.

Gadanecz, B. and Jayaram, K. (2009) ‘Measures of financial stability – a review’, Irving Fisher Committee (IFC) - Bank for International Settlements (BIS), (31), pp. 365–380. doi:

10.1086/663992.

Gallucci, C. and Modina, M. (2016) ‘Bank-firm relationship in default prediction models . An analysis on a sample of Italian firms .’, pp. 1–21.

Ghosh, S. (2011) ‘A simple index of banking fragility: application to Indian data’, Journal of Risk Finance, 12(2), pp. 112–120. doi: 10.1108/15265941111112839.

Hadad, M. D. et al. (2007) ‘Macroeconomic Stress Testing for Indonesian Banking System’, Framework, pp. 1–57.

Howley, T. et al. (2006) ‘The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data’, Knowledge-Based Systems, 19(5), pp. 363–

370. doi: 10.1016/j.knosys.2005.11.014.

Illing, M. and Liu, Y. (2003) ‘Measuring Financial Stress’.

Jokipii, T. and Monnin, P. (2013) ‘The impact of banking sector stability on the real economy’, Journal of International Money and Finance, 32(1), pp. 1–16. doi:

10.1016/j.jimonfin.2012.02.008.

Kaminsky, G L, Lizondo, S., & and Reinhart, C. M. (1998) ‘Leading Indicators of Currency Crises.’, IMF Staff Papers, 45(November), p. 45. doi: 10.1016/S1566-0141(02)00002-X.

Karanovic, G. and Karanovic, B. (2015) ‘Developing an Aggregate Index for Measuring Financial Stability in the Balkans’, Procedia Economics and Finance, 33(15), pp. 3–17. doi:

10.1016/S2212-5671(15)01690-1.

Kirschenmann, K., Malinen, T. and Nyberg, H. (2014) ‘The Risk of Financial Crises: Does it Involve Real or Financial Factors?’, Ssrn, 17(Arkadiankatu 7), pp. 1–40. doi:

(3)

Modelling Banking Stability Index Page 113 of 144 Using Machine Learning Technique

Agus Afiantara 10.2139/ssrn.2520434.

Kočišová, K. (2016) ‘Banking Stability Index : A Cross-Country Study Banking Stability Index : A Cross-Country Study’, https://www.researchgate.net/publication/293823055 Banking, (October 2015).

Korjus, K. (2010) ‘Principal component analysis 1’, pp. 1–8.

Kristína Kočišová, D. S. (2015) ‘Banking Stability Index: New EU countries after Ten Years of Membership’, Working Papers in Interdisciplinary Economics and Business Research, (December), pp. 1–26.

Kuzmenko, O. (2016) ‘Modeling the stability dynamics of Ukrainian banking system’, (74203).

Larionova, N. and Varlamova, J. (2014) ‘Correlation Analysis of Macroeconomic and Banking System Indicators’, Procedia Economics and Finance. Elsevier B.V., 14(14), pp.

359–366. doi: 10.1016/S2212-5671(14)00724-2.

Lazreg, M. Ben and Granmo, O.-C. (2016) ‘Deep Learning for Social Media Analysis in Crises Situations’, (June), pp. 1–6.

Lin, C. (2013) ‘Optimization and Machine Learning’.

Lin, F., Liang, D. and Chu, W. S. (2010) ‘The role of non-financial features related to corporate governance in business crisis prediction’, Journal of Marine Science and Technology, 18(4), pp. 504–513.

McDonald, C. (2017) ‘Demystifying AI, Machine Learning, and Deep Learning’. Available at: https://dzone.com/articles/demystifying-ai-machine-learning-and-deep-learning.

Mirkin, B. (2011) ‘Principal Component Analysis and SVD’, Core Concepts in Data Analysis: Summarization, Correlation and Visualization, pp. 173–219. Available at:

http://dx.doi.org/10.1007/978-0-85729-287-2_5.

Muliaman D. Hadad, Sugiharso Safuan, Wimboh Santoso, Dwityapoetra S. Besar, I. R.

(2006) ‘Macroeconomic Model to measure the Financial Stability Index: Indonesian Case Study’.

Ncr, P. C. et al. (2000) ‘Crisp-dm 1.0’.

Nyman, R. and Ormerod, P. (2016) ‘1701.01428’, pp. 1–14.

Petropoulos, A. et al. (2017) ‘Predicting bank insolvencies using machine learning techniques’, (April 2017), pp. 1–42.

Popovska, J. (2014) ‘Modelling Financial Stability: The Case of the Banking Sector in Macedonia’, Journal of Applied Economics and Business, 2(1), pp. 68–91. Available at:

http://www.aebjournal.org/articles/0201/020104.pdf.

Quinlan, J. R. (2007) ‘Induction of Decision Trees’, pp. 81–106.

(4)

Modelling Banking Stability Index Page 114 of 144 Using Machine Learning Technique

Agus Afiantara Ristolainen, K. (2018) ‘Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity’, Scandinavian Journal of Economics, 120(1), pp. 31–62.

doi: 10.1111/sjoe.12216.

Ronnqvist, S. and Sarlin, P. (2015) ‘Detect & describe: Deep learning of bank stress in the news’, Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, pp. 890–897. doi: 10.1109/SSCI.2015.131.

Setianingrum Meganisa, Sugiyanto, Z. E. (2017) ‘Pendeteksian dini krisis keuangan di indonesia menggunakan gabungan model volatilitas dan markov switching berdasarkan indikator output riil, kredit domestik per pdb, dan ihsg’, pp. 1–8.

Simons, D. and Rolwes, F. (2008) ‘Macroeconomic default modelling and stress testing’, pp.

1–31.

Sumandi (2017) ‘ANALISIS SISTEM DETEKSI DINI TERHADAP KRISIS PERBANKAN SYARIAH ANALYSIS AN EARLY WARNING SYSTEM ON SHARIA BANKING CRISIS’, Jurnal Nisbah, 3, pp. 365–381.

Swamy, V. (2013) ‘Banking System Resilience and Financial Stability’, Munich Personal RePEc Archive, (39922), pp. 0–29. Available at: http://mpra.ub.uni-muenchen.de/47512/.

Tambunan, T. T. H. (2010) ‘The Indonesian Experience with Two Big Economic Crises’, Modern Economy, 01(03), pp. 156–167. doi: 10.4236/me.2010.13018.

Watson, H. J. et al. (2000) ‘JOURNAL OF DATA WAREHOUSING Volume 5 Number 4 Fall 2000’, Journal, 5(4). doi: 10.1016/0022-4073(81)90019-4.

Wimanda, R. E., Maryaningsih, N. and Nurliana, L. (2014) ‘EVALUASI TRANSMISI BAURAN KEBIJAKAN BANK INDONESIA’.

Wirth, R. and Hipp, J. (2000) ‘CRISP-DM : Towards a Standard Process Model for Data Mining’, Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, (24959), pp. 29–39.

Wuryandani, G. et al. (no date) ‘pengelolaan dana dan likuiditas bank 1’.

Referensi

Dokumen terkait

"The impact of corporate governance attributes on intellectual capital disclosure: A longitudinal investigation of Nigerian banking sector", Journal of Banking Regulation,