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"Masa kepemimpinan SBY memiliki pendekatan ekonomi jangka pendek sementara Jokowi memiliki target perkembangan ekonomi jangka panjang agar terbangun lingkungan yang mendukung kegiatan bisnis di Indonesia. Di masa Jokowi, perusahaan konstruksi mengalami peningkatan kinerja operasi yang pesat, tetapi hal ini dibarengi dengan kenaikan tingkat utang perusahaan konstruksi. Dengan menggunakan 50 perusahaan sebagai observasi, dilakukan analisis prediksi kebangkrutan dengan model Altman Z-Score, Altman Z-Score EMS, dan Springate S-Score untuk mengetahui kondisi keuangan perusahaan konstruksi di Indonesia dan bagaimana pengaruh perbedaan periode kepemimpinan presiden terhadap tiga model tersebut.

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Susilo Bambang Yudhoyonos presidency period has target for short-term economic development while Jokowi has a target of long-term economic development in order to build an environment that supports business activities in Indonesia. During the era of Jokowis presidency, construction companies are developing their business activities more than during SBYs era, but the development is coma along with the increase in companys debt performance. By using 50 companies as observations, a prediction bankruptcy analysis is conducted using Altman Z-Score, Altman Z-Score EMS, and Springate S-Score  model to obtain financial information on construction companies in Indonesia and to analyze the relationship of the different presidency period to the three models."

"Penelitian ini dilakukan untuk melihat seberapa cocok model-model prediksi kebangkrutan yang ada dan selama ini digunakan di negara-negara lain untuk digunakan pada masa krisis di Indonesia. Adapun data yang digunakan dalam penelitian adalah perusahaan-perusahaan di Indonesia yang terdaftar pada Bursa Efek Indonesia (BEI) yang terkelompokkan ke dalam beberapa sektor. Re-estimasi koefisien variabel-variabel model dilakukan kepada masing-masing sektor untuk kemudian dilakukan prediksi kebangkrutan dari model re-estimasi tersebut. Hasil prediksi kebangkrutan model re-estimasi kemudian dibandingkan dengan hasil prediksi kebangkrutan dari model aslinya untuk melihat apakah model dapat digunakan pada masa krisis di Indonesia. Hasil dari penelitian adalah model Springate original merupakan model yang paling cocok dengan kondisi di Indonesia pada masa krisis akibat COVID-19. Model Springate memiliki akurasi prediksi kebangkrutan paling tinggi, sementara model Altman Emerging Market menghasilkan Error Type I paling tinggi.

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This research was conducted to see how suitable the existing bankruptcy prediction models that have been used in other countries to be used during the crisis in Indonesia. The data used in research are companies in Indonesia registered in the Indonesia Stock Exchange (IDX) which are grouped into several sectors. The re-estimate of the coefficient of variables models is carried out to each sector for then the bankruptcy prediction of the re-estimation model is carried out. The results of the bankruptcy prediction of the re-estimate model are then compared with the results of the bankruptcy prediction of the original model to see whether the model can be used during the crisis in Indonesia. The result of the study is the original Springne Model is the model that is most suitable for the conditions in Indonesia during the crisis due to Covid-19. The Springate model has the highest accuracy of bankruptcy predictions, while the Altman Emerging Market model produces the highest error type I."

"ABSTRAKKarya akhir ini membahas mengenai 4 model prediksi kebangkrutan yang popular saat ini, dua model berdasarkan data akuntansi yaitu Altman Z scores (1968) dan Ohlson O scores (1980) dan dua model berdasarkan data pasar yaitu Merton model (1974) dan KMV model (1995). Penulis melakukan penelitian terhadap 4 model prediksi kebangkrutan pada 23 perusahaan bangkrut dan 40 perusahaan tidak bangkrut di Indonesia pada kurun waktu 2001-2011. Dari hasiltersebut kami menemukan bahwa KMV model mengungguli model-model yang lainnya dalam hal validasi model, dengan nilai akurasi tertinggi.

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ABSTRACTThis paper asses about 4 popular bankruptcy model, two was accounting based models Altman Z scores (1968) and Ohlson O scores (1980) and two was market based models Merton model (1974) and KMV model (1995). We measure this 4 bankruptcy model bya applied this model into 23 bankruptcy company and 40 non bankruptcy company in Indonesia from 2001-2011. From the result we find that KMV model has relative more explanatory power than other model, with the best accuracy ratio than others models."

"Penelitian ini bertujuan untuk menganalisis prediksi kebangkrutan pada perusahaan di sektor konstruksi yang terdaftar pada Bursa Efek Regional Asia Tenggara (Filipina, Indonesia, Malaysia, dan Thailand). Metode prediksi kebangkrutan yang digunakan adalah model KMV (Kealhofer, McQuown and Vasicek) yang dibuat oleh Moodys. Dari hasil studi menujukan bahwa tingkat probability of default perusahaan sektor konstruksi di Bursa Efek Indonesia berada di posisi paling rendah jika dibandingkan dengan perusahaan sektor konstruksi di Bursa Efek Regional Asia Tenggara.

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This research is aimed to analyze bankruptcy prediction on company who listed in Regional Stock Exchange of South East Asia (Filipina, Indonesia, Malaysia, and Thailand). KMV (Kealhofer, McQuown and Vasicek) method who published by Moodys used to predict the bancrupty. The results of this research found that level of probability of default construction sector companies in Indonesia Stock Exchange is in the lowest position when compared with the construction sector companies in Southeast Asia Regional Stock Exchange."

"Kondisi harga minyak secara global dalam beberapa tahun terakhir mengalami pasang surut akibat harga minyak mengalami penurunan yang signifikan. Hal ini tentunya berimbas pada kesehatan keuangan perusahaan-perusahaan yang bergerak di industri migas. Penelitian ini bertujuan untuk memprediksikan kemungkinan kebangkrutan perusahaan migas di Indonesia selama periode tahun 2011 – 2017. Perusahaan yang diteliti adalah perusahaan migas yang terdaftar di BEI. Adapun tujuan lainnya adalah untuk melihat apakah fenomena global berupa penurunan harga minyak dunia berpengaruh terhadap kesehatan keuangan migas yang direpresentasikan dengan nilai Z-Score serta untuk melihat indikator apa saja yang dapat mempengaruhi kebangkrutan perusahaan migas. Data penelitian menggunakan data laporan keuangan dan laporan tahunan perusahaan yang diambil dari website IDX dan website masing-masing perusahaan. Hasil penelitian menunjukkan bahwa hasil perhitungan Z-Score mampu memprediksikan lima perusahaan berada pada kategori bangkrut sejak 2014 hingga 2017. Di tahun 2011 hanya dua perusahaan terprediksi bangkrut, tahun 2012 hanya memprediksi satu perusahaan, dan tahun 2013 diprediksi tiga perusahaan mengalami kebangkrutan. Penelitian juga menemukan bahwa penurunan harga minyak mempengaruhi hasil Z-Score untuk perusahaan E&P karena pendapatan perusahaan bergantung pada harga minyak di pasar. Sedangkan pada perusahaan jasa migas, penurunan harga minyak tidak terlalu berpengaruh kecuali pada APEX dan BIPI yang lini bisnisnya merupakan jasa pengeboran.

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Global oil price condition for these past few years has been fluctuating and has reached the lowest level. This condition will affect oil and gas companies financial health. This thesis aims to predict the bankruptcy probability of oil and gas companies in Indonesia during period of 2011 – 2017. The observed companies are oil and gas companies that are listed on IDX. Another aim of this thesis is to observe whether the decline in oil price would affect company Z-Score result or not, and to observe which variable that effect the bankruptcy to oil and gas companies. This research uses financial data that obtained from IDX website and company website. The result of the research figures out that Z-Score model could predict at least five companies are in the bankruptcy category since 2014 to 2017. In 2011, only two companies that are predicted will be bankrupt. In 2012, theres only one company will be bankrupt, and in 2013 three companies are predicted to be bankrupt. This research also figures out that the decrease in oil price effects the result of Z-Score for E&P companies as E&P companies revenue relies on oil price in the market. For service companies, the decrease in oil price doesnt give direct impact except for APEX and BIPI which their line business is providing services for drilling explorations."

"Financial distress dapat dianggap sebagai peringatan dini masalah yang dapat menyebabkan kebangkrutan. Memprediksi kebangkrutan menjadi salah satu hal yang dapat dilakukan perusahaan untuk menemukan keadaan kesehatan keuangan perusahaan. Sebanyak 585 firm-year pada perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia dijadikan sampel untuk penelitian ini di mana 113 di antaranya dikategorikan dalam kondisi financial distress. Model prediksi kebangkrutan dapat diperiksa untuk menilai situasi ekonomi perusahaan untuk tujuan lebih lanjut. Altman dan Ohlson adalah beberapa peneliti terkenal yang modelnya dirujuk untuk mengevaluasi kesehatan perusahaan.

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Financial distress can be regarded as an early warning of trouble that can lead to bankruptcy. Predicting bankruptcy becomes one thing that companies can do to discover the state of the company's financial health. A total of 585 firm-years of manufacturing companies that listed in Indonesia Stock Exchange are sampled for this research where 113 of them are categorized in financial distress state. Bankruptcy prediction models may be examined to assess a company's economic situation for further purposes. Altman and Ohlson are some of notable researchers to which their models are referred to evaluating the health of companies."

"Skripsi ini membahas tentang pengaruh volatilitas arus kas terhadap prediksi resiko kebangkrutan pada perusahaan terbuka yang terdaftar di Bursa Efek Indonesia dari tahun 2010-2017 dengan total perusahaan berjumlah 154 perusahaan yang tersebar kedalam semua sektor kecuali sektor keuangan. Skripsi ini menggunakan metode kuantitatif dengan melakukan regresi GLS.Hasil penelitian membuktikan bahwa terdapat pengaruh dari volatilitas arus kas perusahaan terhadap resiko kebangkrutan perusahaan. Penelitian ini juga meneliti tentang pengaruh volatilitas arus kas terhadap financial distress dan menghasilkan bukti yang serupa bahwa volatilitas berpengaruh signifikan terhadap tingkat financial distress perusahaan.

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This thesis is discussing about the impact of cash flow volatilty on bankruptcy risk prediction on listed companies who is listed on Indonesian Stock Exchange from 2010 to 2017 with total observation of 154 companies from all of the sectors excluding financial sector. This thesis is conducted by using GLS regression.

The result shows that cash flow volatility significantly influence the bankruptcy risk. This thesis also investigating the impact of cash flow volatility to financial distress and generating same result that cash flow volatility impact the level of company financial distress."

"ABSTRAKThe economy of Indonesia has rapidly grown since its first economicturmoil in 1997/1998. The annual growth rate of the GDP exceeded 6% in the lastfour years despite the global economies slow down due to the consequences of‘bubble’ subprime mortgage that ruined most of the world’s financial institutions.The growth significantly energizes the local economic activities either in theindustrial market or in the capital market.In spite of the ‘bull’ market, the risk of financial distress remains alive andthe economic direction might change because of the volatility of businessenvironment. There is no firm protected or immune from financial adversity thatmay result in failure, insolvency, default or bankruptcy. Plummeting stock price,reduced dividend payment, consecutive net loss, massive lay-offs, pendingobligations and a fair number of other negative signs are common association withfinancial distress.Widely recognized, financial distress prediction models may be examinedto assess a firm’s economic situation for further purposes. Altman, Ohlson,Zmijewsky, Fulmer, and Springate are some of notable researchers to which theirmodels are referred to evaluating the soundness of a firm. However, each markethas its own financial distress environment that in consequence any financialdistress prediction model requires an evaluation whether or not the modeladequately fits to a certain market, in particular Indonesia for this case. Theimportance of predictors and accuracy will minimize producing misleading resultsfrom the economic forecast.The results of this testing against the first hypothesis showed that none ofthe adjusted models included all the variables of the base model, respectively.There were some variables with insufficient explanatory power to predict thecessation of activities of the tested firms. The second hypothesis argued that theadjusted models were less capable than those developed originally in terms ofaccuracy.

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ABSTRACTThe economy of Indonesia has rapidly grown since its first economicturmoil in 1997/1998. The annual growth rate of the GDP exceeded 6% in the lastfour years despite the global economies slow down due to the consequences of‘bubble’ subprime mortgage that ruined most of the world’s financial institutions.The growth significantly energizes the local economic activities either in theindustrial market or in the capital market.In spite of the ‘bull’ market, the risk of financial distress remains alive andthe economic direction might change because of the volatility of businessenvironment. There is no firm protected or immune from financial adversity thatmay result in failure, insolvency, default or bankruptcy. Plummeting stock price,reduced dividend payment, consecutive net loss, massive lay-offs, pendingobligations and a fair number of other negative signs are common association withfinancial distress.Widely recognized, financial distress prediction models may be examinedto assess a firm’s economic situation for further purposes. Altman, Ohlson,Zmijewsky, Fulmer, and Springate are some of notable researchers to which theirmodels are referred to evaluating the soundness of a firm. However, each markethas its own financial distress environment that in consequence any financialdistress prediction model requires an evaluation whether or not the modeladequately fits to a certain market, in particular Indonesia for this case. Theimportance of predictors and accuracy will minimize producing misleading resultsfrom the economic forecast.The results of this testing against the first hypothesis showed that none ofthe adjusted models included all the variables of the base model, respectively.There were some variables with insufficient explanatory power to predict thecessation of activities of the tested firms. The second hypothesis argued that theadjusted models were less capable than those developed originally in terms ofaccuracy."

"Penggunaan variabel berbasis jaringan pada model prediksi kebangkrutan perusahaan dengan metode XGBoost belum banyak ditemukan. Meskipun prediksi kebangkrutannya sudah dikaji secara luas dan beragam, namun sebagian besar masih berfokus pada penggunaan variabel finansial. Dampak sistemik kebangkrutan dapat meluas hingga mengancam stabilitas sistem keuangan. Dampak sistemik yang diwaspadai terutama yang ditimbulkan oleh konglomerasi ataupun kelompok perusahaan. Hal ini memunculkan pertanyaan apakah kebangkrutan perusahaan di dalam satu kelompok akan saling berpengaruh dan menyebabkan efek sistemik di dalam kelompoknya. Sebelum kondisi buruk diketahui oleh pasar atau publik, otoritas pengawasan tersebut diharapkan dapat mendeteksi lebih awal kondisi buruk yang akan terjadi dan melakukan langkah-langkah konkrit yang diperlukan untuk menyelamatkan perusahaan secara khusus dan sistem perekonomian secara umum.Deteksi dini ini dibangun dengan mengembangkan model prediksi yang bekerja berdasarkan data historis, mampu memprediksi kebangkrutan, dan memetakan potensi dampak sistemiknya pada serangkaian perusahaan yang berelasi. Penelitian ini menggunakan data finansial dan relasional dari perusahaan yang terdaftar pada Bursa Efek Indonesia (BEI) mulai tahun 2010 hingga 2021. Data finansial yang digunakan adalah variabel neraca, rasio solvency, rasio profitability, dan rasio operasional. Data relasional terdiri dari 3 jenis relasi berdasarkan teori ultimate ownership, yaitu pemegang saham yang terdaftar pada laporan tahunan, dewan komisaris dan dewan direksi. Setelah melalui serangkaian literatur review dan eksperimen, metode machine learning XGBoost dipilih karena kemampuannya dalam melakukan prediksi dalam data yang tidak seimbang. Model akhir yang diusulkan adalah model prediksi kebangkrutan dengan tugas klasifikasi kelas bangkrut dan tidak, dengan metode XGBoost, menggunakan integrasi data masukan berupa variabel keuangan dan non-keuangan berbasis jaringan. Model ini terdiri dari pemrosesan input variabel keuangan dan relasional, prediksi dengan XGBoost pada 7 jenis integrasi data, pemilihan hasil prediksi akhir berdasarkan AUC yang terbaik, dan analisis potensi dampak sistemik dari jaringan terpilih berdasarkan model integrasi data terbaik di tahap sebelumnya.Model prediksi kebangkrutan ini sekaligus memberikan kontribusi dalam memvisualisasikan potensi dampak sistemik yang mungkin terjadi. Pada tahap prediksi kebangkrutan digunakan model integrasi data variabel finansial – non finansial. Model dengan integrasi data yang menghasilkan AUC terbaik digunakan pada tahap analisis potensi dampak sistemik. Berdasarkan luaran dari tahap 1, analisis dampaknya dipetakan sesuai relasi yang terbentuk dari jaringan yang bersesuaian dengan model terbaiknya. Hasil pengujian dengan data tes tahun 2019 untuk memprediksi kondisi 1 tahun ke depan menunjukkan AUC sebesar 90.20% dengan model integrasi data finansial – Shareholder. Model usulan memiliki AUC lebih baik dari model Tobback et. al., namun tidak lebih baik dari model Zhao et. al.Analisis potensi dampak sistemik memberikan gambaran jaringan yang terbentuk dengan node sumber adalah perusahaan yang diprediksi bangkrut yang terhubung dengan perusahaan yang berelasi berdasarkan Shareholder. Besar kecilnya edge menggambarkan kuat lemahnya relasi yang ada. Penelitian disertasi ini berhasil membangun model prediksi kebangkrutan dengan variabel finansial dan relasional berbasis jaringan ultimate ownership dengan AUC lebih dari 90%. Hasil disertasi ini juga memberikan pandangan baru dalam melakukan deteksi konglomerasi dan analisis potensi dampak sistemik dari relasi yang ada.

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The application of network-based variables in the company’s bankruptcy prediction model with XGBoost method has not been widely found.. While bankruptcy prediction has been widely and diversely examined, most of them still focus on the use of financing variables. The systemic consequences of bankruptcy can jeopardize the stability of the financial system. The systemic impact under scrutiny primarily arises from conglomerates or corporate organizations. This prompts an inquiry into whether the insolvency of enterprises within a group may impact one another and induce systemic repercussions inside or outside the group. Prior to the market or public awareness of adverse situations, the regulatory body is anticipated to identify these detrimental circumstances early and implement necessary measures to preserve the company specifically and the economic system broadly.

This early detection is established through the creation of a predictive model that utilizes historical data to forecast bankruptcy and assess its potential systemic effects on a network of interconnected enterprises. This research utilizes financial and relational data from firms registered on the Indonesia Stock Exchange (IDX) spanning the years 2010 to 2021. The financial statistics utilized comprise balance sheet variables, solvency ratios, profitability ratios, and operating ratios. Relational data comprises three categories of relations according to the ultimate ownership theory: shareholders identified in the annual report, the board of commissioners (BoC), and the board of directors (BoD). Following an extensive analysis of research and experimentation, the XGBoost machine learning algorithm was selected as the model base due to its efficacy in predicting outcomes within unbalanced datasets. The final proposed model is a bankruptcy prediction model with the task of classifying bankrupt and non-bankrupt classes, with the XGBoost method, using network-based integration of input data in the form of financial and non-financial variables. This model consists of processing financial and relational variable inputs, prediction with XGBoost on 7 types of data integration, selecting the final prediction results based on the best AUC, and analyzing the potential systemic impact of the selected network based on the best data integration model in the previous stage.

This bankruptcy prediction model also contributes to visualizing the potential systemic impacts that may occur. At the bankruptcy prediction stage, a data integration model of financial and non-financial variables is used. The model of data integration exhibiting the highest AUC results is employed at the stage of analyzing potential systemic impacts. The expected impact is delineated based on the output from prior stage, according to the relationships established within the network of the optimal model. The test results utilizing 2019 data to forecast situations one year in advance demonstrated an AUC of 90.20% with the integration model of financial – Shareholder variables. The proposed model has a better AUC than the Tobback et. al., but not better than the Zhao et. al. model. The analysis of potential systemic impacts provides a picture of the network formed with the source node being a company predicted to go bankrupt that is connected to a company related to Shareholders. The size of the edge describes the strength of the existing relationship.

This dissertation research has succeeded in building a bankruptcy prediction model with financial and relational variables based on the ultimate ownership network with an AUC of more than 90%. The results of this dissertation also provide new insights into detecting conglomerates and analyzing the potential systemic impacts of existing relationships."

"Penelitian ini membahas pengaruh bank relationship terhadap risk-taking perusahaan dengan menggunakan total observasi sebanyak 82 perusahaan non-keuangan yang terdaftar di Bursa Efek Indonesia. Dengan menggunakan metode regresi data panel ditemukan hasil bahwa bank relationship berpengaruh terhadap risk-taking perusahaan. Penelitian ini menemukan bahwa perusahaan yang memiliki tingkat leverage yang tinggi dan melakukan bank relationship dalam jangka waktu pendek cenderung akan memiliki risiko yang tinggi. Kemudian, penelitian ini juga menemukan bahwa perusahaan dengan main bank lebih dari satu cenderung memiliki risiko lebih tinggi. Selain itu, peneliti juga menguji mengenai kepemilikan bank dalam bank relationship dan menemukan bahwa perusahaan yang melakukan bank relationship dengan bank asing cenderung memiliki risiko yang tinggi.

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]This research studied about the influence of bank relationship on corporate risk taking, by using 82 observations data of non financial company which is listed in Indonesia Stock Exchange. By using Panel Data Regression, this research found that bank relationship has significant effect on corporate risk taking. This research found that firms which have higher leverage and shorter duration of bank relationship will have higher risk. Furthermore, this research also found that firms which have more than one main bank will have higher risk. Besides, the author also test about bank ownership effect of bank relationship and found that firms which have relationship with foreign lending bank will have higher risk."