http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
CORRECT? CORECT!: Classification of ESG Ratings with Earnings Call Transcript
Haein Lee,Hae Sun Jung,Heungju Park,Jang Hyun Kim 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.4
While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.
Prediction of Corporate Bankruptcy with Machine Learning
Haein Lee,Byunghoon Yu,Jang Hyun Kim,Heungju Park 한국재무학회 2022 한국재무학회 학술대회 Vol.2022 No.11
This study examines the predictability of various machine learning and deep learning models in corporate default forecasts. Using a sample of U.S. corporate defaults over the period of 1963-2020, we find Ensemble classifier and Bi-LSTM classifier forecast the corporate bankruptcy better than other models and the predictability of the Ensemble classifier is more stable in year-to-year variability. Further, machine learning models outperform deep learning models in high yield grade samples, while deep learning models performs better than machine learning models in investment grade samples.
Haein Lee,Hae Sun Jung,Seon Hong Lee,Jang Hyun Kim 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.9
Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as “Metaverse” keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.