運用鏈上資料分析個人投資行為：以比特幣為例

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姓名	蔡秉宏(Ping-Hung Tsai) 查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	運用鏈上資料分析個人投資行為：以比特幣為例 (Analyzing Individual Investment Behavior Through On-Chain Data: A Focus on Bitcoin.)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 (2028-7-1以後開放)
摘要(中)	加密貨幣市場在近年來逐漸受到傳統金融機構的注意，然而作為新興市場，其高波動性的特性使得其風險比傳統金融市場還要更大，而了解其市場行為，建構有效的交易策略有助於投資人減少加密貨幣市場風險。有別於過去傳統金融市場，加密貨幣運用區塊鏈作為其底層技術，而區塊鏈的分散式帳本的特性，能使區塊鏈上所有的交易資料都公開透明，這些資料被稱之為鏈上資料(On-chain Data)，這讓本研究能透過這些資料分析個人的投資行為，從而幫助投資人更加了解加密貨幣市場。本研究將開發一套分析程序，以RSI技術指標為例，研究技術分析是如何影響到加密貨幣市場上的用戶，以加密貨幣市場市值最大的比特幣作為研究對象，透算鏈上數據研究不同用戶的所有交易計算出報酬率，之後再實測不同技術分析參數對其做多元迴歸分析，得到最適合比特幣市場的交易規則。研究結果表明，加密貨幣市場比起時間間隔為一天的價格資料更適用於四小時的價格資料，同時時間週期介於12到24筆價格的技術指標，對用戶的獲利影響最大，這套分析程序在未來也能運用在不同的技術指標與不同的市場標的，提供研究人員更好解讀鏈上資料。
摘要(英)	In recent years, the cryptocurrency market has attracted increasing attention from traditional financial institutions. However, due to its high volatility, the risks are greater than those in traditional financial markets. Understanding market behavior and developing effective trading strategies can help investors mitigate these risks. Unlike traditional markets, cryptocurrencies use blockchain technology, which features a decentralized ledger that makes all transaction data publicly transparent. These on-chain data allow this study to analyze individual investment behaviors, helping investors better understand the cryptocurrency market. This study develops an analytical program using the RSI (Relative Strength Index) technical indicator to examine its impact on users in the cryptocurrency market. Focusing on Bitcoin, the cryptocurrency with the largest market capitalization, the study calculates returns by analyzing on-chain data of different users′ transactions. Subsequently, multiple regression analyses are conducted on different technical analysis parameters to derive optimal trading rules for the Bitcoin market. The results indicate that the cryptocurrency market is better suited to price data with a four-hour interval compared to daily price data. Additionally, technical indicators based on 12 to 24 periods have the most significant impact on user profitability. This analytical program can also be applied to different technical indicators and various market targets, providing researchers with better interpretations of on-chain data.
關鍵字(中)	★ 比特幣 ★ 鏈上資料 ★ 技術分析 ★ 個人交易行為	關鍵字(英)	★ Bitcoin ★ On-chain Data ★ Technical Analysis ★ Individual Trading Behavior
論文目次	第一章緒論 1 1-1研究動機 1 1-2研究目的 2 1-3研究重要性 2 1-4論文架構 3 第二章文獻探討 4 2-1區塊鏈交易原理與資料儲存方式 4 2-2趨勢追蹤策略與逆勢策略與羊群效應對其之影響 5 2-3技術分析於加密貨幣之應用 7 第三章理論基礎與假說 9 第四章分析流程 11 4-1資料搜集與抽樣 11 4-1-1鏈上資料搜集 11 4-1-2價格資料搜集 12 4-1-3抽樣方法 13 4-2RSI計算與參數選擇 14 4-2-1指標計算 14 4-2-2超買區與超賣區設定 15 4-3用戶獲利計算 17 4-4模型建構 18 第五章分析結果 22 5-1敘述統計 22 5-2回歸模型結果 27 5-2-1模型一 27 5-2-2模型二 29 5-2-3模型二之對立模型 32 第六章結論 35 6-1研究結果 35 6-2學術意涵 36 6-3實務建議 38 6-4研究限制與未來研究建議 38 參考文獻 40 附錄一迴歸原始資料 42 模型一 42 模型二 44 對立模型 49
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指導教授	蘇雅惠尚榮安(Yea-Huey Su Rong-An Shang)	審核日期	2024-7-15
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