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Deny Martin
"ABSTRAK
The economy of Indonesia has rapidly grown since its first economic
turmoil in 1997/1998. The annual growth rate of the GDP exceeded 6% in the last
four 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 the
industrial market or in the capital market.
In spite of the ‘bull’ market, the risk of financial distress remains alive and
the economic direction might change because of the volatility of business
environment. There is no firm protected or immune from financial adversity that
may result in failure, insolvency, default or bankruptcy. Plummeting stock price,
reduced dividend payment, consecutive net loss, massive lay-offs, pending
obligations and a fair number of other negative signs are common association with
financial distress.
Widely recognized, financial distress prediction models may be examined
to assess a firm’s economic situation for further purposes. Altman, Ohlson,
Zmijewsky, Fulmer, and Springate are some of notable researchers to which their
models are referred to evaluating the soundness of a firm. However, each market
has its own financial distress environment that in consequence any financial
distress prediction model requires an evaluation whether or not the model
adequately fits to a certain market, in particular Indonesia for this case. The
importance of predictors and accuracy will minimize producing misleading results
from the economic forecast.
The results of this testing against the first hypothesis showed that none of
the adjusted models included all the variables of the base model, respectively.
There were some variables with insufficient explanatory power to predict the
cessation of activities of the tested firms. The second hypothesis argued that the
adjusted models were less capable than those developed originally in terms of
accuracy.

ABSTRACT
The economy of Indonesia has rapidly grown since its first economic
turmoil in 1997/1998. The annual growth rate of the GDP exceeded 6% in the last
four 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 the
industrial market or in the capital market.
In spite of the ‘bull’ market, the risk of financial distress remains alive and
the economic direction might change because of the volatility of business
environment. There is no firm protected or immune from financial adversity that
may result in failure, insolvency, default or bankruptcy. Plummeting stock price,
reduced dividend payment, consecutive net loss, massive lay-offs, pending
obligations and a fair number of other negative signs are common association with
financial distress.
Widely recognized, financial distress prediction models may be examined
to assess a firm’s economic situation for further purposes. Altman, Ohlson,
Zmijewsky, Fulmer, and Springate are some of notable researchers to which their
models are referred to evaluating the soundness of a firm. However, each market
has its own financial distress environment that in consequence any financial
distress prediction model requires an evaluation whether or not the model
adequately fits to a certain market, in particular Indonesia for this case. The
importance of predictors and accuracy will minimize producing misleading results
from the economic forecast.
The results of this testing against the first hypothesis showed that none of
the adjusted models included all the variables of the base model, respectively.
There were some variables with insufficient explanatory power to predict the
cessation of activities of the tested firms. The second hypothesis argued that the
adjusted models were less capable than those developed originally in terms of
accuracy."
Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2012
T34699
UI - Tesis Membership  Universitas Indonesia Library
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Octria Larasati Siswosoebrotho
"ABSTRACT
Financial distress merupakan kondisi kesulitan keuangan yang pada umumnya dialami oleh perusahaan sebelum perusahaan tersebut dapat dinyatakan bangkrut. Dengan menggunakan laporan keuangan, kondisi tersebut pada dasarnya dapat diprediksi. Prediksi dari financial distress sangat berguna bagi manajemen perusahaan untuk melakukan tindakan korektif dalam antisipasinya menghadapi kebangkrutan. Model prediksi dari financial distress sendiri telah berkembang dari penggunaan statistik tradisional hingga artificial intelligence atau machine learning. Penelitian ini bertujuan untuk menganalisis model prediksi financial distress dengan menerapkan machine learning dan membandingkan tiga algoritma dari data mining yaitu decision tree, support vector machine, dan artificial neural network. Sampel dalam penelitian ini menggunakan 115 perusahaan distressed dan 115 perusahaan non-distressed yang aktif di Bursa Efek Indonesia selama periode 2011 hingga 2016 yang diteliti untuk dua tahun yaitu l-t dan t-1. Dalam penelitian ini, dari sebanyak 29 rasio keuangan akan dipilih rasio yang paling sesuai dengan menggunakan feature selection. Hasil dari penelitian menunjukkan bahwa algoritma decision tree dengan tingkat akurasi sebesar 86,37 untuk tahun l-t dan decision tree dengan tingkat akurasi sebesar 88,98 untuk tahun l t-1 memiliki tingkat akurasi yang paling tinggi dalam mengantisipasi financial distress di Indonesia.

ABSTRACT
Financial distress is a condition of financial difficulties that generally a firm would have first go through before the company can be declared bankrupt. By using financial statements, this condition basically could be predicted. Prediction of financial distress is very useful as it could help firms rsquo management to take corrective actions in anticipation of bankruptcy. The predictive model of financial distress itself has evolved from the use of traditional statistics to artificial intelligence or machine learning. This study aims to analyze financial distress prediction model by applying machine learning and comparing three algorithms from data mining namely decision tree, support vector machine, and artificial neural network. The sample in this study used 115 distressed companies and 115 non distressed companies active on the Indonesia Stock Exchange during the period 2011 to 2016 studied for two years ie t and t-1 . In this research, from 29 financial ratios will be selected the most appropriate ratios by using feature selection. The result of this research shows that decision tree algorithm with 86.37 accuracy for year t and decision tree with accuracy of 88.98 for year t-1 has the highest accuracy in anticipating financial distress in Indonesia. "
2018
S-Pdf
UI - Skripsi Membership  Universitas Indonesia Library
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Muhammad Mahesa Panji Putra
"ABSTRAK
Karya 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.

ABSTRACT
This 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."
Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2012
T-Pdf
UI - Tesis Membership  Universitas Indonesia Library
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Mochamad Nabil Faindra Putra
"Penelitian ini bertujuan untuk membuat model prediksi financial distress guna menanggapi penurunan kinerja perusahaan terbuka akibat pandemi COVID-19. Metode penelitian yang digunakan adalah regresi logistik untuk menguji hubungan antara financial distress dengan variabel independen seperti rasio keuangan dan rasio pasar saham. Hasil penelitian menunjukkan bahwa rasio leverage, solvabilitas, dan profitabilitas berpengaruh lebih signifikan dibandingkan rasio lainnya. Karena financial distress tidak terjadi secara tiba-tiba, penelitian ini membagi modelnya menjadi 2, yaitu 1 tahun sebelum distress (M1) dan 2 tahun sebelum distress (M2). Hasilnya menunjukkan bahwa M1 memiliki hasil yang lebih baik, dengan akurasi prediksi mencapai 91,63% (dengan default cut-off point = 0,5). Penulis juga memperkirakan ulang model berbasis akuntansi lainnya dan membandingkan model penulis dengan model lain. Hasilnya menunjukkan bahwa model penulis berkinerja lebih baik dibandingkan model lain (selisih +12,24%); sehingga model penulis menjadi model berbasis akuntansi yang paling cocok untuk prediksi financial distress untuk emiten di Bursa Efek Indonesia. Hal ini dikarenakan model penulis didasarkan pada kombinasi variabel akuntansi dan variabel pasar modal

This study aims to create a new model for financial distress prediction in response to public companies’ deteroriation of performance due to the COVID-19 pandemic. The research method used was logistic regression to examine the relation between financial distress and independent variables such as financial ratios and stock market ratios. The result shows that the ratios of leverage, solvency, and profitability affected more significantly than other ratios. Since financial distress does not occur suddenly, this study divided its model into 2, namely 1 year before the distress (M1) and 2 years before the distress (M2). The results indicate that M1 had a better result, with 91,63% classification accuracy (by default cut-off point = 0.5). We also re-estimated other accounting-based models and compare our model to them. The results demonstrate that our model performed better than other models (+12,24% difference); thereby our model appeared to be the most suitable accounting-based model for financial distress prediction for the Indonesia Stock Exchange. This is because author’s model is based on a combination of accounting variables and capital market variables."
Depok: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2022
T-pdf
UI - Tesis Membership  Universitas Indonesia Library
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Andreadi Noor Pradipta
"Penelitian ini bertujuan untuk menguji dampak defisit dan surplus finansial terhadap penyesuaian struktur modal perusahaan. Defisit (surplus) finansial merupakan kondisi arus kas yang keluar (masuk) melebihi arus uang kas yang masuk (keluar) dan penyesuaian struktur modal adalah penyesuaian menuju leverage yang optimal. Kondisi defisit dan surplus finansial diduga memiliki dampak terhadap kecepatan penyesuaian leverage. Penelitian ini menggunakan metode Generalized Least Square. Hasil dari penelitian ini adalah penyesuaian struktur modal bersifat asimetris karena adanya biaya penyesuaian dan kondisi defisit dan surplus finansial mempengaruhi kecepatan penyesuaian struktur modal.

This study aims to examine the impact of the firm's financial deficit and surplus to capital structure adjustment. Financial deficit (surplus) is a condition of the cash inflow (outflow) that exceeds their cash outflow (inflow) and capital structure adjustment is an adjustment to the optimal leverage. Financial deficit and surplus is suspected to have an impact on the speed of capital structure adjustment. This study uses the generalized least square method. Results from this study is the adjustment of capital structure is asymmetrical due to adjustment costs and deficit and surplus financial affected the speed of capital structure adjustment."
Depok: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2013
S47096
UI - Skripsi Membership  Universitas Indonesia Library
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Andri Dwi Yulianti
"ABSTRAK
Krisis keuangan global yang terjadi pada tahun 2008 menimbulkan dampak yang luas pada perekonomian di Indonesia. Dampak tersebut berpengaruh pada perusahaan-perusahaan publik yang dapat menyebabkan perusahaan mengalami kondisi financial distress sehingga menimbulkan ancaman kebangkrutan. Penelitian ini bertujuan untuk menganalisis pengaruh rasio keuangan terhadap prediksi kondisi financial distress dengan menggunakan model regresi logistik. Sampel yang digunakan adalah perusahaan-perusahaan sektor non-keuangan yang terdaftar di BEI periode 2008-2017. Penelitian ini menggunakan 235 sampel dan hasil penelitian menunjukkan bahwa rasio Cash Flow Margin dan Debt to Equity Ratio memberikan pengaruh positif dalam memprediksikan kondisi financial distress perusahaan, sedangkan rasio Return on Asset dan Cash to Current Liabilities memberikan pengaruh negatif dalam memprediksikan kondisi financial distress perusahaan. Terdapat 2 rasio keuangan yang memiliki pengaruh signifikan, yaitu Cash Flow Margin dan Cash to Current Liabilities, sedangkan 2 rasio lainnya yaitu Return on Asset dan Debt to Equity Ratio tidak memiliki pengaruh signifikan dalam memprediksikan kondisi financial distress perusahaan."
Depok: Fakultas Ilmu Administrasi Universitas Indonesia, 2020
S-pdf
UI - Skripsi Membership  Universitas Indonesia Library
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Lubis, Mentari Maimunah
"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.

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."
Jakarta: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2024
T-pdf
UI - Tesis Membership  Universitas Indonesia Library
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Brilya Dwijayanti
"Penelitian ini bertujuan untuk menganalisis pengaruh eksposur nilai tukar terhadap return saham perusahaan domestik non-keuangan di Indonesia dan determinan eksposur nilai tukarnya. Determinan eksposur nilai tukar yang digunakan dalam penelitian ini adalah kekuatan keuangan, kekuatan operasional, kesempatan pertumbuhan, ukuran perusahaan, dan likuiditas perusahaan. Hasil dari penelitian menunjukkan bahwa sekitar 33 persen dari sampel yang ada menunjukkan terdapat eksposur nilai tukar Rupiah terhadap Euro dan 57 persen dari sampel menunjukkan terdapat eksposur nilai tukar Rupiah terhadap Dollar Amerika Serikat. Kekuatan keuangan yang diproksikan oleh variabel Total Debt to Total Asset dan kekuatan operasional yang diproksikan oleh Asset Tangibility serta Asset Turnover terbukti signifikan memengaruhi besaran eksposur nilai tukar Rupiah terhadap Euro. Sedangkan, kekuatan operasional yang diproksikan oleh Asset Turnover, ukuran perusahaan yang diproksikan oleh Market Value, dan likuiditas yang diproksikan oleh Dividend Payout terbukti signifikan memengaruhi besaran eksposur nilai tukar Rupiah terhadap Dollar Amerika Serikat.

This study aimed to analyze the effect of exchange rate exposure on stock returns of non-financial domestic companies in Indonesia and the determinants of exchange rate exposure. Determinants of exchange rate exposure used in this study is the financial strength, operational strengths, growth opportunities, firm size, and liquidity. Results of the study showed that about 33 percent of the sample showed that there were exchange rate exposure Rupiah against the Euro and 57 percent of the samples showed that there were exposure of the Rupiah against the U.S. Dollar. Financial strength is proxied by the variable Total Debt to Total Assets and operational strength is proxied by Asset Turnover and Asset Tangibility proved to significantly affects the magnitude exchange rate exposure Rupiah against the Euro. Whereas, the operational strength of the proxied by Asset Turnover, size is proxied by the Market Value, and liquidity is proxied by Dividend Payout proved to significantly affects the magnitude exchange rate exposure Rupiah against the U.S. dollar."
Depok: Universitas Indonesia, 2013
S45865
UI - Skripsi Membership  Universitas Indonesia Library
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Rini Ambar Untari
"Tujuan dari penelitian ini untuk mengetahui pengaruh faktor makroekonomi terhadap risiko kebangkrutan pada perusahaan-perusahaan non- keuangan yang terdaftar di Bursa Efek Indonesia. Dalam penelitian ini menggunakan data sekunder kuantitatif serta diuji menggunakan panel data regression dengan model Fixed Effect. Hasil penelitian ini adalah  faktor makroekonomi berupa inflasi,  Produk Domestik Bruto , dan Nilai tukar rupiah terhadap USD, berpengaruh secara signifikan terhadap  risiko kebangkrutan perusahaan.

The purpose of this study is to study the macroeconomic effects on backruptcy risk on non-financial companies listed on the Indonesia Stock Exchange. In this study using quantitative secondary data and panel data regression with the Fixed Effects model. The results of this study are macroeconomic factors consisting of inflation, Gross Domestic Product, and the rupiah exchange rate to USD, and  Firm Size significantly affects the risk of corporate bankruptcy."
Depok: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2019
S-pdf
UI - Skripsi Membership  Universitas Indonesia Library
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Andreas Hartoyo Yaputra
"ABSTRAK
Penelitian ini bertujuan untuk mengembangkan model prediksi peringkat kredit perusahaan dengan menggunakan data keuangan yang tersedia di publik. Model regresi probit ordinal digunakan dalam penelitian ini untuk menentukan spesifikasi dari interval peringkat kredit. Model diuji menggunakan data perusahaan non-keuangan yang terdaftar di Bursa Efek Indonesia dalam periode 2007-2016. Periode tersebut dipilih sebagai perbandingan situasi ekonomi Indonesia yang sempat mengalami dua kali pelemahan ekonomi pada tahun 2008 dan 2013. Berdasarkan hasil empiris, didapat sebuah model yang mampu memprediksi peringkat kredit perusahaan di situasi ekonomi yang beragam dengan menggunakan data keuangan yang sederhana. Model yang dihasilkan mampu memiliki kemampuan prediksi dalam periode satu sampai tiga tahun ke depan.

ABSTRACT
This study aim to develop a methodology using accounting data to construct a credit rating prediction model in Indonesia. Ordered probit analysis is being used in this study to know the specification of statistically significant credit rating intervals. A simple, public domain information based model are able to construct to predict credit rating using financial variable. We test our model using only quantitative and publicly available information of Indonesian listed firms over 2012-2017, a period which includes both crisis phase and slow economic growth of Indonesian economy. Under this scheme, we observe not only a clear and timely response of ratings to the changing economic environment, but we also obtain significant predictive ability model."
Depok: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2018
T50385
UI - Tesis Membership  Universitas Indonesia Library
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