§ 瀏覽學位論文書目資料
  
系統識別號 U0002-2806202011243500
DOI 10.6846/TKU.2020.00808
論文名稱(中文) K線運用於比特幣交易的獲利性分析
論文名稱(英文) Profitability Analysis of Daytrading Bitcoin with Employing K Values
第三語言論文名稱
校院名稱 淡江大學
系所名稱(中文) 管理科學學系企業經營碩士在職專班
系所名稱(英文) Executive Master's Program of Business Administration (EMBA) in Management Sciences
外國學位學校名稱
外國學位學院名稱
外國學位研究所名稱
學年度 108
學期 2
出版年 109
研究生(中文) 林吟芝
研究生(英文) Yin- Tzu Lin
學號 707620141
學位類別 碩士
語言別 繁體中文
第二語言別
口試日期 2020-06-20
論文頁數 32頁
口試委員 指導教授 - 倪衍森
委員 - 黃寶玉
委員 - 曹銳勤
關鍵字(中) 比特幣
期貨交易
日內交易
投資策略
關鍵字(英) Bitcoin,
Futures trading
intraday trading,
investing strategies
第三語言關鍵字
學科別分類
中文摘要
本研究就以BitMex交易所作為研究對象,以BitMex交易所所提供至2018年日內交易資料來探討, 投資人是否在BTC期貨各種三紅K或三黑K介入,並分析上述這些現象出現介入後是否有獲利的契機。在本研究中當沖的介入策略有八種: 連續漲幅三紅與連續漲幅0.1%、0.2%、0.3%,分別在三紅K及三黑K。出場策略有停損不停利可分為二種:(-5%, ∞)與(-10%, ∞);停利不停損可分為二種:(-∞, 5%)與(-∞, 10%),共有32種策略組合的盈虧分析,並且有獲利空間。此外由於本研究的研究設計採用當日平倉策略,可防範在比特幣價格全球市場二十四小時急漲急跌下的持倉風險。而結果顯示某些組合是有獲利的可能性,發現採取連續跌幅0.3%的三黑K停利不停損(-∞, 10%),獲利報酬是最高的。但如考量交易次數過少,亦可考量於停
利不停損平倉策略下三黑K(-∞, 5%)及三紅K介入 (-∞, 10%)參數加大籌碼進行交易。因此在不同狀況下所應採行的介入交易策略或平倉策略亦應有所不同策略,需要審慎的觀察以及數據判斷分析,才有較大的機會在期貨市場上獲取利潤。
英文摘要
Abstract:
This study uses the BitMex exchange as the research object, using the intraday trading data provided by the BitMex exchange to 2018 to discuss whether investors have intervened in various three red K or three black K in BTC futures, and analyzed the above phenomena after intervention Is there any opportunity for profit? In this study, there are eight intervention strategies of Dang Chong: three consecutive gains and three consecutive gains of 0.1%, 0.2%, and 0.3%, respectively in three red K and three black K. There are two types of stop-loss and stop-loss in the exit strategy: (-5%, ∞) and (-10%, ∞); stop-loss and stop loss can be divided into two types: (-∞, 5%) and ( -∞, 10%), a total of 32 strategy combinations of profit and loss analysis, and there is room for profit. In addition, because the research design of this study adopts the same day closing strategy, it can prevent the risk of holding positions in the global market of Bitcoin prices rising rapidly and falling in 24 hours. The results show that some combinations are likely to be profitable, and it is found that with a continuous decline of 0.3%, the three black K stop profits and stop losses (-∞, 10%), the highest profit return. However, if the number of transactions is too small, Eco considers the three black K (-∞, 5%) and three red K intervention (-∞, 10%) parameters under the stop-loss and stop-loss closing strategy to increase the bargaining chips for trading. Therefore, intervention strategies or position closing strategies that should be adopted in different situations should also have different strategies. Careful observation and data judgment and analysis are required to have a greater chance of obtaining profits in the futures market.
第三語言摘要
論文目次
中文摘要	I
英文摘要	II
目  錄	III
圖目錄	IV
表目錄	V
第一章	緒論	1
第一節	研究背景與動機	1 
第二節	研究目的	2
第三節	研究流程	4
第四節	研究架構	5
第二章	文獻探討	6
第一節	比特幣介紹	6
第二節	日內交易	9
第三節	期貨交易	11
第四節	投資策略	13
第三章	研究假說與方法	16
第一節	資料來源	17
第二節	研究假說	18
第三節	研究設計	20
第四章	實證結果	22
第一節	敘述統計量	22
第二節	比特幣各種三紅K線介入的實証結果	23
第三節	比特幣各種三黑K線介入的實証結果	25
第五章	結論與建議	26
第一節	研究結論	26
第二節	投資與管理意涵	27
第三節	研究限制與後續研究建議	29
參考文獻		31
圖目錄
圖1-1	研究架構流程圖	4
圖3-1	陰陽線的範例	19
表目錄
表2-1	CBOE和CME比較	13
表3-1	CME敘述統計	23
表3-2	各種三紅K、三黑K介入下之比特幣日內交易策略	21
表4-1	BitMex交易平台敘述統計	23
表4-2	各類三紅K介入當沖盈虧分析	25
表4-3	各類三黑K介入當沖盈虧分析	26
參考文獻
Atsalakis, G. S., Atsalaki, I. G., Pasiouras, F., & Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research, 276(2), 770-780.
Avramov, D., & Hore, S. (2017). Cross-sectional factor dynamics and momentum returns
. Journal of Financial Markets, 32, 69-96.
Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307-343.
Black, F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3(1-2), 167-179.
Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-38.
Brandvold, M., Molnár, P., Vagstad, K., & Valstad, O. C. A. (2015). Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18-35.
Challet, D., & Ayed, A. B. H. (2013). Predicting financial markets with Google Trends and not so random keywords. arXiv preprint arXiv:1307.4643.
Chan, K. (1992). A further analysis of the lead–lag relationship between the cash market and stock index futures market. The Review of Financial Studies, 5(1), 123-152.
Chan, Y. C., Chui, A. C., & Kwok, C. C. (2001). The impact of salient political and economic news on the trading activity. Pacific-Basin Finance Journal, 9(3), 195-217.
Chen, S. Y., Lin, C. C., Chou, P. H., & Hwang, D. Y. (2002). A comparison of hedge effectiveness and price discovery between TAIFEX TAIEX index futures and SGX MSCI Taiwan index futures. Review of Pacific Basin Financial Markets and Policies, 5(02), 277-300.
Chung, J. M., Choe, H., & Kho, B. C. (2009). The impact of day‐trading on volatility and liquidity. Asia‐Pacific Journal of Financial Studies, 38(2), 237-275.
Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799-1815.
Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88.
Dahlquist, M., & Hasseltoft, H. (2020). Economic momentum and currency returns. Journal of Financial Economics, 136(1), 152-167.
Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under‐and overreactions. the Journal of Finance, 53(6), 1839-1885.
De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. The Journal of Finance, 40(3), 793-805.
Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85-92.
Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144.
Dyhrberg, A. H., Foley, S., & Svec, J. (2018). How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets. Economics Letters, 171, 140-143.
Gandal, N., Hamrick, J. T., Moore, T., & Oberman, T. (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95, 86-96.
Gao, L., Han, Y., Li, S. Z., & Zhou, G. (2018). Market intraday momentum. Journal of Financial Economics, 129(2), 394-414.
Greaves, A., & Au, B. (2015). Using the bitcoin transaction graph to predict the price of bitcoin. No Data.
Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading, and overreaction in asset markets. The Journal of Finance, 54(6), 2143-2184.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of finance, 48(1), 65-91.
Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3-6.
Kim, A. (2015). Does futures speculation destabilize commodity markets?. Journal of Futures Markets, 35(8), 696-714.
Kyröläinen, P. (2008). Day trading and stock price volatility. Journal of Economics and Finance, 32(1), 75-89.
Madan, I., Saluja, S., & Zhao, A. (2015). Automated bitcoin trading via machine learning algorithms. URL: http://cs229. stanford. edu/proj2014/Isaac% 20Madan, 20.
Narayan, P. K., Ahmed, H. A., & Narayan, S. (2015). Do momentum‐based trading strategies work in the commodity futures markets?. Journal of Futures Markets, 35(9), 868-891.
Nnadi, M., & Tanna, S. (2019). Accounting analyses of momentum and contrarian strategies in emerging markets. Asia-Pacific Journal of Accounting & Economics, 26(4), 457-477.
Othman, A. H. A., Kassim, S., Rosman, R. B., & Redzuan, N. H. B. (2020). Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach. Journal of Revenue and Pricing Management, 1-17.
Ozenbas, D., Schwartz, R. A., & Wood, R. A. (2002). Volatility in US and European equity markets: An assessment of market quality. International Finance, 5(3), 437-461.
Pinudom, B., Tungpisansampun, W., Tansuchat, R., & Maneejuk, P. (2018, July). Could Bitcoin enhance the portfolio performance?. In Journal of Physics: Conference Series (Vol. 1053, No. 1, p. 012113). IOP Publishing.  
Platanakis, E., & Urquhart, A. (2019). Should investors include bitcoin in their portfolios? a portfolio theory approach. The British Accounting Review, 100837.
Roope, M., & Zurbruegg, R. (2002). The intra‐day price discovery process between the Singapore Exchange and Taiwan Futures Exchange. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 22(3), 219-240.
Singal, V., & Tayal, J. (2019). Risky short positions and investor sentiment: Evidence from the weekend effect in futures markets. Journal of Futures Markets.
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
Urquhart, A. (2017). Price clustering in Bitcoin. Economics letters, 159, 145-148.
Urquhart, A. (2017). Price clustering in Bitcoin. Economics letters, 159, 145-148.
Viljoen, T., Westerholm, P. J., & Zheng, H. (2014). Algorithmic trading, liquidity, and price discovery An intraday analysis of the SPI 200 futures. Financial Review, 49(2), 245-270.
論文全文使用權限
校內
校內紙本論文立即公開
同意電子論文全文授權校園內公開
校內電子論文立即公開
校外
同意授權
校外電子論文立即公開

如有問題,歡迎洽詢!
圖書館數位資訊組 (02)2621-5656 轉 2487 或 來信