| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊管理學系 | zh_TW |
| DC.creator | 黃福晟 | zh_TW |
| DC.creator | Fu-Cheng Huang | en_US |
| dc.date.accessioned | 2022-6-10T07:39:07Z | |
| dc.date.available | 2022-6-10T07:39:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=108423040 | |
| dc.contributor.department | 資訊管理學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 全球金融市場中，依據國家發展程度、人均收入等不同因素，使得不同國家之指數 商品在面對市場中之波動時，會呈現不同程度之影響。而在市場分類之研究中，通常是 依國家發展相關指標進行分類，鮮少以各國指數之歷史數據進行漲跌幅計算後量化分析 以建立區分標準的方式。
為了建構出能夠量化各國指數歷史數據之漲跌幅以進行區分的方式，本研究參考 王仲麟(2012) 提及在不同國家市場之金融商品，例如指數或基金，當遭遇金融事件 衝擊時，所反映出來跌幅的震盪幅度有所差異，爾後的漲幅震盪程度亦是。因此參考 其概念計算金融事件或全球漲跌幅明顯之區段內漲跌幅之數據以進行市場特性分類。 在金融事件或明顯漲跌幅之區段篩選方式為透過參考先鋒整體市場指數基金(VTI) 指 數之趨勢，篩選出較具代表性之金融事件或漲跌幅現象的區段，因其資產規模大，能 夠完整反映美國股市之整體市場狀況。透過計算出個區段內之漲跌幅數據再透過 K means 演算法進行分群並透過散點圖呈現，藉此產生分類結果。
此外，王仲麟 (2012) 觀察到當面對市場呈現空頭趨勢之現象時，投資不同市場特 性之金融商品其獲得之績效會因其特性而不相同。為驗證此現象是否存在，本研究透 過回測兩個不同年份 (六年及八年) ，並搭配兩種策略概念，一種為定期定額概念之策 略，另一種為當市場呈現空頭趨勢進行投資並於多頭趨勢時停損概念之策略，以進行 交易策略回測並比較績效結果。透過相同回測年份不同策略之績效結果比較，觀察並 分析比較結果。藉此探討不同市場特性之金融商品搭配兩種交易策略概念之績效結果 比較是否會因其特性不同而產生差異。 | zh_TW |
| dc.description.abstract | In the global financial market, the index products of different countries will have different degrees of influence when facing market fluctuations according to different factors such as the country′s development level and per capita income. In the research of market classification, the classification is usually based on national development-related indicators. It is rare to use the historical data of various countries to conduct quantitative analysis to establish classification standards after calculating the rise and fall of the index.
In order to construct a way to quantify the rise and fall of historical data of various countries’ index, this research refers to the concept put forward by Wang Zhonglin (2012). He mentioned that when financial commodities in different countries′ markets, such as indexes or funds, are hit by financial events, the fluctuations in their declines are different, and the fluctuations in their subsequent gains are also different. Therefore, refer to his concept to calculate the data of financial events or the fluctuations in the segments with significant global fluctuations to classify the market characteristics. The selection method for segments of financial events or significant fluctuations is by referring to the trend of the Vanguard Total Market Index Fund (VTI) index to filter out more representative financial events or segments of fluctuations. Because of its large scale of assets, it can fully reflect the overall market conditions of the US stock market. By calculating the fluctuation data in each segment, it is grouped by the K means algorithm and presented through a scatter chart to generate the classification results.
In addition, Wang Zhonglin (2012) observed that when faced with a short market trend, the performance obtained by investing in financial commodities with different market characteristics will vary due to their characteristics. In order to verify the existence of this phenomenon, this research conducted back-testing in two different years (five years and seven years), and matched two strategic concepts. One is the strategy of Dollar-cost Averaging (DCA) concept. The other strategy concept is to invest when the market shows a short trend, and stop loss when the market shows a long trend. After comparing the performance results of different strategies in the same back-test year, we can observe and analyze the comparison results. This is to explore whether the comparison of the performance results of the two trading strategy concepts with financial commodities with different market characteristics will cause differences due to their different characteristics. | en_US |
| DC.subject | 程式交易 | zh_TW |
| DC.subject | ETF | zh_TW |
| DC.subject | K means 分群 | zh_TW |
| DC.subject | 漲跌幅比較 | zh_TW |
| DC.subject | 市場分類 | zh_TW |
| DC.subject | 績效比較 | zh_TW |
| DC.subject | program trading | en_US |
| DC.subject | Exchange traded funds (ETF) | en_US |
| DC.subject | K means clustering | en_US |
| DC.subject | price comparison | en_US |
| DC.subject | market classification | en_US |
| DC.subject | performance comparison | en_US |
| DC.title | 各國主要指數ETF與程式交易策略適配性之分析與分類研究 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Analysis and Classification Research on the Adaptability of Main Index ETFs and Program Trading Strategies in Various Countries | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |