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題名 比特幣日內技術分析交易策略:以交易區間突破為例
Intraday Technical Analysis Trading Strategies in Bitcoin: A Focus on Trading Range Breakouts
作者 陳冠華
Chen, Guan-Hua
貢獻者 岳夢蘭
Yueh, Meng-Lan
陳冠華
Chen, Guan-Hua
關鍵詞 比特幣
加密貨幣
日內交易
高頻交易
技術分析
交易區間突破
Bitcoin
Cryptocurrency
Intraday trading
High-frequency trading
Technical analysis
Trading range breakouts
日期 2024
上傳時間 5-Aug-2024 13:43:50 (UTC+8)
摘要 近年來，加密貨幣市場引起了許多投資者的關注，許多學者也投入研究其中。然而，目前只有少數文章探討了日內技術分析策略在比特幣市場的預測能力與獲利能力。本文使用1分鐘高頻資料，對比特幣的日內交易區間突破策略進行了全面研究。我發現，當出現向上突破與向下突破訊號時，通常會產生顯著的正報酬。此外，研究顯示，當持有期間小於支撐阻力窗口長度的一半時，報酬率更傾向顯著異於無條件均值。自2017年以來，比特幣的日內交易區間突破策略的預測能力下降。我還檢驗了不同資料頻率的影響，發現隨著資料頻率降低，策略組合的報酬率顯著性也隨之降低，這表明比特幣市場的效率性隨著時間和資料頻率的降低而提高。最後，考慮到交易成本後，發現交易區間突破策略在比特幣日內市場中無法超越買進持有策略。
In recent years, the cryptocurrency market has attracted much attention from investors and scholars. However, only a handful of studies have looked at the predictive power and profitability of intraday technical analysis strategies in the Bitcoin market. This study examines intraday trading range breakout strategies using 1-minute high-frequency data. The results show that significant positive returns often occur after breakout signals. Shorter holding periods, less than half the support-resistance window, tend to produce returns that are different from the unconditional mean return. Since 2017, the strategy’s effectiveness has decreased. As data frequency decreases, the returns from trading range breakout strategy become insignificant. Finally, after accounting for transaction costs, the trading range breakout strategies do not outperform the buy-and-hold strategy.
參考文獻 Al-Yahyaee, K. H., Mensi, W., & Yoon, S. M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234. Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4. Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, 177-189. Bessembinder, H., & Chan, K. (1998). Market efficiency and the returns to technical analysis. Financial management, 5-17. Blau, B. M. (2017). Price dynamics and speculative trading in bitcoin. Research in international Business and Finance, 41, 493-499. Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95. Bouri, E., Lau, C. K. M., Lucey, B., & Roubaud, D. (2019). Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Research Letters, 29, 340-346. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?. Finance Research Letters, 20, 192-198. 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. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of finance, 47(5), 1731-1764. Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics letters, 130, 32-36. Corbet, S., Eraslan, V., Lucey, B., & Sensoy, A. (2019). The effectiveness of technical trading rules in cryptocurrency markets. Finance Research Letters, 31, 32-37. Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88. Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics letters, 165, 28-34. Detzel, A., Liu, H., Strauss, J., Zhou, G., & Zhu, Y. (2021). Learning and predictability via technical analysis: evidence from Bitcoin and stocks with hard‐to‐value fundamentals. Financial Management, 50(1), 107-137. Dwyer, G. P. (2015). The economics of Bitcoin and similar private digital currencies. Journal of financial stability, 17, 81-91. Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144. Eross, A., McGroarty, F., Urquhart, A., & Wolfe, S. (2019). The intraday dynamics of bitcoin. Research in international business and finance, 49, 71-81. Glantz, M., & Kissell, R. (2013). Multi-asset risk modeling: techniques for a global economy in an electronic and algorithmic trading era. Academic Press. Grobys, K., Ahmed, S., & Sapkota, N. (2020). Technical trading rules in the cryptocurrency market. Finance Research Letters, 32, 101396. Hsu, P. H., Taylor, M. P., & Wang, Z. (2016). Technical trading: Is it still beating the foreign exchange market?. Journal of International Economics, 102, 188-208. Hu, B., McInish, T., Miller, J., & Zeng, L. (2019). Intraday price behavior of cryptocurrencies. Finance Research Letters, 28, 337-342. Hudson, R., & Urquhart, A. (2021). Technical trading and cryptocurrencies. Annals of Operations Research, 297(1), 191-220. Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280-284. Khairuddin, I. E., Sas, C., Clinch, S., & Davies, N. (2016, May). Exploring motivations for bitcoin technology usage. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2872-2878). Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, 167, 26-28. Kristoufek, L. (2018). On Bitcoin markets (in) efficiency and its evolution. Physica A: statistical mechanics and its applications, 503, 257-262. Marshall, B. R., Cahan, R. H., & Cahan, J. M. (2008). Does intraday technical analysis in the US equity market have value?. Journal of Empirical Finance, 15(2), 199-210. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Olson, D. (2004). Have trading rule profits in the currency markets declined over time?. Journal of banking & Finance, 28(1), 85-105. Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73. Su, F., Wang, X., & Yuan, Y. (2022). The intraday dynamics and intraday price discovery of bitcoin. Research in International Business and Finance, 60, 101625. 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., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63, 49-57. Wyckoff, R. D. (1910). Studies in tape reading. Traders Press. Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43). Academic Press. Zargar, F. N., & Kumar, D. (2019). Informational inefficiency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance, 47, 344-353. Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and its Applications, 510, 658-670.
描述 碩士
國立政治大學
財務管理學系
111357028
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111357028
資料類型 thesis
dc.contributor.advisor 岳夢蘭zh_TW
dc.contributor.advisor Yueh, Meng-Lanen_US
dc.contributor.author (Authors) 陳冠華zh_TW
dc.contributor.author (Authors) Chen, Guan-Huaen_US
dc.creator (作者) 陳冠華zh_TW
dc.creator (作者) Chen, Guan-Huaen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 13:43:50 (UTC+8)-
dc.date.available 5-Aug-2024 13:43:50 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 13:43:50 (UTC+8)-
dc.identifier (Other Identifiers) G0111357028en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152729-
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) 111357028zh_TW
dc.description.abstract (摘要) 近年來，加密貨幣市場引起了許多投資者的關注，許多學者也投入研究其中。然而，目前只有少數文章探討了日內技術分析策略在比特幣市場的預測能力與獲利能力。本文使用1分鐘高頻資料，對比特幣的日內交易區間突破策略進行了全面研究。我發現，當出現向上突破與向下突破訊號時，通常會產生顯著的正報酬。此外，研究顯示，當持有期間小於支撐阻力窗口長度的一半時，報酬率更傾向顯著異於無條件均值。自2017年以來，比特幣的日內交易區間突破策略的預測能力下降。我還檢驗了不同資料頻率的影響，發現隨著資料頻率降低，策略組合的報酬率顯著性也隨之降低，這表明比特幣市場的效率性隨著時間和資料頻率的降低而提高。最後，考慮到交易成本後，發現交易區間突破策略在比特幣日內市場中無法超越買進持有策略。zh_TW
dc.description.abstract (摘要) In recent years, the cryptocurrency market has attracted much attention from investors and scholars. However, only a handful of studies have looked at the predictive power and profitability of intraday technical analysis strategies in the Bitcoin market. This study examines intraday trading range breakout strategies using 1-minute high-frequency data. The results show that significant positive returns often occur after breakout signals. Shorter holding periods, less than half the support-resistance window, tend to produce returns that are different from the unconditional mean return. Since 2017, the strategy’s effectiveness has decreased. As data frequency decreases, the returns from trading range breakout strategy become insignificant. Finally, after accounting for transaction costs, the trading range breakout strategies do not outperform the buy-and-hold strategy.en_US
dc.description.tableofcontents 1. Introduction 1 2. Literature Review. 3 2.1. Cryptocurrency literature 3 2.2. Cryptocurrency intraday literature 4 2.3. Technical analysis literature 5 3. Data and trading rules 6 3.1. Data 6 3.2. Trading rules 8 4. Empirical results 9 4.1. Main results 9 4.2. Sub-sample and different frequencies 10 4.3. Profitability considering transaction costs 11 5. Conclusion 12 Reference 14 Appendix 18zh_TW
dc.format.extent 605753 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111357028en_US
dc.subject (關鍵詞) 比特幣zh_TW
dc.subject (關鍵詞) 加密貨幣zh_TW
dc.subject (關鍵詞) 日內交易zh_TW
dc.subject (關鍵詞) 高頻交易zh_TW
dc.subject (關鍵詞) 技術分析zh_TW
dc.subject (關鍵詞) 交易區間突破zh_TW
dc.subject (關鍵詞) Bitcoinen_US
dc.subject (關鍵詞) Cryptocurrencyen_US
dc.subject (關鍵詞) Intraday tradingen_US
dc.subject (關鍵詞) High-frequency tradingen_US
dc.subject (關鍵詞) Technical analysisen_US
dc.subject (關鍵詞) Trading range breakoutsen_US
dc.title (題名) 比特幣日內技術分析交易策略:以交易區間突破為例zh_TW
dc.title (題名) Intraday Technical Analysis Trading Strategies in Bitcoin: A Focus on Trading Range Breakoutsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Al-Yahyaee, K. H., Mensi, W., & Yoon, S. M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228-234. Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1-4. Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, 177-189. Bessembinder, H., & Chan, K. (1998). Market efficiency and the returns to technical analysis. Financial management, 5-17. Blau, B. M. (2017). Price dynamics and speculative trading in bitcoin. Research in international Business and Finance, 41, 493-499. Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95. Bouri, E., Lau, C. K. M., Lucey, B., & Roubaud, D. (2019). Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Research Letters, 29, 340-346. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?. Finance Research Letters, 20, 192-198. 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. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of finance, 47(5), 1731-1764. Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics letters, 130, 32-36. Corbet, S., Eraslan, V., Lucey, B., & Sensoy, A. (2019). The effectiveness of technical trading rules in cryptocurrency markets. Finance Research Letters, 31, 32-37. Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the Bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81-88. Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics letters, 165, 28-34. Detzel, A., Liu, H., Strauss, J., Zhou, G., & Zhu, Y. (2021). Learning and predictability via technical analysis: evidence from Bitcoin and stocks with hard‐to‐value fundamentals. Financial Management, 50(1), 107-137. Dwyer, G. P. (2015). The economics of Bitcoin and similar private digital currencies. Journal of financial stability, 17, 81-91. Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144. Eross, A., McGroarty, F., Urquhart, A., & Wolfe, S. (2019). The intraday dynamics of bitcoin. Research in international business and finance, 49, 71-81. Glantz, M., & Kissell, R. (2013). Multi-asset risk modeling: techniques for a global economy in an electronic and algorithmic trading era. Academic Press. Grobys, K., Ahmed, S., & Sapkota, N. (2020). Technical trading rules in the cryptocurrency market. Finance Research Letters, 32, 101396. Hsu, P. H., Taylor, M. P., & Wang, Z. (2016). Technical trading: Is it still beating the foreign exchange market?. Journal of International Economics, 102, 188-208. Hu, B., McInish, T., Miller, J., & Zeng, L. (2019). Intraday price behavior of cryptocurrencies. Finance Research Letters, 28, 337-342. Hudson, R., & Urquhart, A. (2021). Technical trading and cryptocurrencies. Annals of Operations Research, 297(1), 191-220. Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25, 280-284. Khairuddin, I. E., Sas, C., Clinch, S., & Davies, N. (2016, May). Exploring motivations for bitcoin technology usage. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2872-2878). Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, 167, 26-28. Kristoufek, L. (2018). On Bitcoin markets (in) efficiency and its evolution. Physica A: statistical mechanics and its applications, 503, 257-262. Marshall, B. R., Cahan, R. H., & Cahan, J. M. (2008). Does intraday technical analysis in the US equity market have value?. Journal of Empirical Finance, 15(2), 199-210. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Olson, D. (2004). Have trading rule profits in the currency markets declined over time?. Journal of banking & Finance, 28(1), 85-105. Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73. Su, F., Wang, X., & Yuan, Y. (2022). The intraday dynamics and intraday price discovery of bitcoin. Research in International Business and Finance, 60, 101625. 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., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63, 49-57. Wyckoff, R. D. (1910). Studies in tape reading. Traders Press. Yermack, D. (2015). Is Bitcoin a real currency? An economic appraisal. In Handbook of digital currency (pp. 31-43). Academic Press. Zargar, F. N., & Kumar, D. (2019). Informational inefficiency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance, 47, 344-353. Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and its Applications, 510, 658-670.zh_TW