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| 研究生： |
馬玉寶 Ma, Yu-Pao |
|---|---|
| 論文名稱： |
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 |
| 指導教授： |
林靖庭
Lin, Ching-Ting 林建秀 Lin, Chien-Hsiu |
| 口試委員: |
洪偉峰
Hung, Wei-Feng 吳牧恩 Wu, Mu-En 陳虹伶 Chen, Hung-Ling 林建秀 Lin, Chien-Hsiu 林靖庭 Lin, Ching-Ting |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 金融學系 Department of Money and Banking |
| 論文出版年： | 2024 |
| 畢業學年度： | 112 |
| 語文別： | 英文 |
| 論文頁數： | 63 |
| 中文關鍵詞： | 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 |
| 外文關鍵詞： | Time Series Forecasting, Supply Chain Interplay, AI Investments Trend, GICS Information Technology, VAR Models |
| 相關次數： | 點閱：89 下載：0 |
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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.
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 62
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。
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全文公開日期 2029/06/16