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題名 從供應鏈相互影響到預測股價報酬：Nvidia之AI供應鏈時間序列實證分析
From Supply Chain Interplay to Stock Returns Foresight: Time series insights from Nvidia Corp’s AI Supply Chain
作者 馬玉寶
Ma, Yu-Pao
貢獻者 林靖庭<br>林建秀
Lin, Ching-Ting<br>Lin, Chien-Hsiu
馬玉寶
Ma, Yu-Pao
關鍵詞 時間序列預測
供應鏈相互影響
AI投資趨勢
GICS資訊科技
VAR模型
Time Series Forecasting
Supply Chain Interplay
AI Investments Trend
GICS Information Technology
VAR Models
日期 2024
上傳時間 1-Jul-2024 12:33:10 (UTC+8)
摘要 輝達（Nvidia）之AI供應鏈成員間相互影響的關係確實能提升預測股價報酬之模型表現！本研究採用四種時間序列方法—ARMA、ARMA-GARCH、ARMA-EGARCH及VAR模型—進行Nvidia之股價報酬預測，並對於其AI供應鏈成員之預測能力進行探討，包含美國及臺灣市場之AI（Artificial Intelligence）伺服器ODM（Original Design Manufacturer）供應商、同業競爭者及終端客戶。實證結果顯示，在長期預測期間下，納入市場數據、AI伺服器ODM供應商及終端客戶之VAR模型（模型六）預測表現優於其他預測模型，其中，在樣本外預測10、20、60、120及240天下，模型六之RMSE分別為0.007869、0.008998、0.011440、0.012992及0.017583。且無論短期或長期預測期間下，多變量模型之預測表現優於單變量模型，反映供應鏈成員股價報酬互相影響的關係在預測上的重要性。值得關注的是，在輝達之AI供應鏈中，終端客戶之預測能力優於同業競爭者，而AI伺服器ODM供應商無法有效提升模型之預測表現，此結果可能歸因於AI投資趨勢仍為現在進行式，而本研究僅考量自2018至2023年之資料。綜上所述，本研究透過結合供應鏈成員股價報酬互相影響的關係．建構創新的預測方法，應用於最新的AI產業，提供投資人對於全球資訊科技業的投資邏輯，藉此優化其投資決策。
The interplay within Nvidia (NVDA)’s AI chip supply chain does improve the predictive accuracy of stock return! We employ four time series methods—ARMA, ARMA-GARCH, ARMA-EGARCH, and VAR models—to forecast NVDA’s stock return out-of-sample. Especially, we examine the forecasting power of NVDA's AI chip supply chain interplay, including its AI server ODM suppliers in Taiwan stock market, as well as its competitors and end customers in the U.S. stock market. Our findings uncover that the VAR model (Model 6), incorporating market data, AI server ODM suppliers and end customers information, outperforms other forecasting methods in the long-term forecast horizon. For the out-of-sample forecasts of 10, 20, 60, 120, and 240 days, the Model 6 exhibits RMSE of 0.007869, 0.008998, 0.011440, 0.012992, and 0.017583, respectively. Multivariate models consistently outperform univariate models across both short- and long-term forecast horizons, highlighting the importance of considering supply chain interplay in stock returns forecasting. Notably, NVDA’s end customers demonstrate greater forecasting power than its competitors within AI chip supply chain, though limited evidence supports the contribution of NVDA’s AI server ODM suppliers to predictive accuracy, possibly due to the ongoing AI investment trend and our study period being limited to 2018-2023. Finally, our novel forecasting method, integrating supply chain interplay, provides valuable insights for investors in the dynamic global technology industry.
參考文獻 陳旭昇（2022）。時間序列分析：總體經濟與財務金融之應用（三版）。雙葉書廊。 Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there?. Review of Financial Studies, 20(3), 651-707. Bahrami, A., Shamsuddin, A., & Uylangco, K. (2018). Out‐of‐sample stock return predictability in emerging markets. Accounting & Finance, 58(3), 727-750. Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18. Bernanke, B. S., & Kuttner, K. N. (2005). What explains the stock market's reaction to Federal Reserve policy?. Journal of Finance, 60(3), 1221-1257. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average?. Review of Financial Studies, 21(4), 1509-1531. Cochrane, D., & Orcutt, G. H. (1949). Application of least squares regression to relationships containing auto-correlated error terms. Journal of the American Statistical Association, 44(245), 32-61. Cohen, L., & Frazzini, A. (2008). Economic links and predictable returns. Journal of Finance, 63(4), 1977-2011. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. Journal of Finance, 40(3), 793-805. Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409-428. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. Eun, C. S., & Shim, S. (1989). International transmission of stock market movements. Journal of Financial and Quantitative Analysis, 24(2), 241-256. Fama, E. F. (1970). Efficient capital markets. Journal of Finance, 25(2), 383-417. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. Fama, E. F. (1995). Random walks in stock market prices. Financial Analysts Journal, 51(1), 75-80. Ferson, W. E., & Harvey, C. R. (1994). Sources of risk and expected returns in global equity markets. Journal of Banking & Finance, 18(4), 775-803. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438. Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45(3), 881-898. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Jensen, M. C. (1978). Some anomalous evidence regarding market efficiency. Journal of Financial Economics, 6(2/3), 95-101. Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics, 74(2), 209-235. Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41-66. Nelson, D. B. (1992). Filtering and forecasting with misspecified ARCH models I: Getting the right variance with the wrong model. Journal of Econometrics, 52(1-2), 61-90. Paatela, A., Noschis, E., & Hameri, A. P. (2017). Abnormal stock returns using supply chain momentum and operational financials. Journal of Portfolio Management, 43(2), 50-60. Patelis, A. D. (1997). Stock return predictability and the role of monetary policy. Journal of Finance, 52(5), 1951-1972. Persons, W. M. (1919). II. The Method Used. Review of Economic Statistics, 117-139. Pesaran, M. H., & Timmermann, A. (1995). Predictability of stock returns: Robustness and economic significance. Journal of Finance, 50(4), 1201-1228. Rapach, D. E., Strauss, J. K., & Zhou, G. (2013). International stock return predictability: What is the role of the United States?. Journal of Finance, 68(4), 1633-1662. Rozeff, M. S., & Kinney Jr, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3(4), 379-402. Rozeff, M. S. (1984). Dividend yields are equity risk premiums. Journal of Portfolio Management, 68-75. Shahrur, H., Becker, Y. L., & Rosenfeld, D. (2010). Return predictability along the supply chain: the international evidence. Financial Analysts Journal, 66(3), 60-77. Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike's information criterion. Biometrika, 63(1), 117-126. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48. Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic Perspectives, 15(4), 101-115. Timmermann, A. (2018). Forecasting methods in finance. Annual Review of Financial Economics, 10, 449-479. Timmermann, A., & Granger, C. W. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), 15-27.
描述 碩士
國立政治大學
金融學系
111352007
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352007
資料類型 thesis
dc.contributor.advisor 林靖庭<br>林建秀zh_TW
dc.contributor.advisor Lin, Ching-Ting<br>Lin, Chien-Hsiuen_US
dc.contributor.author (Authors) 馬玉寶zh_TW
dc.contributor.author (Authors) Ma, Yu-Paoen_US
dc.creator (作者) 馬玉寶zh_TW
dc.creator (作者) Ma, Yu-Paoen_US
dc.date (日期) 2024en_US
dc.date.accessioned 1-Jul-2024 12:33:10 (UTC+8)-
dc.date.available 1-Jul-2024 12:33:10 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2024 12:33:10 (UTC+8)-
dc.identifier (Other Identifiers) G0111352007en_US
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=171537-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 111352007zh_TW
dc.description.abstract (摘要) 輝達（Nvidia）之AI供應鏈成員間相互影響的關係確實能提升預測股價報酬之模型表現！本研究採用四種時間序列方法—ARMA、ARMA-GARCH、ARMA-EGARCH及VAR模型—進行Nvidia之股價報酬預測，並對於其AI供應鏈成員之預測能力進行探討，包含美國及臺灣市場之AI（Artificial Intelligence）伺服器ODM（Original Design Manufacturer）供應商、同業競爭者及終端客戶。實證結果顯示，在長期預測期間下，納入市場數據、AI伺服器ODM供應商及終端客戶之VAR模型（模型六）預測表現優於其他預測模型，其中，在樣本外預測10、20、60、120及240天下，模型六之RMSE分別為0.007869、0.008998、0.011440、0.012992及0.017583。且無論短期或長期預測期間下，多變量模型之預測表現優於單變量模型，反映供應鏈成員股價報酬互相影響的關係在預測上的重要性。值得關注的是，在輝達之AI供應鏈中，終端客戶之預測能力優於同業競爭者，而AI伺服器ODM供應商無法有效提升模型之預測表現，此結果可能歸因於AI投資趨勢仍為現在進行式，而本研究僅考量自2018至2023年之資料。綜上所述，本研究透過結合供應鏈成員股價報酬互相影響的關係．建構創新的預測方法，應用於最新的AI產業，提供投資人對於全球資訊科技業的投資邏輯，藉此優化其投資決策。zh_TW
dc.description.abstract (摘要) The interplay within Nvidia (NVDA)’s AI chip supply chain does improve the predictive accuracy of stock return! We employ four time series methods—ARMA, ARMA-GARCH, ARMA-EGARCH, and VAR models—to forecast NVDA’s stock return out-of-sample. Especially, we examine the forecasting power of NVDA's AI chip supply chain interplay, including its AI server ODM suppliers in Taiwan stock market, as well as its competitors and end customers in the U.S. stock market. Our findings uncover that the VAR model (Model 6), incorporating market data, AI server ODM suppliers and end customers information, outperforms other forecasting methods in the long-term forecast horizon. For the out-of-sample forecasts of 10, 20, 60, 120, and 240 days, the Model 6 exhibits RMSE of 0.007869, 0.008998, 0.011440, 0.012992, and 0.017583, respectively. Multivariate models consistently outperform univariate models across both short- and long-term forecast horizons, highlighting the importance of considering supply chain interplay in stock returns forecasting. Notably, NVDA’s end customers demonstrate greater forecasting power than its competitors within AI chip supply chain, though limited evidence supports the contribution of NVDA’s AI server ODM suppliers to predictive accuracy, possibly due to the ongoing AI investment trend and our study period being limited to 2018-2023. Finally, our novel forecasting method, integrating supply chain interplay, provides valuable insights for investors in the dynamic global technology industry.en_US
dc.description.tableofcontents 摘要 2 Abstract 3 Contents 4 List of Figures 5 List of Tables 6 1. Introduction 7 2. Forecasting Models and Methodology 11 2.1 Stationary Time Series 11 2.2 Information Criteria 12 2.3 Granger’s Causality Test 12 2.4 Time Series Models 13 2.5 Assessing Forecasting Models 17 3. Data and Sample 19 3.1 Variable Selection 23 3.2 Descriptive Time Series of the Data 27 4. Empirical Findings 32 4.1 Univariate Models 33 ARMA Model 34 ARMA-GARCH Model 36 ARMA-EGARCH Model 37 4.2 Multivariate Models 38 VAR Model: Market and Competitors 44 VAR Model: Market, Competitors and AI Server ODM Suppliers 46 VAR Model: Market, End Customers and AI Server ODM Suppliers 49 4.3 Forecasting Performance on Different Forecast Horizon 51 4.4 Summary of Empirical Results 53 5. Conclusions and Futures Research 54 Appendix 56 Reference 62zh_TW
dc.format.extent 5324813 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352007en_US
dc.subject (關鍵詞) 時間序列預測zh_TW
dc.subject (關鍵詞) 供應鏈相互影響zh_TW
dc.subject (關鍵詞) AI投資趨勢zh_TW
dc.subject (關鍵詞) GICS資訊科技zh_TW
dc.subject (關鍵詞) VAR模型zh_TW
dc.subject (關鍵詞) Time Series Forecastingen_US
dc.subject (關鍵詞) Supply Chain Interplayen_US
dc.subject (關鍵詞) AI Investments Trenden_US
dc.subject (關鍵詞) GICS Information Technologyen_US
dc.subject (關鍵詞) VAR Modelsen_US
dc.title (題名) 從供應鏈相互影響到預測股價報酬：Nvidia之AI供應鏈時間序列實證分析zh_TW
dc.title (題名) From Supply Chain Interplay to Stock Returns Foresight: Time series insights from Nvidia Corp’s AI Supply Chainen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 陳旭昇（2022）。時間序列分析：總體經濟與財務金融之應用（三版）。雙葉書廊。 Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there?. Review of Financial Studies, 20(3), 651-707. Bahrami, A., Shamsuddin, A., & Uylangco, K. (2018). Out‐of‐sample stock return predictability in emerging markets. Accounting & Finance, 58(3), 727-750. Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18. Bernanke, B. S., & Kuttner, K. N. (2005). What explains the stock market's reaction to Federal Reserve policy?. Journal of Finance, 60(3), 1221-1257. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average?. Review of Financial Studies, 21(4), 1509-1531. Cochrane, D., & Orcutt, G. H. (1949). Application of least squares regression to relationships containing auto-correlated error terms. Journal of the American Statistical Association, 44(245), 32-61. Cohen, L., & Frazzini, A. (2008). Economic links and predictable returns. Journal of Finance, 63(4), 1977-2011. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. Journal of Finance, 40(3), 793-805. Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409-428. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. Eun, C. S., & Shim, S. (1989). International transmission of stock market movements. Journal of Financial and Quantitative Analysis, 24(2), 241-256. Fama, E. F. (1970). Efficient capital markets. Journal of Finance, 25(2), 383-417. Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. Fama, E. F. (1995). Random walks in stock market prices. Financial Analysts Journal, 51(1), 75-80. Ferson, W. E., & Harvey, C. R. (1994). Sources of risk and expected returns in global equity markets. Journal of Banking & Finance, 18(4), 775-803. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424-438. Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45(3), 881-898. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. Jensen, M. C. (1978). Some anomalous evidence regarding market efficiency. Journal of Financial Economics, 6(2/3), 95-101. Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics, 74(2), 209-235. Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41-66. Nelson, D. B. (1992). Filtering and forecasting with misspecified ARCH models I: Getting the right variance with the wrong model. Journal of Econometrics, 52(1-2), 61-90. Paatela, A., Noschis, E., & Hameri, A. P. (2017). Abnormal stock returns using supply chain momentum and operational financials. Journal of Portfolio Management, 43(2), 50-60. Patelis, A. D. (1997). Stock return predictability and the role of monetary policy. Journal of Finance, 52(5), 1951-1972. Persons, W. M. (1919). II. The Method Used. Review of Economic Statistics, 117-139. Pesaran, M. H., & Timmermann, A. (1995). Predictability of stock returns: Robustness and economic significance. Journal of Finance, 50(4), 1201-1228. Rapach, D. E., Strauss, J. K., & Zhou, G. (2013). International stock return predictability: What is the role of the United States?. Journal of Finance, 68(4), 1633-1662. Rozeff, M. S., & Kinney Jr, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of Financial Economics, 3(4), 379-402. Rozeff, M. S. (1984). Dividend yields are equity risk premiums. Journal of Portfolio Management, 68-75. Shahrur, H., Becker, Y. L., & Rosenfeld, D. (2010). Return predictability along the supply chain: the international evidence. Financial Analysts Journal, 66(3), 60-77. Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike's information criterion. Biometrika, 63(1), 117-126. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48. Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic Perspectives, 15(4), 101-115. Timmermann, A. (2018). Forecasting methods in finance. Annual Review of Financial Economics, 10, 449-479. Timmermann, A., & Granger, C. W. (2004). Efficient market hypothesis and forecasting. International Journal of Forecasting, 20(1), 15-27.zh_TW