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題名 台灣半導體企業營收預測：Lasso 迴歸與總經指標
Enhancing Revenue Forecasting for Taiwan's Semiconductor Industry: A Lasso Regression Approach with Macroeconomic Indicators
作者 林琨翔
Lin, Kun-Xiang
貢獻者 莊皓鈞
Chuang, Hao-Chun
林琨翔
Lin, Kun-Xiang
關鍵詞 半導體
營收預測
Lasso 迴歸
總經指標
科技半導體
預測模型
Semiconductor Industry
Revenue Forecasting
Lasso Regression
Macroeconomic Indicators
Technology
Forecasting Model
日期 2024
上傳時間 5-Aug-2024 12:12:08 (UTC+8)
摘要 營收預測對於產能規劃至關重要，能夠有效預估客戶需求，進而優化生產資源配置。本研究採用 Lasso 迴歸分析，將總體經濟指標整合至營收預測模型中，以提升預測準確度。我們以台灣半導體企業為研究對象，分析其營收數據與總體經濟指標的關聯性，探討不同供應鏈角色所對應的關鍵總體經濟指標。研究結果發現，Lasso 迴歸分析後，關鍵總經指標在提前 4-6 個月的營收預測上所提升的準確度顯著優於提前 1-3 個月，且關鍵總體經濟指標會隨目標公司的特性及扮演的角色而有所差異，進一步解釋經濟變化對半導體企業營收的影響。傳統的營收預測方法主要依賴專家知識和經驗，存在局限性。本研究提議採用資料驅動方法建立營收預測的標準程序，使企業能夠根據分析結果做出商業決策。
As the Revenue forecasting plays a crucial role to predict our customer demands in order to prepare for the production. This study delves into the integration of macroeconomic indicators into revenue forecast models using Lasso regression analysis to enhance accuracy. We conducted an analysis of revenue data from Taiwan semiconductor companies and macroeconomic indicators to identify the most influential macroeconomic indicators at various stages within the supply chain. The results indicate that after the utilization of Lasso regression, incorporating macroeconomic indicators significantly improves revenue prediction accuracy for the 4-6 month prior compared to the 1-3 month prior. Additionally, we discovered that the key macroeconomic indicators varied based on the characteristics of the target companies, providing some insights behind of their relationship. Given the limitations of traditional revenue prediction methods based on expert knowledge, we advocate for a data-driven approach to establish a standardized procedure for revenue predictions, enabling informed business decisions based on the analysis results.
參考文獻 Chen, H. L. 2008. Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms. Journal of Real Estate Finance and Economics, 40: 310–331. Dzikevičius, A., & Šaranda, S. 2016. Establishing a set of macroeconomic factors explaining variation over time of performance in business sectors. Verslas: Teorija ir Praktika, 17: 159–166. Elliott, G., Gargano, A., & Timmermann, A. 2015. Complete subset regressions with large-dimensional sets of predictors. Journal of Economic Dynamics & Control, 54: 86–110. Gajewar, A., & Bansal, G. 2016. Revenue forecasting for enterprise products. arXiv preprint, arXiv:1701.06624. Hung, H. C., Chiu, Y. C., & Wu, M. C. 2017. Analysis of competition between IDM and fabless-foundry business models in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing, 30: 254-260. Hung, S. W., He, D.-S., & Lu, W.-M. 2014. Evaluating the dynamic performances of business groups from the carry-over perspective: A case study of Taiwan’s semiconductor industry. Omega, 46: 1-10. Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. 2018. Tactical sales forecasting using a very large set of macroeconomic indicators. European Journal of Operational Research, 264: 558–569. Sanders, N.R., & Ritzman, L.P. 1991. On knowing when to switch from quantitative to judgemental forecasts. International Journal of Operations & Production Management, 11(6): 27–37. Tibshirani, R. 1996. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1): 267-288. Tibshirani, R. 2011. Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodological), 73(3): 273-282. Tsai, B. H. 2009. Dynamic modeling and simulation of Taiwan's IC industrial clustering to China. PICMET: Portland International Center for Management of Engineering and Technology, Proceedings, 3307-3314. Verstraete, G., Aghezzaf, E. H., & Desmet, B. 2020. A leading macroeconomic indicators’ based framework to automatically generate tactical sales forecasts. Computers & Industrial Engineering, 139: 106-169. Wang, C.-T., & Chiu, C.-S. 2014. Competitive strategies for Taiwan’s semiconductor industry in a new world economy. Technology in Society, 36: 60–73. Whitfield, R.I., & Duffy, A.H.B. 2013. Extended revenue forecasting within a service industry. International Journal of Production Economics, 141(2): 505-518.
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
111363051
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111363051
資料類型 thesis
dc.contributor.advisor 莊皓鈞zh_TW
dc.contributor.advisor Chuang, Hao-Chunen_US
dc.contributor.author (Authors) 林琨翔zh_TW
dc.contributor.author (Authors) Lin, Kun-Xiangen_US
dc.creator (作者) 林琨翔zh_TW
dc.creator (作者) Lin, Kun-Xiangen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 12:12:08 (UTC+8)-
dc.date.available 5-Aug-2024 12:12:08 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 12:12:08 (UTC+8)-
dc.identifier (Other Identifiers) G0111363051en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152437-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 111363051zh_TW
dc.description.abstract (摘要) 營收預測對於產能規劃至關重要，能夠有效預估客戶需求，進而優化生產資源配置。本研究採用 Lasso 迴歸分析，將總體經濟指標整合至營收預測模型中，以提升預測準確度。我們以台灣半導體企業為研究對象，分析其營收數據與總體經濟指標的關聯性，探討不同供應鏈角色所對應的關鍵總體經濟指標。研究結果發現，Lasso 迴歸分析後，關鍵總經指標在提前 4-6 個月的營收預測上所提升的準確度顯著優於提前 1-3 個月，且關鍵總體經濟指標會隨目標公司的特性及扮演的角色而有所差異，進一步解釋經濟變化對半導體企業營收的影響。傳統的營收預測方法主要依賴專家知識和經驗，存在局限性。本研究提議採用資料驅動方法建立營收預測的標準程序，使企業能夠根據分析結果做出商業決策。zh_TW
dc.description.abstract (摘要) As the Revenue forecasting plays a crucial role to predict our customer demands in order to prepare for the production. This study delves into the integration of macroeconomic indicators into revenue forecast models using Lasso regression analysis to enhance accuracy. We conducted an analysis of revenue data from Taiwan semiconductor companies and macroeconomic indicators to identify the most influential macroeconomic indicators at various stages within the supply chain. The results indicate that after the utilization of Lasso regression, incorporating macroeconomic indicators significantly improves revenue prediction accuracy for the 4-6 month prior compared to the 1-3 month prior. Additionally, we discovered that the key macroeconomic indicators varied based on the characteristics of the target companies, providing some insights behind of their relationship. Given the limitations of traditional revenue prediction methods based on expert knowledge, we advocate for a data-driven approach to establish a standardized procedure for revenue predictions, enabling informed business decisions based on the analysis results.en_US
dc.description.tableofcontents Chapter 1 Introduction 1 Chapter 2 Literature Review 3 Chapter 3 Data and Method 5 3.1 Data 5 3.2 Method 9 Chapter 4 Results of Analysis 12 4.1 Aggregate Analysis 12 4.2 Subgroup analysis 15 Chapter 5 Conclusion 29 Appendix 31 Reference List 37zh_TW
dc.format.extent 1174245 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111363051en_US
dc.subject (關鍵詞) 半導體zh_TW
dc.subject (關鍵詞) 營收預測zh_TW
dc.subject (關鍵詞) Lasso 迴歸zh_TW
dc.subject (關鍵詞) 總經指標zh_TW
dc.subject (關鍵詞) 科技半導體zh_TW
dc.subject (關鍵詞) 預測模型zh_TW
dc.subject (關鍵詞) Semiconductor Industryen_US
dc.subject (關鍵詞) Revenue Forecastingen_US
dc.subject (關鍵詞) Lasso Regressionen_US
dc.subject (關鍵詞) Macroeconomic Indicatorsen_US
dc.subject (關鍵詞) Technologyen_US
dc.subject (關鍵詞) Forecasting Modelen_US
dc.title (題名) 台灣半導體企業營收預測：Lasso 迴歸與總經指標zh_TW
dc.title (題名) Enhancing Revenue Forecasting for Taiwan's Semiconductor Industry: A Lasso Regression Approach with Macroeconomic Indicatorsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Chen, H. L. 2008. Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms. Journal of Real Estate Finance and Economics, 40: 310–331. Dzikevičius, A., & Šaranda, S. 2016. Establishing a set of macroeconomic factors explaining variation over time of performance in business sectors. Verslas: Teorija ir Praktika, 17: 159–166. Elliott, G., Gargano, A., & Timmermann, A. 2015. Complete subset regressions with large-dimensional sets of predictors. Journal of Economic Dynamics & Control, 54: 86–110. Gajewar, A., & Bansal, G. 2016. Revenue forecasting for enterprise products. arXiv preprint, arXiv:1701.06624. Hung, H. C., Chiu, Y. C., & Wu, M. C. 2017. Analysis of competition between IDM and fabless-foundry business models in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing, 30: 254-260. Hung, S. W., He, D.-S., & Lu, W.-M. 2014. Evaluating the dynamic performances of business groups from the carry-over perspective: A case study of Taiwan’s semiconductor industry. Omega, 46: 1-10. Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. 2018. Tactical sales forecasting using a very large set of macroeconomic indicators. European Journal of Operational Research, 264: 558–569. Sanders, N.R., & Ritzman, L.P. 1991. On knowing when to switch from quantitative to judgemental forecasts. International Journal of Operations & Production Management, 11(6): 27–37. Tibshirani, R. 1996. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1): 267-288. Tibshirani, R. 2011. Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodological), 73(3): 273-282. Tsai, B. H. 2009. Dynamic modeling and simulation of Taiwan's IC industrial clustering to China. PICMET: Portland International Center for Management of Engineering and Technology, Proceedings, 3307-3314. Verstraete, G., Aghezzaf, E. H., & Desmet, B. 2020. A leading macroeconomic indicators’ based framework to automatically generate tactical sales forecasts. Computers & Industrial Engineering, 139: 106-169. Wang, C.-T., & Chiu, C.-S. 2014. Competitive strategies for Taiwan’s semiconductor industry in a new world economy. Technology in Society, 36: 60–73. Whitfield, R.I., & Duffy, A.H.B. 2013. Extended revenue forecasting within a service industry. International Journal of Production Economics, 141(2): 505-518.zh_TW