臺灣博碩士論文加值系統

外匯市場是由全球各地區不同交易時段的交易市場組成，倫敦市場和紐約市場交易時間的重疊區是全球外匯交易最頻繁的時段，將資料視覺化後發現在兩大市場交易時間重疊區內，匯率的波動幅度較大，因此判斷交易時間重疊區會對匯率造成影響。每週末外匯市場休市後可能發布出乎意料的新聞消息，所以收市前持有外匯的風險較高。將資料視覺化後觀察發現，在收市前和開盤後，匯率會有明顯的漲跌現象，因此判斷週末會對匯率造成影響。
綜上所述，本研究除探討一般量價屬性外，並加入週末和交易時間重疊區的因素，選擇歐元兌美元為研究標的，建立預測匯率的支持向量迴歸(Support Vector Regression, SVR)模型，透過基因演算法(Genetic Algorithm, GA)調整模型參數，以取得最佳化模型。實驗結果顯示，加入週末及交易時間重疊區屬性的GA-SVR較僅使用外匯基本量價屬性之GA-SVR可降低9.4%的平均絕對百分比誤差(Mean Absolute Percent Error, MAPE)。

The foreign exchange market is composed of trading markets in different trading hours in various regions of the world. The overlapping area of trading hours between the London market and the New York market is the most frequent period of global foreign exchange transactions. The data is visualized and found in the overlapping areas of the two major trading hours. The exchange rate fluctuates a lot, so judging the overlapping time of the trading time will affect the exchange rate. Every weekend, the foreign exchange market may release unexpected news after the market closes, so the risk of holding foreign exchange before the market close is higher. After visualizing the data, it is observed that the exchange rate will have a significant rise and fall before and after the market close, so it is judged that the weekend will affect the exchange rate.
In summary, this study explores the Support Vector Regression (SVR) model of forecasting exchange rate by selecting the euro-dollar as the research target, in addition to the general price-quantity attributes, and adding the factors of weekend and trading time overlap. The model parameters are adjusted by the Genetic Algorithm (GA) to obtain an optimized model. The experimental results show that the GA-SVR with the weekend and trading time overlap area attribute can reduce the Mean Absolute Percent Error (MAPE) by 9.4% compared with the GA-SVR using only the foreign exchange basic price attribute.

摘要 I
Abstract II
目次 III
表次 V
圖次 VI
第一章 緒論 1
第一節 研究背景 1
第二節 研究目的 3
第三節 論文架構 4
第二章 文獻探討 5
第一節 外匯市場探討 5
第二節 資料視覺化 8
第三節 支持向量迴歸於金融研究應用 10
第四節 基因演算法與參數估測應用 13
第三章 研究方法 15
第一節 外匯資料收集與處理 15
第二節 支持向量迴歸 21
第三節 基因演算法 26
第四章 研究結果 31
第一節 匯率實驗數據 31
第二節 驗證結果 31
第五章 結論與建議 39
參考文獻 41
中文參考 41
英文參考 42
網頁參考 44

中文參考
[1] 黃瀞萱 (2013)。支持向量迴歸結合真實波動分類預測股票指數。臺北市立大學資訊科學系碩士班碩士論文，臺北市。
[2] 陳東明 (1991)。台灣股票市場價量關係之實證研究。國立臺灣大學商學研究所碩士論文，臺北市。
[3] 張宏丞 (2016)。BI視覺化工具運用於資安日誌分析—以S公司為例。資訊管理學系在職專班。
[4] 周以騏 (2018)。基於文字情感分析應用在台灣股價指數之研究。國立臺北科技大學資訊與財金管理系。
[5] 李晟源 (2015)。支援向量迴歸利用行政區域分群預測房屋價格。臺北市立大學資訊科學系碩士學位在職進修專班。
[6] 游廷鈞 (2017)。基因演算法結合支持向量機應用於預測美國大聯盟季後賽隊伍。臺北市立大學資訊科學系碩士學位在職進修專班。
[7] 廖麗滿，黃敬仁，林志諭 (2011)。穩健多目標基因演算法應用於流程型工廠之排程研究。技術學刊，第26卷第1期，第65-71頁。
[8] 楊金庫，郭雷 (2016)。基於二維EMD和遺傳演算法的多聚焦圖像融合。技術學刊，第31卷第1期，第51-58頁。
[9] 黃祺偉 (2009)。多核心支援迴歸向量機應用於股價預測。國立中山大學電機工程學系。
[10] 邱昭彰，李安邦 (2005)。遺傳演算法在發展股市投資專家知識規則之研究。明新學報 31 期，第87-99頁。
[11] 任立斌 (2018)。應用特徵選取進行股價預測與獲利可能性之研究。中興大學資訊管理學系所學位論文，第1-100頁。

英文參考
[12] Cross, F. (1973). The behavior of stock prices on Fridays and Mondays. Financial analysts journal, 29(6), 67-69.
[13] Bush, P. J., & Stephens, J. E. (2016). The Return of the Monday Effect in European Currency Markets: An Empirical Analysis of the Impact of the Economic Crisis on Market Efficiency. International Journal of Finance & Economics, 21(3), 241-246.
[14] Ward, M. O., Grinstein, G., & Keim, D. (2015). Interactive data visualization: foundations, techniques, and applications. AK Peters/CRC Press.
[15] Nair, L., Shetty, S., & Shetty, S. (2016). Interactive visual analytics on Big Data: Tableau vs D3. js. Journal of e-Learning and Knowledge Society, 12(4).
[16] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[17] Vapnik, V., Golowich, S. E., & Smola, A. J. (1997). Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems (pp. 281-287).
[18] Sermpinis, G., Stasinakis, C., Theofilatos, K., & Karathanasopoulos, A. (2015). Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations. European Journal of Operational Research, 247(3), 831-846.
[19] Jiang, F., & Wu, W. (2016, August). Hybrid Genetic Algorithm and Support Vector Regression Performance in CNY Exchange Rate Prediction. In 2016 International Conference on Engineering Science and Management. Atlantis Press.
[20] Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
[21] Ying, C. C. (1966). Stock market prices and volumes of sales. Econometrica (pre-1986), 34(3), 676.
[22] McGill, R., Tukey, J. W., & Larsen, W. A. (1978). Variations of box plots. The American Statistician, 32(1), 12-16.
[23] Levinson, M. (2014). The economist guide to financial markets: Why they exist and how they work. The Economist.
[24] Costantini, M., Cuaresma, J. C., & Hlouskova, J. (2016). Forecasting errors, directional accuracy and profitability of currency trading: The case of EUR/USD exchange rate. Journal of Forecasting, 35(7), 652-668.
[25] Roodbar, B., Metcalf, H., & Casalin, F. (2019). Trading European Central Bank rumours on the EUR-USD exchange rate market. International Review of Financial Analysis, 61, 53-70.
[26] Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction. Neurocomputing, 172, 446-452.
[27] Babu, A. S., & Reddy, S. K. (2015). Exchange rate forecasting using ARIMA, neural network and fuzzy neuron. Journal of Stock & Forex Trading, 3(4), 1-5.
[28] Dao, T. M., McGroarty, F., & Urquhart, A. (2016). A calendar effect: Weekend overreaction (and subsequent reversal) in spot FX rates. Journal of Multinational Financial Management, 37, 158-167.
[29] Chen, W., Guo, F., & Wang, F. Y. (2015). A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems, 16(6), 2970-2984.
[30] Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
[31] Bajpai, P., & Kumar, M. (2010). Genetic algorithm–an approach to solve global optimization problems. Indian Journal of computer science and engineering, 1(3), 199-206.
[32] Yang, M. H., Lo, C. C., Chen, H. M., & Huang, H. L. (2005). Hybrid genetic algorithms for minimizing the maximum completion time for the wafer probing scheduling problem. Journal of the Chinese institute of industrial engineers, 22(3), 218-225.
[33] Ren, Y., & Bai, G. (2010). Determination of optimal SVM parameters by using GA/PSO. JCP, 5(8), 1160-1168.
[34] Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Machine learning, 46(1-3), 131-159.

網頁參考
[35] 外匯交易的黃金時間|福匯FXCM官網. (2019). Retrieved from https://www.fuhuiglobal.com/tc/insights/forex-trading-time/
[36] MetaQuotes Software Corp. (2005). MetaTrader 4 Trading Platform Released. Retrieved from https://www.metatrader4.com/zh/company/10
[37] [資料分析&機器學習] 第2.5講：資料視覺化(Matplotlib, Seaborn, Plotly). (2019). Retrieved from https://medium.com/jameslearningnote/資料分析-機器學習-第2-5講-資料視覺化-matplotlib-seaborn-plotly-75cd353d6d3f
[38] Python使用plotly繪製資料圖表的方法 | 程式前沿. (2018). Retrieved from https://codertw.com/程式語言/364919/
[39] Triennial Central Bank Survey of foreign exchange and OTC derivatives markets in 2016. (2016). Retrieved from https://www.bis.org/publ/rpfx16.htm?m=6%7C381%7C677
[40] Microsoft. (2019). 什麼是 Power BI | Microsoft Power BI. Retrieved from https://powerbi.microsoft.com/zh-tw/what-is-power-bi/
[41] 外匯市場介紹 | 外匯戰士 - Part 3. (2013). Retrieved from https://fxwarrior.net/newbie/forex-introduction/3/
[42] Tableau - 視覺化商業智慧分析軟體 - 昊青公司軟體代理商. (2019). Retrieved from https://www.sciformosa.com.tw/tableau.php?tid=639