應用機器學習於數位貨幣社群網路之情緒分析:以比特幣為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：7

、訪客IP：3.131.110.169

姓名

李柏維(Po-Wei Lee) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

應用機器學習於數位貨幣社群網路之情緒分析:以比特幣為例
(Applying machine learning in sentiment analysis of digital currency online posts:a case study of Bitcoin)

相關論文

★ 本國壽險業內部管理制度與經營績效之探討	★ 企業購併宣告對股東財富效果之影響-以台灣金融業為例
★ 金融科技對台灣金融業之影響-以線上貸款為例	★ 銀行授信違約風險-財務比率評分之預警效果
★ 所得稅制優化修正與兩稅合一廢除之影響	★ 區塊鏈技術應用於金融科技公司企業資金電子調撥系統實作與驗證
★ 房屋貸款者風險特性與個人星座關係之研析-以個案銀行分行房貸業務為例	★ 金融科技浪潮下銀行分行經營績效之探討－以個案銀行為例
★ 中國A股正式納入MSCI指數之異常報酬探討	★ 企業信用管理政策之個案研究
★ 生技公司與醫美診所聯合經營商業模式探討與財務分析－以某公開發行公司為例	★ 公司併購之經營績效分析－以某併購案為例
★ 結構型商品投資個案探討-股價(權)連結型/基金連結型	★ 越南台商融資模式之研究
★ 低利環境利差縮減下銀行分行經營效率之探討-以個案銀行為例	★ 經濟供需模型評價死亡率債券

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 (2024-7-1以後開放)

摘要(中)

本文探究投資人情緒是否會影響比特幣交易行為、以及比特幣日報酬及日報酬波動(7日、30日)會受到哪些因素影響。因此，本文延用了Antweiler and Frank (2004)、Cookson and Niessner (2020)所使用的投資人看漲訊號指標(Bullishness Signal, BS)與投資人看法分歧度指標(Agreement Index, AI)，透過2013年10月至2020年12月共53,415筆Bitcointalk論壇文章，以Python的VADER套件為工具做文字分析，並運用機器學習LSTM模型建構了共四組情緒指標，根據文獻納入區塊鏈資訊、總體經濟、全球貨幣匯率、情緒指標共28個變數建構模型，探究變數對比特幣日報酬及日報酬波動(7日、30日)之間的關聯性。
實證結果發現，區塊鏈資訊、總體經濟、全球貨幣匯率加上四組情緒指標共28個變數中，共計5個變數為比特幣日報酬預測之重要變數，分別為比特幣算力(BitcoinHashRate)、黃金日報酬(Gold)、恐慌指數(VIX)、英鎊匯率變化率(GBP)、人民幣匯率變化率(CNY)。共計9個變數為比特幣日報酬波動(7日、30日) 預測之重要變數，分別為上海證交所綜合股價指數變化率(SSE)與四組情緒指標(投資人看漲訊號、投資人看法分歧度指標為一組，共計四種建立方式)。
在建構情緒指標方面，本文運用機器學習LSTM模型，訓練模型的準確性達到95.19%，驗證模型的準確性達到91.13%，並將模型建立的四組情緒指標作為解釋變數探究對比特幣日報酬、日報酬波動預測性。由實證結果發現，本文透過投資人論壇文章建構的情緒指標能夠有效地預測比特幣日報酬波動。

摘要(英)

This study is intended to explores whether investor sentiment would affect bitcoin trading behavior, and what factors would affect bitcoin daily return and daily return volatility. Therefore, this study cites two sentiment factors used by Antweiler and Frank (2004) and Cookson and Niessner (2020): Bullishness Signal (BS) and Agreement Index (AI). The factors construction process uses the VADER package in Python as a text analysis tool, and uses the Long Short-Term Memory(LSTM) model to construct a total of four sets of sentiment factors. The sample data is a total of 53,415 Bitcointalk forum articles from October 2013 to December 2020. According to the literature, we included 28 variables of blockchain information, macro economic development, global currency exchange rates, and sentiment factors to construct a regression model.
The empirical results found that out of 28 variables including blockchain information, macro economic development, global currency exchange rates, and sentiment factors, a total of 5 variables are important variables for Bitcoin daily return prediction, including BitcoinHashRate. , Golden Daily Return (Gold), VIX, British Pound Exchange Rate (GBP), RMB Exchange Rate (CNY). A total of 9 variables are important variables for Bitcoin daily return volatility prediction, including the Shanghai Stock Exchange Composite Index return (SSE) and four sets of sentiment factors.
In terms of constructing sentiment factors, this study uses machine learning LSTM model. The accuracy of the training model reached 95.19%. The empirical results found that the sentiment fators constructed in this study through Bitcointalk forum articles can effectively predict the daily return volatility of Bitcoin.

關鍵字(中)

★ 數位貨幣
★ 比特幣
★ 機器學習
★ 情緒指標

關鍵字(英)

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
第二章文獻回顧 3
2-1 比特幣屬性相關研究 3
2-2 投資人情緒對資產價格影響研究 4
2-3 機器學習應用議題研究 5
第三章資料 8
3-1 資料來源 8
3-2 樣本選擇 8
第四章研究方法 10
4-1 變數說明 10
4-1-1 應變數 10
4-1-2 自變數 10
4-2 模型介紹 16
第五章研究結果 19
5-1 敘述統計與Pearson相關係數 19
5-2 迴歸結果與分析 20
第六章結論 23
參考文獻 43
附錄 45
附錄 1 VADER應用於Bitcointalk文章範例 45
附錄 2 LSTM模型流程架構 47
附錄 3 迴歸結果：總體經濟(2) 51
附錄 4 迴歸結果：全球貨幣匯率(2) 53

參考文獻

Alessandretti, L., ElBahrawy, A., Aiello, L. M., & Baronchelli, A., 2018, Anticipating cryptocurrency prices using machine learning. Complexity, 2018.
Antweiler, W., & Frank, M. Z., 2004, Is all that talk just noise? The information content of internet stock message boards. The Journal of finance, 59(3), 1259-1294.
Chen, C., Liu, L., & Zhao, N. , 2020, Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19. Emerging Markets Finance and Trade, 56(10), 2298-2309.
Ciaian, P., Rajcaniova, M., & Kancs, d. A., 2016, The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
Colianni, S., Rosales, S., & Signorotti, M., 2015, Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. CS229 Project, 1-5.
Cookson, J. A., & Niessner, M., 2020, Why don′t we agree? Evidence from a social network of investors. The Journal of finance, 75(1), 173-228.
Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M., 2014, Bitcoin-asset or currency? revealing users′ hidden intentions. Revealing Users′ Hidden Intentions (April 15, 2014). ECIS.
Ho, C., & Hung, C.-H., 2009, Investor sentiment as conditioning information in asset pricing. Journal of Banking & Finance, 33(5), 892-903.
Hochreiter, S., & Schmidhuber, J., 1997, Long short-term memory. Neural computation, 9(8), 1735-1780.
Hutto, C., & Gilbert, E., 2014, Vader: A parsimonious rule-based model for sentiment analysis of social media text. Paper presented at the Proceedings of the International AAAI Conference on Web and Social Media.
Jang, H., & Lee, J., 2017, An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. Ieee Access, 6, 5427-5437.
Jubinski, D., & Lipton, A. F., 2013, VIX, gold, silver, and oil: how do commodities react to financial market volatility? Journal of Accounting and Finance, 13(1), 70-88.
Kadilli, A., 2015, Predictability of stock returns of financial companies and the role of investor sentiment: A multi-country analysis. Journal of Financial Stability, 21, 26-45.
Kaminski, J., 2014, Nowcasting the bitcoin market with twitter signals. arXiv preprint arXiv:1406.7577.
Karalevicius, V., Degrande, N., & De Weerdt, J., 2018, Using sentiment analysis to predict interday Bitcoin price movements. The Journal of Risk Finance.
Latif, M., Arshad, S., Fatima, M., & Farooq, S., 2011, Market efficiency, market anomalies, causes, evidences, and some behavioral aspects of market anomalies. Research journal of finance and accounting, 2(9), 1-13.
Loughran, T., & McDonald, B., 2011, When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of finance, 66(1), 35-65.
Matta, M., Lunesu, I., & Marchesi, M., 2015, Bitcoin Spread Prediction Using Social and Web Search Media. Paper presented at the UMAP workshops.
Mattke, J., Maier, C., Reis, L., & Weitzel, T., 2021, Bitcoin investment: a mixed methods study of investment motivations. European Journal of Information Systems, 30(3), 261-285.
McGinnis, J. O., & Roche, K., 2019, Bitcoin: Order without law in the digital age. Ind. LJ, 94, 1497.
Nakamoto, S., 2019, Bitcoin: A peer-to-peer electronic cash system.
Patel, J., Shah, S., Thakkar, P., & Kotecha, K., 2015, Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268.
Philippas, D., Rjiba, H., Guesmi, K., & Goutte, S., 2019, Media attention and Bitcoin prices. Finance Research Letters, 30, 37-43.
Shah, V. H., 2007, Machine learning techniques for stock prediction. Foundations of Machine Learning| Spring, 1(1), 6-12.
Shahzad, S. J. H., Bouri, E., Roubaud, D., Kristoufek, L., & Lucey, B., 2019, Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322-330.
Stenqvist, E., & Lönnö, J., 2017, Predicting Bitcoin price fluctuation with Twitter sentiment analysis.
Taleb, N., 2019, Prospective applications of blockchain and bitcoin cryptocurrency technology. TEM Journal, 8(1), 48-55.
Van Wijk, D., 2013, What can be expected from the BitCoin. Erasmus Universiteit Rotterdam, 18.
Yermack, D., 2015, Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency, 31-43

指導教授

黃泓人

審核日期

2021-8-13

推文