전체
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[세미나][26년 6월 3주차 세미나] 2026 Summer Break Plan: WACV 2027 Round 1 투고 준비( and 3 more others )
2026-06-19 -
[세미나][26년 6월 2주차 세미나] ZergRAG: 진화 기반 도메인 적응형 RAG 인덱싱 전략( and 3 more others )
2026-06-19 -
[세미나][26년 6월 1주차 세미나] Hallucination-Aware VLM for Fine-Grained Image and Object Captioning
2026-06-19 -
[세미나][26년 5월 5주차 세미나] Knowledge Graph RAG: Agentic Crawling and Graph Construction in Enterprise Documents 논문 리뷰( and 3 more others )
2026-06-04 -
[세미나][26년 5월 4주차 세미나] VCSSF: Video-Conditioned State Space Fusion 기반 Audio-Visual Speech Separation( and 3 more others )
2026-05-25 -
[세미나][26년 5월 3주차 세미나] 거시경제 컨텍스트 통합 Macro-Aware FT-Transformer(MA-FT) 제안: 부동산, 기업재무 규모별 예측 성능 비교( and 9 more others )
2026-05-18
공지사항
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[공지사항] 2026-1학기 컴퓨터비전 연구실 연구원 모집
2026-05-14 -
[공지사항] 학부연구생 서준일 KSC2025 학부생 최우수 논문상 수상
2026-01-30 -
[공지사항] 2025년 2학기 1차 컴퓨터비전 연구실 연구원 모집
2025-09-29 -
[공지사항] Graduate Program Admission for International Students
2025-07-22 -
[공지사항] 학부연구생 최규문, 조동현 봉림소프트웨어전시회 공대학장상 수상
2025-06-04 -
[공지사항] 2025년 1학기 컴퓨터비전 연구실 연구원 모집
2025-04-30
세미나
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[세미나] [26년 6월 3주차 세미나] 2026 Summer Break Plan: WACV 2027 Round 1 투고 준비( and 3 more others )
2026-06-19 -
[세미나] [26년 6월 2주차 세미나] ZergRAG: 진화 기반 도메인 적응형 RAG 인덱싱 전략( and 3 more others )
2026-06-19 -
[세미나] [26년 6월 1주차 세미나] Hallucination-Aware VLM for Fine-Grained Image and Object Captioning
2026-06-19 -
[세미나] [26년 5월 5주차 세미나] Knowledge Graph RAG: Agentic Crawling and Graph Construction in Enterprise Documents 논문 리뷰( and 3 more others )
2026-06-04 -
[세미나] [26년 5월 4주차 세미나] VCSSF: Video-Conditioned State Space Fusion 기반 Audio-Visual Speech Separation( and 3 more others )
2026-05-25 -
[세미나] [26년 5월 3주차 세미나] 거시경제 컨텍스트 통합 Macro-Aware FT-Transformer(MA-FT) 제안: 부동산, 기업재무 규모별 예측 성능 비교( and 9 more others )
2026-05-18
자료실
갤러리
International Journal
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Predicting Corporate Management Performance Using AI: Incorporating CEO Strategy Insights from Sustainable Management Reports
https://doi.org/10.1371/journal.pone.0347140 Abstract This study proposes an AI-based model to predict corporate management performance by combining financial data with strategic information extracted from CEO messages in sustainability reports. Using a dataset of 1,271 listed companies on Korea’s KOSPI and KOSDAQ markets (2016–2023), we applied eight machine learning and deep learning classifiers: KNN, SVM, GBM, CatBoost, GAN, RNN, LSTM, and Transformer. Financial variables were selected based on prior accounting research, while strategic variables were derived via text mining of CEO messages and categorized using the Sustainable Balanced Scorecard (SBSC) framework. Results show that models incorporating both financial and strategy-based variables outperformed those using financial data alone. Notably, the Transformer model achieved the highest predictive accuracy, followed by LSTM and RNN. These findings provide actionable insights for investors and corporate stakeholders while advancing interdisciplinary research between accounting and AI. Under 5-fold cross-validation, the best-performing hybrid model (Transformer with SBSC features) achieved Accuracy = 0.8467, AUC = 0.8481, and F1 = 0.8572, and adding SBSC strategy indicators improved mean performance across models (ΔAccuracy=+0.0121; ΔAUC=+0.0092; ΔF1=+0.0119).
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KoTaP: A Panel Dataset for Corporate Tax Avoidance, Performance, and Governance in Korea
https://www.nature.com/articles/s41597-026-06722-5#citeas Abstract This study introduces the Korean Tax Avoidance Panel (KoTaP), a long-term panel dataset of non-financial firms listed on KOSPI and KOSDAQ between 2011 and 2024. After excluding financial firms, firms with non-December fiscal year ends, capital impairment, and negative pre-tax income, the final dataset consists of 12,653 firm-year observations from 1,754 firms. KoTaP is designed to treat corporate tax avoidance as a predictor variable and link it to multiple domains, profitability, stability, growth, and governance. Tax avoidance itself is measured using complementary indicators—cash effective tax rate, GAAP effective tax rate, and book–tax difference measures—with adjustments to ensure interpretability. A key strength of KoTaP is its standardized firm-year panel structure with standardized variables and its consistency with international literature on the distribution and correlation of core indicators. At the same time, it reflects distinctive institutional features of Korean firms, such as concentrated ownership, high foreign shareholding, and elevated liquidity ratios, providing both international comparability and contextual uniqueness. KoTaP enables applications in econometric and machine-learning applications, including explainable methods.
