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All Journal Signifikan : Jurnal Ilmu Ekonomi Media Statistika Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series International Journal of Advances in Intelligent Informatics Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik Inferensi Madaniya Proceeding ISETH (International Summit on Science, Technology, and Humanity) Bulletin of Applied Mathematics and Mathematics Education Aceh International Journal of Science and Technology Eduvest - Journal of Universal Studies
Dedy Dwi Prastyo
Department Of Statistics, Institut Teknologi Sepuluh Nopember
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Journal : Media Statistika

 1 
CORPORATE FINANCIAL DISTRESS PREDICTION USING STATISTICAL EXTREME VALUE-BASED MODELING AND MACHINE LEARNING Dedy Dwi Prastyo; Rizki Nanda Savera; Danny Hermawan Adiwibowo
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.1-12

Abstract

The industrial sector plays a leading role in an economy such that the financial stability of companies from this sector be a big concern. Two financial ratios, i.e., the Interest Coverage Ratio (ICR) and the Return on Assets (ROA), are used to determine the corporate financial distress conditions. This work considers two schemes for determining financial distress. First, a company is categorized as distressed if either ICR<1 or ROA<0. The second scheme is for when both ICR<1 and ROA<0 are met. The proportion of distressed and non-distressed companies is imbalanced. Our work views the distressed companies (minority class) as a rare event, causing the proportion to be extremely small, such that the Extreme Value Theory can be employed. The so-called Generalized Extreme Value regression (GEVR), developed from GEV distribution, predicts the distressed labels. The GEVR's performance is compared using machine learning with and without feature selection. The feature selection in GEVR uses backward elimination. The model for prediction employs a drift or windowing concept, i.e., using past-period predictors to predict the current response. The empirical results found that the GEVR, with and without the feature selection, provides the best prediction for financial distress.
ENSEMBLE-BASED LOGISTIC REGRESSION ON HIGH-DIMENSIONAL DATA: A SIMULATION STUDY Widhianingsih, Tintrim Dwi Ary; Kuswanto, Heri; Prastyo, Dedy Dwi
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.13-24

Abstract

Dramatic computation growth encourages big data era, which induces data size escalation in various fields. Apart from huge sample size, cases arise high-dimensional data having more feature size than its samples. High-computing power compels the usage of modern approaches to deal with this typical dataset, while in practice, common logistic regression method is yet applied due to its simplicity and explainability. Applying logistic regression on high-dimensional data arises multicollinearity, overfitting, and computational complexity issues. Logistic Regression Ensemble (Lorens) and Ensemble Logistic Regression (ELR) are the logistic-regression-based alternative methods proposed to solve these problems. Lorens adopts ensemble concept with mutually exclusive feature partitions to form several subsets of data, while ELR involves feature selection in the algorithm by drawing part of features based on probability ranking value. This paper uncovers the effectiveness of Lorens and ELR applied to high-dimensional data classification through simulation study under three different scenarios, i.e., with feature size variation, for imbalanced high-dimensional data, and under multicollinearity conditions. Our simulation study reveals that ELR outperforms Lorens and obtains more stable performance over different feature sizes and imbalanced data settings. On the other hand, Lorens achieves more reliable performance than ELR on a simulation study with a multicollinearity issue.
APPLICATION OF THE DYNAMIC FACTOR MODEL ON NOWCASTING SECTORAL ECONOMIC GROWTH WITH HIGH-FREQUENCY DATA Supriyatna, Putu Krishnanda; Prastyo, Dedy Dwi; Akbar, Muhammad Sjahid
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.128-139

Abstract

Economic growth is crucial for planning, yet delayed data releases challenge timely decision-making. Nowcasting offers near-real-time insights using high-frequency indicators (released monthly, weekly, or even daily) to predict low-frequency variables (quarterly or yearly). This study uses high-frequency indicators (monthly), such as stock price changes, air quality, transportation data, financial conditions, and Google Trends, to nowcast quarterly GDP through the Dynamic Factor Model (DFM). The data used span from January 2010 until March 2023, which is split into two: January 2010 until March 2022 for training data and the rest as testing data. Compared to the benchmark Autoregressive Moving Average with Exogenous Variables (ARMAX) model, DFM demonstrates superior accuracy with lower symmetric Mean Absolute Percentage Error (sMAPE). In addition, to evaluate the model performance in nowcasting the GDP across the sector using DFM, the additional metrics, i.e., Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Adjusted R-squared, concluded that in the industrial and transportation sectors results in sufficient nowcasting of GDP, Meanwhile, In the financial sector, the results of the nowcasting GDP give poor estimation results that need improvement.
Co-Authors A. Tuti Rumiati Abdul Karim Abdul Karim Adatul Mukarohmah Affandy*, Nur Azizah Ahsan, Muhammad Akhmad Faturohman Bambang Widjanorko Otok Bambang Widjanorko Otok Budi Manfaat budi manfaat, budi Danny Hermawan Adiwibowo Data Iranata dwi purwanto Eksiandayani, Santi Era Ardhya Pramesti Erma Oktania Permatasari Faturohman, Akhmad Fithriasari, Kartika Forestryani, Veniola Fredi Suryadi Fredi Suryadi Hendri Prabowo Heri Kuswanto Hidayatul Khusna Husna Miratin Nuroini Ismaini Zain Latifah, Turfah Luh Putu Shintya Handayani Maharsi, Rengganis Woro Maulana, Mahendra Andiek Muhammad Hisyam Lee Muhammad Sjahid Akbar Nadjadji Anwar Novi Ajeng Salehah Prandipa, Rayza Pusaka, Semerdanta Rhifda Zukhrufi Rizki Nanda Savera Roosyidah, Nila Ayu Nur S Suhartono, S Salafiyah Isnawati Santi Puteri Rahayu Saputra, Wisnowan Hendy Setiawan Setiawan Setiawan Setiawan Shofi Andari Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Supriyatna, Putu Krishnanda Veniola Forestryani Vita Ratnasari Wardoyo, Wasis Wibawati Wibawati Widhianingsih, Tintrim Dwi Ary Yusop, Zulkifli