本研究探討OpenAI產品發布對台灣AI概念股股價的影響，研究採用事件研究法，以GPT-1至GPT-4及ChatGPT的發布為事件，分析台灣55家台灣上市櫃AI概念股在事件日的標準化平均異常報酬（SAR）和標準化累積平均異常報酬（SCAR）。結果顯示OpenAI產品發布顯著影響相關概念股的股價，尤其在GPT-3、ChatGPT及GPT-4發布日當天之標準化異常報酬皆顯著異於零。在標準化累積異常報酬的事件窗口分析方面，GPT-1、GPT-2、GPT-3及GPT-4在發布日前20天至後1天最為顯著，說明這三個GPT系列產品可能反應投資人可利用內線交易獲取超額報酬之情形。同時，本研究利用ANOVA單因子變異數檢測五項GPT系列產品的標準化累積異常報酬之間是否存在差異，結果顯示GPT-2及GPT-4這兩項產品與其他系列產品相比顯著不同。
此外，本研究通過多元迴歸分析，主要探討產品原創性、研發經費比率、股東權益報酬率、營收成長率、負債比率、現金流量比率和應收帳款週轉率對標準化累積平均異常報酬率的影響。結果表明，產品原創性對股價的異常報酬有顯著影響，並且在短期效果上股東權益報酬率、營收成長率、公司年限及淨值市價比也對股價的累積異常報酬產生顯著影響。本研究進一步將55家AI概念股分為四個產業類別，並對標準化累積異常報酬進行分析，研究顯示資訊服務產業對標準化累積異常報酬的影響尤為顯著。本研究為投資者和企業提供了理解科技創新對資本市場影響的重要參考，並建議企業在新產品開發中注重研發投入和創新性，從而提升市場競爭力和投資回報，同時為投資者的標的選擇提供有價值的分析。

This study investigates the impact of OpenAI product releases on the stock prices of AI-related stocks in Taiwan. Using the event study method, it analyzes the standardized average abnormal returns (SAR) and standardized cumulative average abnormal returns (SCAR) of 55 listed AI-related stocks around the release dates of GPT-1, GPT-2, GPT-3, GPT-4, and ChatGPT. The results demonstrate that these releases significantly affect stock prices, with GPT-3, ChatGPT, and GPT-4 showing particularly notable SAR on their release days. The SCAR analysis indicates significant effects from 20 days before to one day after the release for GPT-1, GPT-2, GPT-3, and GPT-4, suggesting potential insider trading advantages.
ANOVA analysis reveals significant differences in SCAR among the GPT series, with GPT-2 and GPT-4 showing significant deviations compared to other products. Multiple regression analysis identifies product originality, return on equity, revenue growth rate, company age, and net worth to market value ratio as significant factors influencing SCAR. The study also finds that the information services industry exerts a particularly significant impact on SCAR.
This research provides valuable insights for investors and enterprises, emphasizing the importance of R&D investment and innovation in enhancing market competitiveness and investment returns. It suggests that companies should focus on these areas to improve their market position and provide investors with a detailed analysis for better decision-making.

一、 中文部分
吳政忠(2022)。2022臺灣AI國力調查。國家科學及技術委員會。
沈中華、李建然(2000)。事件研究法。華泰出版社。
李國榮、呂素蓮和洪榆棟(2012)。企業購併績效再檢驗：以臺灣電子業為例。臺灣企業績效期刊，5(2)，153-171。
金成隆、林修葳、邱煒恒(2005)。研究發展支出與資本支出的價值攸關性：以企業生命週期論析。中山管理評論，13(3)，617-643。
邱皓政(2010)。量化研究與統計分析：SPSS/PASW資料分析範例解析(第五版)。五南圖書出版社。
林哲鵬和郭怡萍(2007)。競爭策略下新產品宣告對股價的影響：就台灣資訊電子業公司之檢視。科技管理學刊，12(1)，p1-28。
林薰苑、陳仲詠、古惟中、楊仲捷(2023)。ChatGPT的衝擊與因應之道。電工通訊季刊，91-98。
陳昇瑋、溫怡玲(2019)。人工智慧在臺灣：產業轉型的契機與挑戰。天下雜誌出版社。
陳隆麒、郭敏華、吳政穎和盧雲江(1997)。財務評等五力分析模型：以觀光業為例。管理與資訊學報，2期，077-108。
陳彥婷(2021)。庫藏股減資與現金減資之宣告效果比較-以臺灣上市上櫃公司為例，Journal of Data Analysis, 16(2), 59-84。
范秉航(2019)。科技化？網路化？AI化？金融服務的課題與挑戰。臺灣經濟研究月刊。42(11)，70-75。
高麗萍、謝佳臻和邵姵雅(2014)。新產品宣告對供應鏈廠商股價影響之研究-以蘋果 iPad 與 iPhone 宣告為例。管理資訊計算，3(2)，p.31-44。
梁榮輝、李東歐和林思好(2007)。企業資產經營能力對股價影響。華人經濟，5(1)，1-20。
黃宜侯、沈中華和陳志鈞(2013)。金融海嘯主要事件對信用違約交換之影響。中山管理評論，21(2)，p.255-298。
游博仰(2023)。資料中心GPU廠商發展策略分析：以輝達為例。國立臺灣大學管理學院商學研究所，碩士論文。
鄭哲惠、王兼善和張嘉文(2015) 現金流量資訊衡量電子業的企業生命週期之概況：兼論其對盈餘攸關性之影響。東吳經紀商學學報，91期，p.61-80
謝宇宸(2023)。銀行服務業應對生成式人工智慧技術的創新策略與應用展望 --以玉山商業銀行為例。臺灣大學商學研究所碩士，碩士論文。
二、 英文部分
Abdullah, M., Madain, A., & Jararweh, Y. (2022). ChatGPT: Fundamentals, applications, and social impacts. In 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), 1-8. IEEE. https://doi.org/10.1109/SNAMS58071.2022.10062688
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
Bollerslev, C., Chou, R. Y. & Kroner, K. F. (1992). ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence. Journal of Economic, 52(1-2), 5-59. https://doi.org/10.1016/0304-4076(92)90064-X
Brown, S. J. and Warner, J. B., 1980, Measuring Security Price Performance, Journal of Financial Economics, 8(3), 205-258. https://doi.org/10.1016/0304-405X(80)90002-1
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ..., Amodei, D. (2020). Language models are few-shot learners. OpenAI. https://arxiv.org/pdf/2005.14165.pdf
Cao, L. (2020). AI in finance: A review. Available at SSRN 3647625. http://dx.doi.org/10.2139/ssrn.3647625
Chu, J., He, Y., Hui, K. W., & Lehavy, R. (2024). New product announcements, innovation disclosure, and future firm performance. Review of Accounting Studies, 1-32. https://doi.org/10.1007/s11142-024-09820-0
Cormier, D., & Gordon, I. M. (2001). An examination of social and environmental reporting strategies. Accounting, Auditing & Accountability Journal, 14(5), 587-617. https://doi.org/10.1108/EUM0000000006264
Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Sun, X., Xu, J., Li, L., & Sui, Z. (2023). A survey on in-context learning. arXiv preprint arXiv:2301.00234. https://arxiv.org/abs/2301.00234
Dolley, J. C. (1933). Characteristics and procedure of common stock split-ups. Harvard Business Review, 11(3), 316-326. https://libraries.escp.eu/Default/doc/bth/6763607/characteristics-and-procedure-of-common-stock-split-ups
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008. https://doi.org/10.2307/1912773
Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance, 47(2), 427-465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x
Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. International Economic Review, 10(1), 1-21. https://doi.org/10.2307/2525569
Hendershott, T., & Riordan, R. (2013). Algorithmic trading and the market for liquidity. Journal of Financial and Quantitative Analysis, 48(4), 1001-1024. https://doi.org/10.1017/S0022109013000471
Kalyan, K. S. (2024). A survey of GPT-3 family large language models including ChatGPT and GPT-4. Natural Language Processing Journal, 6(100048). https://doi.org/10.1016/j.nlp.2023.100048
Khan, M., & Watts, R. L. (2009). Estimation and empirical properties of a firm-year measure of accounting conservatism. Journal of Accounting and Economics, 48(2-3), 132-150. https://doi.org/10.1016/j.jacceco.2009.08.002
Khandani, A. E., Kim, A. J. & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787. https://doi.org/10.1016/j.jbankfin.2010.06.001
Komo, D., Chang, C. I., & Ko, H. (1994). Neural network technology for stock market index prediction. In International Conference on Speech, Image Processing and Neural Networks, 553-556. https://doi.org/10.1109/SIPNN.1994.344854
Koubaa, A. (2023) Gpt-4 vs. GPT-3.5: A Concise Showdown. Preprints. https://doi.org/10.20944/preprints202303.0422.v1
Lee, S. P., & Chen, H. J. (2011). Corporate governance and firm value as determinants of CEO compensation in Taiwan: 2SLS for panel data model. Management Research Review, 34(3), 252-265. https://doi.org/10.1108/01409171111116286
Lin, W. C., & Chang, S. C. (2012). Corporate governance and the stock market reaction to new product announcements. Review of Quantitative Finance and Accounting, 39(2), 273-291. https://doi.org/10.1007/s11156-011-0248-x
Lopez de Prado, M. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
Meek, G. K., Roberts, C. B., & Gray, S. J. (1995). Factors influencing voluntary annual report disclosures by U.S., U.K. and continental European multinational corporations. Journal of International Business Studies, 26(3), 555-572. https://doi.org/10.1057/palgrave.jibs.8490186
Morris, M. R., Sohl-Dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., & Legg, S. (2023). Levels of AGI: Operationalizing progress on the path to AGI. Google DeepMind. https://doi.org/10.48550/arXiv.2311.02462
Nelson, D. M. Q., Pereira, A. C. M. & de Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN), 1419-1426. https://doi.org/10.1109/IJCNN.2017.7966019
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35, 27730-27744.
https://doi.org/10.48550/arXiv.2203.02155
Porter, M. E. (1985). Competitive Advantage: Creating and Sustaining Superior performance. The Free press.
Porter, M. E. & Stern, S. (2001). Innovation: Location matters. Sloan Management Review, 42(4):28-36. https://sloanreview.mit.edu/article/innovation-location-matters/
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. Technical report, OpenAI. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3(1), 121-154. https://doi.org/10.1016/j.iotcps.2023.04.003
Schneider, E. T. R., de Souza, J. V. A., Gumiel, Y. B., Moro, C., & Paraiso, E. C. (2021). A GPT-2 language model for biomedical texts in Portuguese. International Symposium on Computer-Based Medical Systems (CBMS), 474-479. https://doi.org/10.1109/CBMS52027.2021.00056
Sheng, S. C., Wai, H. K., & Hwa, I. K. (2005). The wealth effect of new product introductions on industry rivals. Journal of Business, 78(3), 969-996. https://doi.org/10.1086/429650
Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., ... & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv:1908.09203. https://doi.org/10.48550/arXiv.1908.09203
Stickel, S. E. (1985). The effect of Value Line Investment Survey rank changes on common stock price. Journal of Financial Economics, 14(1), 121-143. https://doi.org/10.1016/0304-405X(85)90046-7
Wang, S., Liu, Y., Xu, Y., Zhu, C., & Zeng, M. (2021). Want to reduce labeling cost? GPT-3 can help. arXiv preprint arXiv:2108.13487. https://doi.org/10.48550/arXiv.2108.13487
Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 9(1), 1-27. https://doi.org/10.1037/h0025848
Zheng, X., Zhang, C., Woodland, P. C. (2021). Adapting GPT, GPT-2 and BERT language models for speech recognition. In 2021 IEEE Automatic speech recognition and understanding workshop ,162-168. https://doi.org/10.1109/ASRU51503.2021.9688232
三、 網路資源
林以璿(2023, November 22)。OpenAI政變深度解析》全世界最聰明的一群人，為何打造了失敗的企業架構？取自 https://www.cw.com.tw/article/5128188
楊孟軒(2023, May, 25)。AI供應鏈包括哪些公司？3張表看懂。天下雜誌取。取自https://reurl.cc/QXAkj0
趙龍(2023，December)。打造第二成長曲線-證交所推動數位轉型。正交專論。取自https://www.twse.com.tw/market_insights/zh/detail/ff8080818c000119018c32f29ee9010e
Anthropic (2023). Company: Anthropic. URLhttps://www.anthropic.com/company
Cheryl Martin (2019, March 27)。人工智慧與機器學習如何推動學術研究發展。取自 https://blogs.nvidia.com.tw/2019/03/27/how-ai-machine-learning-are-advancing-academic-research/
Heaven, W. D. (2022). The new version of GPT-3 is much better behaved (and should be less toxic). MIT Technology Review. https://www.technologyreview.com/2022/01/27/1044398/new-gpt3-openai-chatbot-language-model-ai-toxic-misinformation/.
Joe Chung (2024, January 14). ChatGPT概念股是什麽？2024年有哪些值得關注的聊天機器人概念股？我要如何投資？取自
https://www.mitrade.com/zh/insights/shares/us-stock-recommendation/chatgpt-concept-stock
OpenAI (2018, April 9). OpenAI Charter. 取自 https://openai.com/charter.
OpenAI (2023). OpenAI: About. 取自 https://openai.com/about.
OpenAI (2023, March 14) GPT-4 Technical Report. 取自 https://openai.com/research/gpt-4