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題名 基於法說會逐字稿的短期股價波動性預測：採用模組化IOSFCR_R機制的自適應單隱藏層前饋神經網路
Adaptive SLFN with Modular IOSFCR_R Mechanism for Short-Term Stock Volatility Prediction Based on Earnings Conference Call Transcript
作者 莊宏祥
Chuang, Hung-Hsiang
貢獻者 蔡瑞煌
Tsaih, Rua-Huan
莊宏祥
Chuang, Hung-Hsiang
關鍵詞 法說會逐字稿
股價波動指標預測
語意檢索強化生成（RAG）
FinBERT 情感分析
模組化神經網路
IOSFCR_R 機制
Earnings Conference Call Transcript
Stock Volatility Prediction
Retrieval-Augmented Generation (RAG)
FinBERT Sentiment Analysis
Modular Neural Network
IOSFCR_R Mechanism
日期 2025
上傳時間 4-Aug-2025 14:26:23 (UTC+8)
摘要 財務法說會（Earnings Conference Call, ECC）為企業與投資人溝通財務現況與未來展望之重要管道，其語意內容常對市場情緒與股價波動產生顯著影響。然由於法說會文本結構複雜、語意層次多元，傳統自然語言處理（NLP）方法在有效提取關鍵語意與預測市場反應上仍存挑戰。為此，本研究提出一套整合語意檢索、情緒量化與自適應神經架構的預測機制，結合 Retrieval-Augmented Generation（RAG）、FinBERT 與模組化單層前饋神經網路（IOSFCR_R），以建構一個可處理事件驅動語意輸入並預測短期股價波動性的結構化系統。 本研究聚焦於探討法說會語意特徵對股價短期（3 日）與中期（7 日）波動性之可預測性，並進一步驗證模型是否具備跨公司語意泛化能力（Cross-Firm Semantic Generalization）。實驗設計上，模型僅於部分公司資料上訓練，並以未見公司（AMD、ORCL）為測試對象，進行外部驗證。模型版本中，IOSFCR_R_300 經由模組重組與正則化策略，有效控制過擬合問題，於所有評估指標（MAE、MAPE、RMSE）上皆顯著優於線性回歸（Linear Regression）與雙層神經網絡（2LNN）等傳統基準模型，展現穩定的跨樣本預測能力與語意解讀能力。 本研究成果顯示，結合語意檢索與情感向量化之深度學習架構，能有效擷取法說會語意對市場波動之隱含關係，並於變動劇烈之情境下保持預測穩定性，具備處理事件驅動金融資料之潛力。未來可進一步拓展應用至多語言、多產業與非語言特徵（如聲音、技術指標）之多模態預測任務，實現更全面且具彈性的市場行為建模系統。 
Earnings Conference Calls (ECCs) serve as a crucial medium through which firms communicate their financial status and strategic outlook to investors. The semantic content expressed by corporate executives often induces significant market sentiment and short-term stock volatility. However, the inherently complex and multi-dimensional nature of ECC transcripts presents persistent challenges for traditional natural language processing (NLP) methods in effectively extracting key insights and predicting market reactions. To address this, this study proposes an integrated prediction framework that combines semantic retrieval, sentiment quantification, and an adaptive neural architecture. Specifically, we incorporate Retrieval-Augmented Generation (RAG), FinBERT, and a modularized Input-Output Self-Organizing Fully Connected Regressor (IOSFCR_R) to forecast short-term stock volatility in response to event-driven textual data. This research focuses on examining the semantic predictability of ECCs for both short-term (3-day) and medium-term (7-day) stock volatility, while empirically testing the model’s ability to generalize across firms (cross-firm semantic generalization). The experimental setup involves training models on a subset of firms and evaluating performance on two unseen companies (AMD and ORCL), thereby providing a robust validation of generalization capability. Among the model variants, IOSFCR_R_300—with enhanced regularization and modular recomposition—effectively mitigates overfitting and consistently outperforms benchmark models such as Linear Regression and Two-Layer Neural Networks (2LNN) across all evaluation metrics (MAE, MAPE, RMSE). These findings demonstrate the model’s superior robustness and semantic interpretability in cross-firm forecasting scenarios. The results of this study underscore the potential of integrating semantic retrieval and sentiment-aware deep learning architectures in capturing the latent relationship between financial discourse and market behavior. The proposed IOSFCR_R model maintains high predictive stability even under volatile conditions, offering a promising solution for event-driven financial forecasting. Future research may further extend this framework to multilingual, cross-sector, and multimodal prediction tasks by incorporating prosodic and market-based indicators, thereby enabling more comprehensive and flexible modeling of investor responses. The study employs a dataset of 1,022 ECC transcripts from publicly listed firms, with the predictive target defined as stock volatility over a 3-day and 7-day post-event window. Experimental results demonstrate that the proposed IOSFCR_R-based framework outperforms traditional statistical models and non-modular neural networks across multiple evaluation metrics, including MAE, MAPE, and RMSE. The results indicate superior accuracy, interpretability, and generalization capability across different firms and time periods. This study provides empirical evidence that the integration of semantic-driven feature extraction with modular adaptive learning significantly enhances the modeling of event-based financial texts. Future research may extend this framework by incorporating multi-modal data sources—such as audio signals, news reports, or social media—to expand its applicability across languages, industries, and dynamic market conditions.  
參考文獻 Abarbanell, J. S., & Bushee, B. J. (1997). Fundamental Analysis, Future Earnings, and Stock Prices. Journal of Accounting Research, 35(1), 1. https://doi.org/10.2307/2491464 Araci, D. T. (2019). FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. https://arxiv.org/pdf/1908.10063 Awotunde, J. B., Adeniyi, E. A., Ogundokun, R. O., & Ayo, F. E. (2021). Application of Big Data with Fintech in Financial Services. 107–132. https://doi.org/10.1007/978-981-33-6137-9_3 Box, G. E. P. ., Jenkins, G. M. ., Reinsel, G. C. ., & Ljung, G. M. . (2016). Time series analysis : forecasting and control. https://books.google.com/books/about/Time_Series_Analysis.html?hl=zh-TW&id=rNt5CgAAQBAJ Cao, Y., Chen, Z., Pei, Q., Lee, N. J., Subbalakshmi, K. P., & Ndiaye, P. M. (2024). Ecc analyzer: Extract trading signal from earnings conference calls using large language model for stock performance prediction. arXiv preprint arXiv:2404.18470. Granger, C. W. J., & Teräsvirta, T. (1993). Modelling Nonlinear Economic Relationships. Modelling Nonlinear Economic Relationships. https://doi.org/10.1093/OSO/9780198773191.001.0001 Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/J.ESWA.2019.01.012 Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A Large Language Model for Extracting Information from Financial Text*. Contemporary Accounting Research, 40(2), 806–841. https://doi.org/10.1111/1911-3846.12832 Kumar, D., Sarangi, P. K., & Verma, R. (2022). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49, 3187–3191. https://doi.org/10.1016/J.MATPR.2020.11.399 Kumar, M., Economics, M. A.-S. in B. and, & 2014, undefined. (n.d.). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Researchgate.Net. Retrieved July 3, 2025, from https://www.researchgate.net/profile/Manoj_Kumar458/publication/263505554_An_Application_Of_Time_Series_Arima_Forecasting_Model_For_Predicting_Sugarcane_Production_In_India/links/0f31753b1c8ef0afeb000000/An-Application-Of-Time-Series-Arima-Forecasting-Model-For-Predicting-Sugarcane-Production-In-India.pdf Liu, F., Kang, Z., & Han, X. (2024). Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models. https://arxiv.org/abs/2408.05933v1 Li, H. (2024). An advanced learning mechanism for long-term time-series forecasting (Master’s thesis, Department of Management Information Systems, National Chengchi University, Taiwan). Mayew, W. J., & Venkatachalam, M. (2012). The power of voice: Managerial affective states and future firm performance. Journal of Finance, 67(1), 1–43. https://doi.org/10.1111/J.1540-6261.2011.01705.X;JOURNAL:JOURNAL:15406261;CTYPE:STRING:JOURNAL Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/J.ASEJ.2014.04.011 Nabipour, M., Nayyeri, P., Jabani, H., & Mosavi, A. (2020). Deep learning for Stock Market Prediction. Entropy 2020, Vol. 22, Page 840, 22(8), 840. https://doi.org/10.3390/e22080840 Neurocomputing, G. Z.-, & 2003, undefined. (n.d.). Time series forecasting using a hybrid ARIMA and neural network model. ElsevierGP ZhangNeurocomputing, 2003•Elsevier. Retrieved July 3, 2025, from https://www.sciencedirect.com/science/article/pii/S0925231201007020?casa_token=F2hWa1CS1UwAAAAA:aAaKrrZFPIaEKxL504c3_Vhd9EWuG--oYBKDis0UxNt1HbPLOBZ8_mr3R8iMcB6vVyBZQ0U1pA Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 5(1), 1–25. https://doi.org/10.1186/S40537-017-0111-6/TABLES/8 Tsaih, R. R. (1998). An explanation of reasoning neural networks. Mathematical and Computer Modelling, 28(2), 37–44. https://doi.org/10.1016/S0895-7177(98)00090-9 Yang, H., Zhang, B., Wang, N., Guo, C., Zhang, X., Lin, L., Wang, J., Zhou, T., Guan, M., Zhang, R., & Wang, C. D. (2024). FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4841493
描述 碩士
國立政治大學
資訊管理學系
112356016
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112356016
資料類型 thesis
dc.contributor.advisor 蔡瑞煌zh_TW
dc.contributor.advisor Tsaih, Rua-Huanen_US
dc.contributor.author (Authors) 莊宏祥zh_TW
dc.contributor.author (Authors) Chuang, Hung-Hsiangen_US
dc.creator (作者) 莊宏祥zh_TW
dc.creator (作者) Chuang, Hung-Hsiangen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 14:26:23 (UTC+8)-
dc.date.available 4-Aug-2025 14:26:23 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 14:26:23 (UTC+8)-
dc.identifier (Other Identifiers) G0112356016en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158572-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 112356016zh_TW
dc.description.abstract (摘要) 財務法說會（Earnings Conference Call, ECC）為企業與投資人溝通財務現況與未來展望之重要管道，其語意內容常對市場情緒與股價波動產生顯著影響。然由於法說會文本結構複雜、語意層次多元，傳統自然語言處理（NLP）方法在有效提取關鍵語意與預測市場反應上仍存挑戰。為此，本研究提出一套整合語意檢索、情緒量化與自適應神經架構的預測機制，結合 Retrieval-Augmented Generation（RAG）、FinBERT 與模組化單層前饋神經網路（IOSFCR_R），以建構一個可處理事件驅動語意輸入並預測短期股價波動性的結構化系統。 本研究聚焦於探討法說會語意特徵對股價短期（3 日）與中期（7 日）波動性之可預測性，並進一步驗證模型是否具備跨公司語意泛化能力（Cross-Firm Semantic Generalization）。實驗設計上，模型僅於部分公司資料上訓練，並以未見公司（AMD、ORCL）為測試對象，進行外部驗證。模型版本中，IOSFCR_R_300 經由模組重組與正則化策略，有效控制過擬合問題，於所有評估指標（MAE、MAPE、RMSE）上皆顯著優於線性回歸（Linear Regression）與雙層神經網絡（2LNN）等傳統基準模型，展現穩定的跨樣本預測能力與語意解讀能力。 本研究成果顯示，結合語意檢索與情感向量化之深度學習架構，能有效擷取法說會語意對市場波動之隱含關係，並於變動劇烈之情境下保持預測穩定性，具備處理事件驅動金融資料之潛力。未來可進一步拓展應用至多語言、多產業與非語言特徵（如聲音、技術指標）之多模態預測任務，實現更全面且具彈性的市場行為建模系統。 zh_TW
dc.description.abstract (摘要) Earnings Conference Calls (ECCs) serve as a crucial medium through which firms communicate their financial status and strategic outlook to investors. The semantic content expressed by corporate executives often induces significant market sentiment and short-term stock volatility. However, the inherently complex and multi-dimensional nature of ECC transcripts presents persistent challenges for traditional natural language processing (NLP) methods in effectively extracting key insights and predicting market reactions. To address this, this study proposes an integrated prediction framework that combines semantic retrieval, sentiment quantification, and an adaptive neural architecture. Specifically, we incorporate Retrieval-Augmented Generation (RAG), FinBERT, and a modularized Input-Output Self-Organizing Fully Connected Regressor (IOSFCR_R) to forecast short-term stock volatility in response to event-driven textual data. This research focuses on examining the semantic predictability of ECCs for both short-term (3-day) and medium-term (7-day) stock volatility, while empirically testing the model’s ability to generalize across firms (cross-firm semantic generalization). The experimental setup involves training models on a subset of firms and evaluating performance on two unseen companies (AMD and ORCL), thereby providing a robust validation of generalization capability. Among the model variants, IOSFCR_R_300—with enhanced regularization and modular recomposition—effectively mitigates overfitting and consistently outperforms benchmark models such as Linear Regression and Two-Layer Neural Networks (2LNN) across all evaluation metrics (MAE, MAPE, RMSE). These findings demonstrate the model’s superior robustness and semantic interpretability in cross-firm forecasting scenarios. The results of this study underscore the potential of integrating semantic retrieval and sentiment-aware deep learning architectures in capturing the latent relationship between financial discourse and market behavior. The proposed IOSFCR_R model maintains high predictive stability even under volatile conditions, offering a promising solution for event-driven financial forecasting. Future research may further extend this framework to multilingual, cross-sector, and multimodal prediction tasks by incorporating prosodic and market-based indicators, thereby enabling more comprehensive and flexible modeling of investor responses. The study employs a dataset of 1,022 ECC transcripts from publicly listed firms, with the predictive target defined as stock volatility over a 3-day and 7-day post-event window. Experimental results demonstrate that the proposed IOSFCR_R-based framework outperforms traditional statistical models and non-modular neural networks across multiple evaluation metrics, including MAE, MAPE, and RMSE. The results indicate superior accuracy, interpretability, and generalization capability across different firms and time periods. This study provides empirical evidence that the integration of semantic-driven feature extraction with modular adaptive learning significantly enhances the modeling of event-based financial texts. Future research may extend this framework by incorporating multi-modal data sources—such as audio signals, news reports, or social media—to expand its applicability across languages, industries, and dynamic market conditions.  en_US
dc.description.tableofcontents 摘要 i Abstract ii Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Motivation 3 1.3 Research Objectives 5 1.4 Thesis Structure 7 Chapter 2. Literature Review 9 2.1 Retrieval-Augmented Generation (RAG) 9 2.2 Query Design and Financial Question Bank 11 2.3 Volatility Formulation 12 2.4 FinBERT: A Financial Text Feature Extraction Model 13 2.5 Cramming and Pruning Techniques 17 2.6 IOSFCR: A Modular Learning Mechanism for Adaptive Neural Networks (Li, 2024) 20 Chapter 3. Research Methodology 26 3.1 Transcript Retrieval and Processing 30 3.2 Sentiment Feature Extraction via FinBERT 32 3.3 Prediction Model Design 34 3.4 Evaluation 36 Chapter 4. Experiment Design 37 4.1 Dataset 39 4.2 Experimental Design 47 Chapter 5. Experiment Result 50 5.1 Comparison of Model Settings 50 5.1.1 3-Day Volatility 50 5.1.2 7-Day Volatility 52 5.2 Baseline Model Comparison 55 5.2.1 3-Day Volatility Forecasting 56 5.2.2 7-Day Volatility Forecasting 58 5.3 Summary of Semantic Generalization and Market Response Forecasting Performance 61 Chapter 6. Conclusion and future work 64 6.1 Conclusion and Contributions 64 6.2 Limitations and Future Work 66 6.2.1 Limitations: 66 6.2.2 Future Research Directions: 67 Appendix 69 References 73zh_TW
dc.format.extent 4087050 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112356016en_US
dc.subject (關鍵詞) 法說會逐字稿zh_TW
dc.subject (關鍵詞) 股價波動指標預測zh_TW
dc.subject (關鍵詞) 語意檢索強化生成（RAG）zh_TW
dc.subject (關鍵詞) FinBERT 情感分析zh_TW
dc.subject (關鍵詞) 模組化神經網路zh_TW
dc.subject (關鍵詞) IOSFCR_R 機制zh_TW
dc.subject (關鍵詞) Earnings Conference Call Transcripten_US
dc.subject (關鍵詞) Stock Volatility Predictionen_US
dc.subject (關鍵詞) Retrieval-Augmented Generation (RAG)en_US
dc.subject (關鍵詞) FinBERT Sentiment Analysisen_US
dc.subject (關鍵詞) Modular Neural Networken_US
dc.subject (關鍵詞) IOSFCR_R Mechanismen_US
dc.title (題名) 基於法說會逐字稿的短期股價波動性預測：採用模組化IOSFCR_R機制的自適應單隱藏層前饋神經網路zh_TW
dc.title (題名) Adaptive SLFN with Modular IOSFCR_R Mechanism for Short-Term Stock Volatility Prediction Based on Earnings Conference Call Transcripten_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abarbanell, J. S., & Bushee, B. J. (1997). Fundamental Analysis, Future Earnings, and Stock Prices. Journal of Accounting Research, 35(1), 1. https://doi.org/10.2307/2491464 Araci, D. T. (2019). FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. https://arxiv.org/pdf/1908.10063 Awotunde, J. B., Adeniyi, E. A., Ogundokun, R. O., & Ayo, F. E. (2021). Application of Big Data with Fintech in Financial Services. 107–132. https://doi.org/10.1007/978-981-33-6137-9_3 Box, G. E. P. ., Jenkins, G. M. ., Reinsel, G. C. ., & Ljung, G. M. . (2016). Time series analysis : forecasting and control. https://books.google.com/books/about/Time_Series_Analysis.html?hl=zh-TW&id=rNt5CgAAQBAJ Cao, Y., Chen, Z., Pei, Q., Lee, N. J., Subbalakshmi, K. P., & Ndiaye, P. M. (2024). Ecc analyzer: Extract trading signal from earnings conference calls using large language model for stock performance prediction. arXiv preprint arXiv:2404.18470. Granger, C. W. J., & Teräsvirta, T. (1993). Modelling Nonlinear Economic Relationships. Modelling Nonlinear Economic Relationships. https://doi.org/10.1093/OSO/9780198773191.001.0001 Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/J.ESWA.2019.01.012 Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A Large Language Model for Extracting Information from Financial Text*. Contemporary Accounting Research, 40(2), 806–841. https://doi.org/10.1111/1911-3846.12832 Kumar, D., Sarangi, P. K., & Verma, R. (2022). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49, 3187–3191. https://doi.org/10.1016/J.MATPR.2020.11.399 Kumar, M., Economics, M. A.-S. in B. and, & 2014, undefined. (n.d.). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Researchgate.Net. Retrieved July 3, 2025, from https://www.researchgate.net/profile/Manoj_Kumar458/publication/263505554_An_Application_Of_Time_Series_Arima_Forecasting_Model_For_Predicting_Sugarcane_Production_In_India/links/0f31753b1c8ef0afeb000000/An-Application-Of-Time-Series-Arima-Forecasting-Model-For-Predicting-Sugarcane-Production-In-India.pdf Liu, F., Kang, Z., & Han, X. (2024). Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models. https://arxiv.org/abs/2408.05933v1 Li, H. (2024). An advanced learning mechanism for long-term time-series forecasting (Master’s thesis, Department of Management Information Systems, National Chengchi University, Taiwan). Mayew, W. J., & Venkatachalam, M. (2012). The power of voice: Managerial affective states and future firm performance. Journal of Finance, 67(1), 1–43. https://doi.org/10.1111/J.1540-6261.2011.01705.X;JOURNAL:JOURNAL:15406261;CTYPE:STRING:JOURNAL Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/J.ASEJ.2014.04.011 Nabipour, M., Nayyeri, P., Jabani, H., & Mosavi, A. (2020). Deep learning for Stock Market Prediction. Entropy 2020, Vol. 22, Page 840, 22(8), 840. https://doi.org/10.3390/e22080840 Neurocomputing, G. Z.-, & 2003, undefined. (n.d.). Time series forecasting using a hybrid ARIMA and neural network model. ElsevierGP ZhangNeurocomputing, 2003•Elsevier. Retrieved July 3, 2025, from https://www.sciencedirect.com/science/article/pii/S0925231201007020?casa_token=F2hWa1CS1UwAAAAA:aAaKrrZFPIaEKxL504c3_Vhd9EWuG--oYBKDis0UxNt1HbPLOBZ8_mr3R8iMcB6vVyBZQ0U1pA Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big Data: Deep Learning for financial sentiment analysis. Journal of Big Data, 5(1), 1–25. https://doi.org/10.1186/S40537-017-0111-6/TABLES/8 Tsaih, R. R. (1998). An explanation of reasoning neural networks. Mathematical and Computer Modelling, 28(2), 37–44. https://doi.org/10.1016/S0895-7177(98)00090-9 Yang, H., Zhang, B., Wang, N., Guo, C., Zhang, X., Lin, L., Wang, J., Zhou, T., Guan, M., Zhang, R., & Wang, C. D. (2024). FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4841493zh_TW