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題名 濾嘴法則應用在加密貨幣的獲利性
Profitability of Applying Filter Rule to Cryptocurrency
作者 羅健捷
Lo, Chien-Chieh
貢獻者 郭維裕
Kuo, Wei-yu
羅健捷
Lo, Chien-Chieh
關鍵詞 加密貨幣
傳統濾嘴法則
純做多濾嘴法則
使用擴張窗口的濾嘴法則
BTC
ETH
LTC
XRP
Cryptocurrency
Filter rule
Filter rule with expanding window
ETH
BTC
LTC
XRP
日期 2022
上傳時間 1-Feb-2023 14:05:16 (UTC+8)
摘要 這幾年，加密貨幣發展興盛，越來越多人將加密貨幣當成一種投資標的，因此除了一開始的去中心化交易平台，現在也越來越多集中交易所，例如：Binance(幣安)、Coinbase、MAX等等，讓更多投資人可以不用開設任何加密貨幣的錢包，直接在平台上註冊一個帳戶，就可以購買加密貨幣，使投資加密貨幣的流程更加簡單方便。因此本研究在文獻探討中，討論很多有關於加密貨幣的各種特質，包括其波動度、與經濟指標的相關性、加密貨幣投資者的投資傾向，再討論已被使用在加密貨幣上的投資策略，並且陳述這些研究發現的結果，最後依據前述研究發現加密貨幣市場有羊群效應以及更適合類似動能交易的策略為方向，從而決定使用傳統濾嘴法則、純做多或做空的濾嘴法則、加上擴張窗口的濾嘴法則，三種不同方式的策略應用在BTC、ETH、LTC、XRP四種貨幣上，討論累積報酬率、交易次數、平均報酬等等，其中不同與以往的濾嘴法則，以前的濾嘴法則單純使用對稱的條件組合，本研究的漲跌幅條件範圍從0.03到0.08，每0.005為一個單位，各有11個條件，從中組合出121種不對稱的漲跌幅條件組合。從研究結果發現，傳統濾嘴法則應用在上述的四種加密貨幣上，在大部分情況下都無法獲得比買進持有策略更好的報酬率，但當該策略變成純做多或純做空的濾嘴法則後，發現純做多的策略，雖然不是在所有條件皆能打代買進持有策略，但明顯報酬率大多數比傳統濾嘴法則來得更好，同時也意味著純做空的濾嘴法則多半處於虧損狀態，最後，為了測試是否使用歷史資料中的最佳濾嘴條件，可以在未來的交易中獲得更好的報酬，本研究結合擴張窗口的方式來執行濾嘴法則，並且每兩個月做一次調整，但結果是歷史資料在該策略中無法獲得較好的報酬率表現。
In recent years, the development of cryptocurrencies has flourished, and more and more people regard cryptocurrencies as a subject of investment. Therefore, in addition to the initial decentralized trading platforms, there are also more and more new exchanges, such as: Binance, Coinbase, MAX, etc., so that more investors can directly buy cryptocurrencies without opening any cryptocurrency wallets, making the process of investing in cryptocurrencies easier and more convenient. In the literature review, many characteristics of cryptocurrencies are discussed, including their volatility, correlation with economic indicators, the investment tendency of cryptocurrency investors, and the investment strategies that have been used in cryptocurrencies, and it states the results of these research findings. According to the characteristics found in the aforementioned research, the cryptocurrency market has a herd effect and is more suitable for strategies similar to momentum trading. It is decided to use traditional filter rule, pure long or short filter rule, and the filter rule with the expansion window. Three different strategies are applied to BTC, ETH, LTC, and XRP to discuss the cumulative return, the number of transactions, the average return, etc. The filter rule is slightly different from the previous filter rule, which are used by Fama and Blume (1966), in filters combination. The previous filter rule simply used a symmetrical combination of filter conditions; however, the range of the rise and fall conditions in this study is from 0.03 to 0.08, each 0.005 is an unit, and each has 11 conditions, therefore there are 121 combination of rise and fall conditions. In the result, it is found that the traditional filter rules applied to the above four cryptocurrencies cannot obtain a better return than the buy and hold strategy in most cases. But when the strategy became pure long or pure short filter rule, It found that the pure long strategy, although it did not win buy and hold strategy under all conditions, obviously most of the returns are better than the traditional filter rule. Meanwhile, it also means that the pure short filter rule is mostly at a loss. Finally, in order to test whether using the best filter conditions in the historical data can get better returns in future trades, this study discuss the filter rule with an expanding window, and adjustment of investment is made every two months, but the result is that historical data does not provide a better return performance in this strategy.
參考文獻 Almujamed, H. I., 2018, Filter rule performance in an emerging market: evidence
from Qatari listed companies. International Journal of Productivity and
Performance Management.
Arnott, R. D., et al., 2005, Fundamental indexation, Financial Analysts Journal 61(2), 83-99.
Baek, C. and M. Elbeck, 2015, Bitcoins as an investment or speculative vehicle? A first look, Applied Economics Letters 22(1), 30-34.
Borri, N., 2019, Conditional tail-risk in cryptocurrency markets, Journal of Empirical Finance 50, 1-19.
Cooper, M., 1999, Filter rules based on price and volume in individual security overreaction, The Review of Financial Studies 12(4), 901-935.
Corbet, S., et al., 2019, The effectiveness of technical trading rules in cryptocurrency markets, Finance Research Letters 31, 32-37.
Detzel, Liu, Strauss, Zhou, and Zhu, 2021, Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals, Financial Management 50(1), 107-137.
Elgammal, M. M., et al., 2021, The predictive ability of stock market factors, Studies in Economics and Finance.
Fama, E. F. and M. E. Blume (1966)., Filter rules and stock-market trading, The Journal of Business 39(1), 226-241.
Jennergren, L. P., 1975, A Note on Filter Trading on the Stockholm Stock Exchange, The Swedish Journal of Economics 77(2), 252-259.
Kyriazis, N. A., 2019, A survey on efficiency and profitable trading opportunities in cryptocurrency markets, Journal of Risk and Financial Management 12(2), 67.
Kyriazis, N. A., 2021, A survey on volatility fluctuations in the decentralized cryptocurrency financial assets, Journal of Risk and Financial Management 14(7), 293.
Leirvik, T., 2022, Cryptocurrency returns and the volatility of liquidity, Finance Research Letters 44, 102031.
Liu, W., 2019, Portfolio diversification across cryptocurrencies, Finance Research Letters 29, 200-205.
Liu, Y. and A. Tsyvinski, 2021, Risks and returns of cryptocurrency, The Review of Financial Studies 34(6), 2689-2727.
Liu, Y., et al., 2019, Common risk factors in cryptocurrency, National Bureau of Economic Research.
Urquhart, A., 2017, Price clustering in Bitcoin, Economics letters 159, 145-148.
周建新 and 陳振遠, 2021, 濾嘴法則操作績效與台灣期貨市場效率性之研究.
描述 碩士
國立政治大學
國際經營與貿易學系
109351034
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109351034
資料類型 thesis
dc.contributor.advisor 郭維裕zh_TW
dc.contributor.advisor Kuo, Wei-yuen_US
dc.contributor.author (Authors) 羅健捷zh_TW
dc.contributor.author (Authors) Lo, Chien-Chiehen_US
dc.creator (作者) 羅健捷zh_TW
dc.creator (作者) Lo, Chien-Chiehen_US
dc.date (日期) 2022en_US
dc.date.accessioned 1-Feb-2023 14:05:16 (UTC+8)-
dc.date.available 1-Feb-2023 14:05:16 (UTC+8)-
dc.date.issued (上傳時間) 1-Feb-2023 14:05:16 (UTC+8)-
dc.identifier (Other Identifiers) G0109351034en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/143187-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易學系zh_TW
dc.description (描述) 109351034zh_TW
dc.description.abstract (摘要) 這幾年，加密貨幣發展興盛，越來越多人將加密貨幣當成一種投資標的，因此除了一開始的去中心化交易平台，現在也越來越多集中交易所，例如：Binance(幣安)、Coinbase、MAX等等，讓更多投資人可以不用開設任何加密貨幣的錢包，直接在平台上註冊一個帳戶，就可以購買加密貨幣，使投資加密貨幣的流程更加簡單方便。因此本研究在文獻探討中，討論很多有關於加密貨幣的各種特質，包括其波動度、與經濟指標的相關性、加密貨幣投資者的投資傾向，再討論已被使用在加密貨幣上的投資策略，並且陳述這些研究發現的結果，最後依據前述研究發現加密貨幣市場有羊群效應以及更適合類似動能交易的策略為方向，從而決定使用傳統濾嘴法則、純做多或做空的濾嘴法則、加上擴張窗口的濾嘴法則，三種不同方式的策略應用在BTC、ETH、LTC、XRP四種貨幣上，討論累積報酬率、交易次數、平均報酬等等，其中不同與以往的濾嘴法則，以前的濾嘴法則單純使用對稱的條件組合，本研究的漲跌幅條件範圍從0.03到0.08，每0.005為一個單位，各有11個條件，從中組合出121種不對稱的漲跌幅條件組合。從研究結果發現，傳統濾嘴法則應用在上述的四種加密貨幣上，在大部分情況下都無法獲得比買進持有策略更好的報酬率，但當該策略變成純做多或純做空的濾嘴法則後，發現純做多的策略，雖然不是在所有條件皆能打代買進持有策略，但明顯報酬率大多數比傳統濾嘴法則來得更好，同時也意味著純做空的濾嘴法則多半處於虧損狀態，最後，為了測試是否使用歷史資料中的最佳濾嘴條件，可以在未來的交易中獲得更好的報酬，本研究結合擴張窗口的方式來執行濾嘴法則，並且每兩個月做一次調整，但結果是歷史資料在該策略中無法獲得較好的報酬率表現。zh_TW
dc.description.abstract (摘要) In recent years, the development of cryptocurrencies has flourished, and more and more people regard cryptocurrencies as a subject of investment. Therefore, in addition to the initial decentralized trading platforms, there are also more and more new exchanges, such as: Binance, Coinbase, MAX, etc., so that more investors can directly buy cryptocurrencies without opening any cryptocurrency wallets, making the process of investing in cryptocurrencies easier and more convenient. In the literature review, many characteristics of cryptocurrencies are discussed, including their volatility, correlation with economic indicators, the investment tendency of cryptocurrency investors, and the investment strategies that have been used in cryptocurrencies, and it states the results of these research findings. According to the characteristics found in the aforementioned research, the cryptocurrency market has a herd effect and is more suitable for strategies similar to momentum trading. It is decided to use traditional filter rule, pure long or short filter rule, and the filter rule with the expansion window. Three different strategies are applied to BTC, ETH, LTC, and XRP to discuss the cumulative return, the number of transactions, the average return, etc. The filter rule is slightly different from the previous filter rule, which are used by Fama and Blume (1966), in filters combination. The previous filter rule simply used a symmetrical combination of filter conditions; however, the range of the rise and fall conditions in this study is from 0.03 to 0.08, each 0.005 is an unit, and each has 11 conditions, therefore there are 121 combination of rise and fall conditions. In the result, it is found that the traditional filter rules applied to the above four cryptocurrencies cannot obtain a better return than the buy and hold strategy in most cases. But when the strategy became pure long or pure short filter rule, It found that the pure long strategy, although it did not win buy and hold strategy under all conditions, obviously most of the returns are better than the traditional filter rule. Meanwhile, it also means that the pure short filter rule is mostly at a loss. Finally, in order to test whether using the best filter conditions in the historical data can get better returns in future trades, this study discuss the filter rule with an expanding window, and adjustment of investment is made every two months, but the result is that historical data does not provide a better return performance in this strategy.en_US
dc.description.tableofcontents 圖次 vi
表次 vii
第一章 緒論 1
第一節 濾嘴法則應用於加密貨幣 1
第二節 研究架構 1
第二章 文獻回顧與探討 3
第一節 加密貨幣的投資興起 3
第二節 加密貨幣的相關研究 3
第三節 應用於加密貨幣的投資策略 4
第四節 濾嘴法則的相關研究 5
第三章 研究方法 8
第一節 全樣本區間的濾嘴法則 8
一、 時間與標的選取方法 8
二、 濾嘴法則 8
三、 對稱與非對稱漲/跌幅條件 10
第二節 擴張窗口的濾嘴法則 11
第四章 實證結果 12
第一節 資料選取 12
第二節 研究結果展示方式 12
第三節 全樣本區間的傳統濾嘴法則 13
一、 傳統濾嘴法則的獲利性 13
二、 傳統濾嘴法則與買進持有策略比較 17
第四節 全樣本區間的純做多單或空單的濾嘴法則 20
一、 純多單與純空單的打擊率 20
二、 純做多與純做空的累積報酬率 23
第五節 擴張窗口(Expanding Window)下的濾嘴法則 27
第五章 結論 30
參考文獻 31		zh_TW
dc.format.extent 856322 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109351034en_US
dc.subject (關鍵詞) 加密貨幣zh_TW
dc.subject (關鍵詞) 傳統濾嘴法則zh_TW
dc.subject (關鍵詞) 純做多濾嘴法則zh_TW
dc.subject (關鍵詞) 使用擴張窗口的濾嘴法則zh_TW
dc.subject (關鍵詞) BTCzh_TW
dc.subject (關鍵詞) ETHzh_TW
dc.subject (關鍵詞) LTCzh_TW
dc.subject (關鍵詞) XRPzh_TW
dc.subject (關鍵詞) Cryptocurrencyen_US
dc.subject (關鍵詞) Filter ruleen_US
dc.subject (關鍵詞) Filter rule with expanding windowen_US
dc.subject (關鍵詞) ETHen_US
dc.subject (關鍵詞) BTCen_US
dc.subject (關鍵詞) LTCen_US
dc.subject (關鍵詞) XRPen_US
dc.title (題名) 濾嘴法則應用在加密貨幣的獲利性zh_TW
dc.title (題名) Profitability of Applying Filter Rule to Cryptocurrencyen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Almujamed, H. I., 2018, Filter rule performance in an emerging market: evidence
from Qatari listed companies. International Journal of Productivity and
Performance Management.
Arnott, R. D., et al., 2005, Fundamental indexation, Financial Analysts Journal 61(2), 83-99.
Baek, C. and M. Elbeck, 2015, Bitcoins as an investment or speculative vehicle? A first look, Applied Economics Letters 22(1), 30-34.
Borri, N., 2019, Conditional tail-risk in cryptocurrency markets, Journal of Empirical Finance 50, 1-19.
Cooper, M., 1999, Filter rules based on price and volume in individual security overreaction, The Review of Financial Studies 12(4), 901-935.
Corbet, S., et al., 2019, The effectiveness of technical trading rules in cryptocurrency markets, Finance Research Letters 31, 32-37.
Detzel, Liu, Strauss, Zhou, and Zhu, 2021, Learning and predictability via technical analysis: Evidence from bitcoin and stocks with hard‐to‐value fundamentals, Financial Management 50(1), 107-137.
Elgammal, M. M., et al., 2021, The predictive ability of stock market factors, Studies in Economics and Finance.
Fama, E. F. and M. E. Blume (1966)., Filter rules and stock-market trading, The Journal of Business 39(1), 226-241.
Jennergren, L. P., 1975, A Note on Filter Trading on the Stockholm Stock Exchange, The Swedish Journal of Economics 77(2), 252-259.
Kyriazis, N. A., 2019, A survey on efficiency and profitable trading opportunities in cryptocurrency markets, Journal of Risk and Financial Management 12(2), 67.
Kyriazis, N. A., 2021, A survey on volatility fluctuations in the decentralized cryptocurrency financial assets, Journal of Risk and Financial Management 14(7), 293.
Leirvik, T., 2022, Cryptocurrency returns and the volatility of liquidity, Finance Research Letters 44, 102031.
Liu, W., 2019, Portfolio diversification across cryptocurrencies, Finance Research Letters 29, 200-205.
Liu, Y. and A. Tsyvinski, 2021, Risks and returns of cryptocurrency, The Review of Financial Studies 34(6), 2689-2727.
Liu, Y., et al., 2019, Common risk factors in cryptocurrency, National Bureau of Economic Research.
Urquhart, A., 2017, Price clustering in Bitcoin, Economics letters 159, 145-148.
周建新 and 陳振遠, 2021, 濾嘴法則操作績效與台灣期貨市場效率性之研究.		zh_TW