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題名 機器學習為基礎的現貨與期貨動態採購模型
A Machine Learning-Based Dynamic Purchasing Model of Spot and Futures作者 薛名皓
Hsueh, Ming-Hao貢獻者 莊皓鈞
Chuang, Hao-Chun
薛名皓
Hsueh, Ming-Hao關鍵詞 機器學習
數據分析
數值模擬
動態採購
原物料期貨
Machine learning
Data analysis
Numerical simulation
Dynamic procurement
Raw material futures日期 2022 上傳時間 2-五月-2022 15:00:56 (UTC+8) 摘要 對於需要持續優化獲利的企業而言，能否在原物料價格隨機變動下進行成本最佳化的採購規劃，對其營運和財務績效的管理甚為重要。此決策問題在原物料有現貨和期貨可供選擇時又更為複雜。一般而言，企業可以採用以歷史價格先行預測價格走勢，並依據預測值訂定未來一段期間採購計畫的策略。有別於此種先預測後決策的傳統思維，本研究提出一個新的動態採購最佳化模型，從機器學習觀點運用現貨和期貨的價格數據，以預估最佳採購量而非價格預測為訓練目標，進而求得各個輸入特徵的最佳係數解。模擬分析結果顯示，此動態採購模型的成本表現顯著優於依據價格預測值所做的採購決策。除了模擬實驗，我們使用近兩年的布蘭特原油現貨與期貨價格進行實證分析，再次驗證本研究提出的模型優於依據價格預測值進行決策的模式。本文提出的理論模型有著線性規劃的高運算效率，並可用在多種須考量現貨和期貨價格的原物料採購情境，如金屬、穀物、原油、天然氣等，故同時具有實務價值。
For enterprises that need to continuously optimize profits, it is very important to optimize the procurement planning under the random fluctuations of raw material prices, especially in the management of their operational and financial performance. This decision problem is more complicated when raw materials can be purchased through the spot and futures markets. Generally speaking, enterprises can adopt a strategy of predicting price trends in advance based on historical prices and then formulating procurement plans for a period of time in the future based on the predicted values. Different from the traditional thinking of making predictions before making decisions, this study proposes a new dynamic procurement optimization model, which uses the price data of spot and futures from the perspective of machine learning to estimate the optimal procurement volume instead of price prediction. The simulation results show that the procurement decision of this dynamic procurement model is significantly better than the procurement decision based on price forecasts. In addition to the simulation experiments, we use the spot and futures prices of the Brent crude oil in the past two years to conduct empirical analysis and do verify that the model proposed in this study is superior to the decision-making model based on price forecasts. The theoretical model proposed in this paper has high computational efficiency of linear programming and can be used in a variety of raw material procurement scenarios where spot and futures prices must be considered, such as metals, grains, crude oil, natural gas, etc. Thus, it has a practical value at the same time.參考文獻 Beutel, A. L., & Minner, S. (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics, 140(2), 637-645.Geman, H. (2005). Energy commodity prices: Is mean-reversion dead?. The Journal of Alternative Investments, 8(2), 31-45.Geman, H., & Nguyen, V. N. (2005). Soybean inventory and forward curve dynamics. Management Science, 51(7), 1076-1091.Goel, A., & Gutierrez, G. J. (2011). Multiechelon procurement and distribution policies for traded commodities. Management Science, 57(12), 2228-2244.Mandl, C., & Minner, S. (2020). Data-driven optimization for commodity procurement under price uncertainty. Manufacturing & Service Operations Management.Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging. The Journal of finance, 52(3), 923-973.Secomandi, N., & Kekre, S. (2014). Optimal energy procurement in spot and forward markets. Manufacturing & Service Operations Management, 16(2), 270-282.Shrestha, G. B., Pokharel, B. K., Lie, T. T., & Fleten, S. E. (2008). Management of price uncertainty in short-term generation planning. IET generation, transmission & distribution, 2(4), 491-504.Thakkar, A., & Chaudhari, K. (2021). Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Information Fusion, 65, 95-107. 描述 碩士
國立政治大學
資訊管理學系
109356029資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356029 資料類型 thesis dc.contributor.advisor 莊皓鈞 zh_TW dc.contributor.advisor Chuang, Hao-Chun en_US dc.contributor.author (作者) 薛名皓 zh_TW dc.contributor.author (作者) Hsueh, Ming-Hao en_US dc.creator (作者) 薛名皓 zh_TW dc.creator (作者) Hsueh, Ming-Hao en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-五月-2022 15:00:56 (UTC+8) - dc.date.available 2-五月-2022 15:00:56 (UTC+8) - dc.date.issued (上傳時間) 2-五月-2022 15:00:56 (UTC+8) - dc.identifier (其他 識別碼) G0109356029 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139985 - 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) 109356029 zh_TW dc.description.abstract (摘要) 對於需要持續優化獲利的企業而言，能否在原物料價格隨機變動下進行成本最佳化的採購規劃，對其營運和財務績效的管理甚為重要。此決策問題在原物料有現貨和期貨可供選擇時又更為複雜。一般而言，企業可以採用以歷史價格先行預測價格走勢，並依據預測值訂定未來一段期間採購計畫的策略。有別於此種先預測後決策的傳統思維，本研究提出一個新的動態採購最佳化模型，從機器學習觀點運用現貨和期貨的價格數據，以預估最佳採購量而非價格預測為訓練目標，進而求得各個輸入特徵的最佳係數解。模擬分析結果顯示，此動態採購模型的成本表現顯著優於依據價格預測值所做的採購決策。除了模擬實驗，我們使用近兩年的布蘭特原油現貨與期貨價格進行實證分析，再次驗證本研究提出的模型優於依據價格預測值進行決策的模式。本文提出的理論模型有著線性規劃的高運算效率，並可用在多種須考量現貨和期貨價格的原物料採購情境，如金屬、穀物、原油、天然氣等，故同時具有實務價值。 zh_TW dc.description.abstract (摘要) For enterprises that need to continuously optimize profits, it is very important to optimize the procurement planning under the random fluctuations of raw material prices, especially in the management of their operational and financial performance. This decision problem is more complicated when raw materials can be purchased through the spot and futures markets. Generally speaking, enterprises can adopt a strategy of predicting price trends in advance based on historical prices and then formulating procurement plans for a period of time in the future based on the predicted values. Different from the traditional thinking of making predictions before making decisions, this study proposes a new dynamic procurement optimization model, which uses the price data of spot and futures from the perspective of machine learning to estimate the optimal procurement volume instead of price prediction. The simulation results show that the procurement decision of this dynamic procurement model is significantly better than the procurement decision based on price forecasts. In addition to the simulation experiments, we use the spot and futures prices of the Brent crude oil in the past two years to conduct empirical analysis and do verify that the model proposed in this study is superior to the decision-making model based on price forecasts. The theoretical model proposed in this paper has high computational efficiency of linear programming and can be used in a variety of raw material procurement scenarios where spot and futures prices must be considered, such as metals, grains, crude oil, natural gas, etc. Thus, it has a practical value at the same time. en_US dc.description.tableofcontents 第一章 緒論 1第二章 文獻探討 4第三章 研究模型 7一、市場價格 7二、外生變數 8三、庫存成本 8四、採購決策 9第四章 模擬過程及結果分析 14第一節 模擬數值設定 14一、價格公式說明 14二、參數設定 15三、訓練與測試 16四、資料集選取 17五、模型比較基準 18第二節 數值分析 19一、比較模型一與模型二：僅參考當期報價v.s.多參考過往報價 20二、比較模型一在不同訓練/測試長度下的表現差異 21三、比較模型一與模型三：不納入外生變數 v.s. 納入外生變數 22四、比較價格預測模型與本文採購模型的採購處方誤差 23第五章 實證資料驗證 27第六章 結論與建議 31參考文獻 33 zh_TW dc.format.extent 2171688 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356029 en_US dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 數據分析 zh_TW dc.subject (關鍵詞) 數值模擬 zh_TW dc.subject (關鍵詞) 動態採購 zh_TW dc.subject (關鍵詞) 原物料期貨 zh_TW dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Data analysis en_US dc.subject (關鍵詞) Numerical simulation en_US dc.subject (關鍵詞) Dynamic procurement en_US dc.subject (關鍵詞) Raw material futures en_US dc.title (題名) 機器學習為基礎的現貨與期貨動態採購模型 zh_TW dc.title (題名) A Machine Learning-Based Dynamic Purchasing Model of Spot and Futures en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Beutel, A. L., & Minner, S. (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics, 140(2), 637-645.Geman, H. (2005). Energy commodity prices: Is mean-reversion dead?. The Journal of Alternative Investments, 8(2), 31-45.Geman, H., & Nguyen, V. N. (2005). Soybean inventory and forward curve dynamics. Management Science, 51(7), 1076-1091.Goel, A., & Gutierrez, G. J. (2011). Multiechelon procurement and distribution policies for traded commodities. Management Science, 57(12), 2228-2244.Mandl, C., & Minner, S. (2020). Data-driven optimization for commodity procurement under price uncertainty. Manufacturing & Service Operations Management.Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging. The Journal of finance, 52(3), 923-973.Secomandi, N., & Kekre, S. (2014). Optimal energy procurement in spot and forward markets. Manufacturing & Service Operations Management, 16(2), 270-282.Shrestha, G. B., Pokharel, B. K., Lie, T. T., & Fleten, S. E. (2008). Management of price uncertainty in short-term generation planning. IET generation, transmission & distribution, 2(4), 491-504.Thakkar, A., & Chaudhari, K. (2021). Fusion in stock market prediction: a decade survey on the necessity, recent developments, and potential future directions. Information Fusion, 65, 95-107. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200397 en_US
