| dc.contributor.advisor | 吳柏林<br>陳樹衡 | zh_TW |
| dc.contributor.advisor | Wu, Berlin<br>Chen, Shu-Heng | en_US |
| dc.contributor.author (Authors) | 棗厥庸 | zh_TW |
| dc.contributor.author (Authors) | Tsao, Chueh-Yung | en_US |
| dc.creator (作者) | 棗厥庸 | zh_TW |
| dc.creator (作者) | Tsao, Chueh-Yung | en_US |
| dc.date (日期) | 1998 | en_US |
| dc.date.accessioned | 18-Sep-2009 18:27:57 (UTC+8) | - |
| dc.date.available | 18-Sep-2009 18:27:57 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Sep-2009 18:27:57 (UTC+8) | - |
| dc.identifier (Other Identifiers) | B2002001686 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/36391 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 應用數學研究所 | zh_TW |
| dc.description (描述) | 85751002 | zh_TW |
| dc.description (描述) | 87 | zh_TW |
| dc.description.abstract (摘要) | 本研究中，我們計算OGA演化投資策略在五類時間數列模型上之表現，這五類模型分別是線性模型、雙線性模型、自迴歸條件異質變異數模型、門檻模型以及混沌模型。我們選擇獲勝機率、累積報酬率、夏普比例以及幸運係數做為評斷表現之準則，並分別推導出其漸近統計檢定。有別於一般計算智慧在財務工程上之應用，利用蒙地卡羅模擬法，研究中將對各表現準則提出嚴格之統計檢定結果。同時在實証研究中，我們考慮歐元兌美元及美元兌日圓的tick-by-tick匯率資料。故本研究主要的重點之一，乃是對於OGA演化投資策略，於這些模擬及實証資料上的有效性應用，作了深入且廣泛的探討。 | zh_TW |
| dc.description.abstract (摘要) | In this study, the performance of ordinary GA-based trading strategies are evaluated under five classes of time series model, namely, linear ARMA model, bilinear model, ARCH model, threshold model and chaotic model. The performance criteria employed are the winning probability, accumulated returns, Sharpe ratio and luck coefficient. We then provide the asymptotic statistical tests for these criteria. Unlike many existing applications of computational intelligence in financial engineering, for each performance criterion, we provide a rigorous statistical results based on Monte Carlo simulation. In the empirical study, two tick-by-tick foreign exchange rates are also considered, namely, EUR/USD and USD/JPY. As a result, this study provides us with a thorough understanding about the effectiveness of ordinary GA for evolving trading strategies under these artificial and natural time series data. | en_US |
| dc.description.tableofcontents | 1 Introduction and Motivation 2 The Trading Strategies 3 Performance Criteria and Their Statistical Tests 4 Simulation Designs and Analysis 5 Empirical Analysis 6 Concluding Remarks and Future Research | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#B2002001686 | en_US |
| dc.subject (關鍵詞) | 遺傳演算法 | zh_TW |
| dc.subject (關鍵詞) | 投資策略 | zh_TW |
| dc.subject (關鍵詞) | 時間數列模型 | zh_TW |
| dc.subject (關鍵詞) | Tick-by-tick資料 | zh_TW |
| dc.subject (關鍵詞) | 蒙地卡羅模擬 | zh_TW |
| dc.subject (關鍵詞) | 夏普級數 | zh_TW |
| dc.subject (關鍵詞) | 幸運係數 | zh_TW |
| dc.subject (關鍵詞) | Genetic Algorithms | en_US |
| dc.subject (關鍵詞) | Trading Strategies | en_US |
| dc.subject (關鍵詞) | Time Series Models | en_US |
| dc.subject (關鍵詞) | Tick-by-tick Data | en_US |
| dc.subject (關鍵詞) | Monte Carlo Simulation | en_US |
| dc.subject (關鍵詞) | Sharpe Ratio | en_US |
| dc.subject (關鍵詞) | Luck Coefficient | en_US |
| dc.title (題名) | 遺傳演算法投資策略在動態環境下的統計分析 | zh_TW |
| dc.title (題名) | The Statistical Analysis of GAs-Based Trading Strategies under Dynamic Landscape | en_US |
| dc.type (資料類型) | thesis | en |
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