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.

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > Electronic Thesis & Dissertation > Item 987654321/95498

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/95498

Title:	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.
Authors:	蔡秉宏;Tsai, Ping-Hung
Contributors:	資訊管理學系
Keywords:	比特幣;鏈上資料;技術分析;個人交易行為;Bitcoin;On-chain Data;Technical Analysis;Individual Trading Behavior
Date:	2024-07-15
Issue Date:	2024-10-09 16:54:26 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。 ;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.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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