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研究生: 黃明源
Huang, Ming-Yuan
論文名稱: 統計與機器學習方法的資產價格預測—以比特幣為例
Asset Price Forecasting with Statistical and Machine Learning Methods-The case study of Bitcoin
指導教授: 何宗武
Ho, Tsung-Wu
口試委員: 施人英
Shih, Jen-Ying
楊浩彥
Yang, Hao-Yen
何宗武
Ho, Tsung-Wu
口試日期: 2023/04/27
學位類別: 碩士
Master
系所名稱: 全球經營與策略研究所
Graduate Institute of Global Business and Strategy
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 66
中文關鍵詞: 比特幣格蘭傑因果相關檢驗VARARIMASVMGBMENET組合預測法
英文關鍵詞: Bitcoin, Granger Causality, VAR, ARIMA, SVM, GBM, ENET, Combination Forecast
DOI URL: http://doi.org/10.6345/NTNU202301224
論文種類: 學術論文
相關次數: 點閱:111下載:19
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  • 資產價格的預測性在市場效率假說提出後便受到眾多討論,至今亦有許多研究係利用此概念在不同的金融資產上進行實證分析。隨著近年加密貨幣的快速發展,其價格劇烈的增長吸引許多投機客進入該市場,而使得發展及波動更加劇烈,在市值大幅成長的同時也引來學者的警告,諾貝爾經濟學獎得主Joseph Stiglitz 與Robert Shiller便稱其為「危險的投機泡沫」。本研究同時利用傳統時間序列模型與機器學習模型,對比特幣報酬與價格進行預測並比較預測表現。我們發現機器學習模型對報酬進行預測,有較小的預測誤差,表現優於傳統時間序列模型,然而透過繪圖則顯現出其較佳的預測效果係來自較低幅度的波動,實際上預測表現並不理想。而後利用組合預測法改善,根據Diebold-Mariano 檢定的結果,雖然比起單一傳統時間序列模型來得要好,卻也是源自低幅度的波動所致。基於上述結果,我們認為比特幣的價格或報酬的可預測性較低。

    The predictability of asset prices has been widely discussed since the introduction of the market efficiency hypothesis and has been applied to various financial assets. With the rapid development of cryptocurrencies in recent years, their volatile price growth has attracted many speculators to enter the market, leading to even more intense development and fluctuations. As a result, scholars have issued warnings, with Nobel laureates in economics Joseph Stiglitz and Robert Shiller referring to it as a "dangerous speculative bubble". This study simultaneously employs both traditional time series models and machine learning models to predict the returns and prices of Bitcoin, comparing their predictive performances. We found that machine learning models have smaller prediction errors than traditional time series models in predicting returns. However, visualizations show that superior predictive performance of machine learning models is due to lower magnitude volatility. Based on the results, we conclude that predicting prices or returns of Bitcoin is much infeasible.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 2 第三節 研究架構 3 第二章 文獻回顧 4 第一節 資產價格預測與預測能力 4 第二節 預測模型與方法 5 第三章 研究方法 7 第一節 研究架構 7 第二節 變數選擇與篩選方法 8 第三節 時間序列預測架構 10 第四節 傳統時間序列模型 12 第五節 機器學習模型 14 第六節 單一模型架構 17 第七節 組合預測法18 第八節 評估指標 18 第四章 實證結果 21 第二節 資料來源與比特幣敘述統計 21 第二節 格蘭傑因果檢驗 22 第三節 單一模型預測表現 24 第四節 組合預測法預測表現 31 第五章 結論 33 第一節 研究結果 33 第二節 研究貢獻 34 第三節 研究限制與未來改進方向 35 參考資料 36 附錄 40 附錄一 選用變數與敘述統計 40 附錄二 參數設置 53 附錄三 各組合預測方法 54 附錄四 全部組合預測結果 56

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