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  • 學位論文

由區塊鏈資料探討比特幣特性

An Investigation of Bitcoin Transaction Records

指導教授 : 盧信銘

摘要


比特幣為一個對等式架構的數位貨幣系統,其背後的核心技術為區塊鏈,而區塊鏈記載了比特幣的所有歷史交易紀錄,並且公開分散放置在網路當中。因此,我們將針對區塊鏈中的交易記錄進行分析,並以三個不同的觀點來探討比特幣的特性,包含了時間、貨幣性質以及比特幣投資者的活動。首先,我們以時間維度作為出發點,觀察比特幣從2009年至2016年間,其整體環境的成長變化,發現比特幣的區塊、交易以及地址數量皆呈現指數成長的趨勢。接下來,以貨幣角度來看,我們定義了比特幣的貨幣流通速度以及吉尼係數的計算方式,並以這兩個指標來與真實貨幣相比較。最後,則是以比特幣的投資活動來看,我們利用Cox風險比例模型及羅吉斯回歸檢驗比特幣投資者是否存在正向回饋循環的現象。我們從研究結果中發現,比特幣正以指數遞增的方式快速地成長,並且有越來越多人在使用比特幣。即便如此,我們從比特幣的使用情形來看,認為比特幣的特質仍與現實世界的貨幣具有相當的差距。而若將比特幣視為一項投資工具,結果顯示比特幣的投資者會因爲前次投資的正向報酬,而促使其進行下一次的投資行為,間接顯示比特幣投資市場具有泡沫化的可能。透過直接對比特幣的交易記錄進行分析,有助於從中發現比特幣潛在的特性,並能快速的掌握比特幣的變化趨勢。

關鍵字

比特幣 區塊鏈 數位貨幣 貨幣 投資工具

並列摘要


Bitcoin is a peer-to-peer digital currency system in which all transactions are recorded in a public ledger called blockchain. We analyze the blockchain transaction data to characterize the features of Bitcoin. Specifically, we investigates Bitcoin from three distinct perspectives: time, currency, and individual investment behavior. Our analysis are based on historical transaction records from 2009 to 2016. First, we visualize the changes of Bitcoin in the time dimension. Second, we calculated the velocity of Bitcoin and the Gini coefficient for investigating the characteristics of Bitcoin as a pseudo-currency. Finally, we use Cox regression and Logistic regression to understand individual investment behavior. We found that Bitcoin is growing very rapidly in terms of transaction numbers and address. Even so, the characteristics of Bitcoin are very different from the real-world currency. By analyzing investor behavior, we found that Bitcoin has the possibility to form a bubble. In conclusion, we further explored and found the characteristics of Bitcoin using blockchain data.

參考文獻


Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network. PLoS ONE, 9(5), e97205.
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National Institute of Standards and Technology (NIST). (2002). SHA256 Standard. (FIPS PUB 180-2). Retrieved from http://csrc.nist.gov/publications/fips/fips180-2/fips180-2withchangenotice.pdf.
Pearson, N. D., Yang, Z., & Zhang, Q. (2016). Evidence about Bubble Mechanisms: Precipitating Event, Feedback Trading, and Social Contagion. Proceedings of the 7th Miami Behavioral Finance Conference. Miami, Florida, USA.
Pham, P. T., & Lee, S. (2016). Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods. Retrieved from arXiv:1611.03941.

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