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題名 比特幣鏈上行為與市場價格關聯性之探討
An Investigation of the Relationship Between Bitcoin On-Chain Activity and Market Price
作者 文詳惠
Wen, Xiang-Hui
貢獻者 岳夢蘭
Yueh, Meng-Lan
文詳惠
Wen, Xiang-Hui
關鍵詞 比特幣
區塊鏈上資料
Bitcoin
On-chain Data
日期 2025
上傳時間 1-Sep-2025 16:36:21 (UTC+8)
摘要 相較傳統金融市場資訊封閉的特性，區塊鏈具備高度透明與可追溯性，使研究者得以直接觀察特定地址的資金進出與使用模式。本文聚焦於「重複使用地址」的鏈上行為，透過 Google BigQuery 提供之比特幣區塊鏈資料，建構 2018–2024 年間地址活動的量化指標，進一步探討其與市場變數之關聯性。研究首先依據交易次數與最大資產規模篩選出具代表性的重複使用地址，並針對其資產分布、交易頻率、餘額歸零次數與首次歸零前活躍天數等特徵進行敘述統計分析。結果顯示，大多數地址具短期使用傾向，呈現高度右偏的長尾分布結構，僅少數地址呈現長期反覆使用行為，顯示鏈上投資行為的明顯異質性。本研究進一步分析比特幣價格報酬、美股報酬、政策不確定性指數（UNC）、市場波動率（VIX）與 Google Trends 熱度等市場指標，這些變數對新生地址與結束地址數量之解釋力。實證結果顯示，比特幣報酬為最主要且穩定的影響變數，對於地址數的變動幅度皆具顯著正向影響，而市場波動率的上升對鏈上轉出行為也有顯著正向影響。
Compared to the relatively opaque nature of traditional financial markets, blockchain offers high transparency and traceability, allowing researchers to directly observe fund movements and usage patterns of specific addresses. This study focuses on the on-chain behavior of "reused addresses" and utilizes Bitcoin blockchain data provided by Google BigQuery to construct quantitative indicators of address activity from 2018 to 2024. Representative reused addresses are identified based on transaction frequency and maximum asset value, with descriptive statistical analyses conducted on their asset distribution, transaction frequency, number of times the balance returns to zero, and the number of active days before the first zero balance. Results reveal that most addresses exhibit short-term usage patterns and follow a highly right-skewed, long-tailed distribution, while only a minority demonstrate repeated long-term usage, indicating significant heterogeneity in on-chain investment behavior. This study further examines the explanatory power of various market indicators—including Bitcoin returns, U.S. stock returns, the Economic Policy Uncertainty Index (UNC), market volatility (VIX), and Google Trends popularity—on the number of newly created and terminated addresses. Empirical results show that Bitcoin returns are the most significant and stable driving factor, with a strong positive effect on address activity. In addition, rising market volatility is also found to significantly influence address termination behavior on theblockchain.
參考文獻 Aalborg, H. A., Molnár, P., & de Vries, J. E. (2019). What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters, 29, 255–265. Antulov-Fantulin, N., Tolic, D., Piskorec, M., Zhang, C., & Vodenska, I. (2018). Inferring short-term volatility indicators from Bitcoin blockchain [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1809.07856 Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions & Money, 54, 177 189. Guo, W., Jahanshahloo, H., Spokeviciute, L., & Wang, Q. (2025). The dual impact of on-chain and off-chain factors on Bitcoin market efficiency. The British Accounting Review, 101641. Hau, L., Zhu, H., Shahbaz, M., & Sun, W. (2021). Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis. The North American Journal of Economics and Finance, 55, 101297. Koutmos, D. (2018). Exogenous drivers of Bitcoin and cryptocurrency volatility: A heterogeneous autoregressive jump model approach. Journal of International Financial Markets, Institutions and Money, 56, 62–74. Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE, 10(4), e0123923. Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., & Savage, S. (2013). A fistful of bitcoins: Characterizing payments among men with no names. In Proceedings of the 2013 ACM Internet Measurement Conference (pp. 127–140). ACM. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system [White paper]. https://bitcoin.org/bitcoin.pdf Schär, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 103(2), 153–174. Yu, H., Sun, Y., Liu, Y., & Zhang, L. (2023). Bitcoin Gold, Litecoin Silver: An introduction to cryptocurrency’s valuation and trading strategy [Preprint]. arXiv. https://arxiv.org/abs/2308.00013 Zhang, Y., Wang, Y., & Liu, J. (2025). Bitcoin price direction prediction using on-chain data and feature selection. Digital Finance, 6(1), 45–62.
描述 碩士
國立政治大學
財務管理學系
112357005
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112357005
資料類型 thesis
dc.contributor.advisor 岳夢蘭zh_TW
dc.contributor.advisor Yueh, Meng-Lanen_US
dc.contributor.author (Authors) 文詳惠zh_TW
dc.contributor.author (Authors) Wen, Xiang-Huien_US
dc.creator (作者) 文詳惠zh_TW
dc.creator (作者) Wen, Xiang-Huien_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 16:36:21 (UTC+8)-
dc.date.available 1-Sep-2025 16:36:21 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 16:36:21 (UTC+8)-
dc.identifier (Other Identifiers) G0112357005en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159338-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理學系zh_TW
dc.description (描述) 112357005zh_TW
dc.description.abstract (摘要) 相較傳統金融市場資訊封閉的特性，區塊鏈具備高度透明與可追溯性，使研究者得以直接觀察特定地址的資金進出與使用模式。本文聚焦於「重複使用地址」的鏈上行為，透過 Google BigQuery 提供之比特幣區塊鏈資料，建構 2018–2024 年間地址活動的量化指標，進一步探討其與市場變數之關聯性。研究首先依據交易次數與最大資產規模篩選出具代表性的重複使用地址，並針對其資產分布、交易頻率、餘額歸零次數與首次歸零前活躍天數等特徵進行敘述統計分析。結果顯示，大多數地址具短期使用傾向，呈現高度右偏的長尾分布結構，僅少數地址呈現長期反覆使用行為，顯示鏈上投資行為的明顯異質性。本研究進一步分析比特幣價格報酬、美股報酬、政策不確定性指數（UNC）、市場波動率（VIX）與 Google Trends 熱度等市場指標，這些變數對新生地址與結束地址數量之解釋力。實證結果顯示，比特幣報酬為最主要且穩定的影響變數，對於地址數的變動幅度皆具顯著正向影響，而市場波動率的上升對鏈上轉出行為也有顯著正向影響。zh_TW
dc.description.abstract (摘要) Compared to the relatively opaque nature of traditional financial markets, blockchain offers high transparency and traceability, allowing researchers to directly observe fund movements and usage patterns of specific addresses. This study focuses on the on-chain behavior of "reused addresses" and utilizes Bitcoin blockchain data provided by Google BigQuery to construct quantitative indicators of address activity from 2018 to 2024. Representative reused addresses are identified based on transaction frequency and maximum asset value, with descriptive statistical analyses conducted on their asset distribution, transaction frequency, number of times the balance returns to zero, and the number of active days before the first zero balance. Results reveal that most addresses exhibit short-term usage patterns and follow a highly right-skewed, long-tailed distribution, while only a minority demonstrate repeated long-term usage, indicating significant heterogeneity in on-chain investment behavior. This study further examines the explanatory power of various market indicators—including Bitcoin returns, U.S. stock returns, the Economic Policy Uncertainty Index (UNC), market volatility (VIX), and Google Trends popularity—on the number of newly created and terminated addresses. Empirical results show that Bitcoin returns are the most significant and stable driving factor, with a strong positive effect on address activity. In addition, rising market volatility is also found to significantly influence address termination behavior on theblockchain.en_US
dc.description.tableofcontents 第一章 緒論 ------1 第二章 文獻回顧 ------2 第三章 研究方法 ------4 第一節 樣本來源與資料篩選 ------4 第二節 變數定義與迴歸模型設定 ------5 第四章 敘述統計 ------6 第五章 實證分析與結果 ------11 第六章 結論與建議 ------26 參考文獻 ------28zh_TW
dc.format.extent 1135031 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112357005en_US
dc.subject (關鍵詞) 比特幣zh_TW
dc.subject (關鍵詞) 區塊鏈上資料zh_TW
dc.subject (關鍵詞) Bitcoinen_US
dc.subject (關鍵詞) On-chain Dataen_US
dc.title (題名) 比特幣鏈上行為與市場價格關聯性之探討zh_TW
dc.title (題名) An Investigation of the Relationship Between Bitcoin On-Chain Activity and Market Priceen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Aalborg, H. A., Molnár, P., & de Vries, J. E. (2019). What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters, 29, 255–265. Antulov-Fantulin, N., Tolic, D., Piskorec, M., Zhang, C., & Vodenska, I. (2018). Inferring short-term volatility indicators from Bitcoin blockchain [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1809.07856 Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions & Money, 54, 177 189. Guo, W., Jahanshahloo, H., Spokeviciute, L., & Wang, Q. (2025). The dual impact of on-chain and off-chain factors on Bitcoin market efficiency. The British Accounting Review, 101641. Hau, L., Zhu, H., Shahbaz, M., & Sun, W. (2021). Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis. The North American Journal of Economics and Finance, 55, 101297. Koutmos, D. (2018). Exogenous drivers of Bitcoin and cryptocurrency volatility: A heterogeneous autoregressive jump model approach. Journal of International Financial Markets, Institutions and Money, 56, 62–74. Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE, 10(4), e0123923. Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., & Savage, S. (2013). A fistful of bitcoins: Characterizing payments among men with no names. In Proceedings of the 2013 ACM Internet Measurement Conference (pp. 127–140). ACM. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system [White paper]. https://bitcoin.org/bitcoin.pdf Schär, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets. Federal Reserve Bank of St. Louis Review, 103(2), 153–174. Yu, H., Sun, Y., Liu, Y., & Zhang, L. (2023). Bitcoin Gold, Litecoin Silver: An introduction to cryptocurrency’s valuation and trading strategy [Preprint]. arXiv. https://arxiv.org/abs/2308.00013 Zhang, Y., Wang, Y., & Liu, J. (2025). Bitcoin price direction prediction using on-chain data and feature selection. Digital Finance, 6(1), 45–62.zh_TW