博碩士論文 105453031 詳細資訊




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姓名 彭柏凡(Po-Fan Peng)  查詢紙本館藏   畢業系所 資訊管理學系在職專班
論文名稱 衍生性金融商品之客戶投資分析與建議-整合分群與關聯法則技術
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摘要(中) 金融機構在2007~2008兩年間,是一個不平靜的時代。2007年發生第一波的金融海嘯,次級房貸的問題開始爆發,造成了美國第五大投資銀行”貝爾斯登”宣布旗下兩檔次貸基金倒閉、虧損。最後該公司由摩根大通收購,但此時全世界只要有關於次貸的銀行或是金融機構無一倖免於這場金融災難,損失慘重。
觀看此時的台灣,台灣當時2007年7月底時的大盤走勢來到9,000多點,但是事隔不到半年間,2008年初已經跌破2,000多點,來到7,000多點。這樣的比例約市值蒸發了將近6兆台幣。
第二波金融風暴在2008年到來,當時美國最大銀行雷曼兄弟宣布破產,消息一出,全球股市像洩洪一樣,在2008年底,然而台灣大盤指數僅僅只有不到4,000點。然而這樣的危機造成了甚麼後續效應?其中,冰島破產、全球失業率高達5.75%創下歷史新高、花旗銀行的股價更是跌到只有1美元。台灣一些中小企業更是紛紛倒閉,百業蕭條。
回顧並綜觀2007~2008年間為何會有如此巨大的金融危機?其中有一個很重要的原因,是金融產業長期利用金融交易商品進行財務的槓桿操作,這樣的槓桿操作小則1、2倍的財物損失,嚴重了話可能到達100倍甚至200倍的財務虧損。
有鑑於此,本論文研究主要是希望透過金融海嘯間的交易資料,與非金融海嘯間的交易資料進行資料探勘,進而達到可以根據客戶的KYC(Know your Customer)指標,給予客戶較有依據參考的交易組合。本研究利用H商業銀行的衍生性金融商品在2007~2017十年間的交易資料進行分群中的K-means法則與關聯法則中的Apriori演算法進行資料探勘,藉由當局投資環境為變數,再依據客戶的風險屬性,給定選擇權交易的交易模式,即在高風險的投資環境於可選擇投資型的選擇權組合為權利金採用現金交易方式進行,相反的,若當局為較低風險的投資環境,可以建議客戶選取為權利金採用按交易比率的模式進行交易。期盼,本研究可以對於H商業銀行在往後進行選擇權交易時,給客戶較有力的推薦與建議。
摘要(英) Between 2007 and 2008, it was a difficult period to financial institutions. The first financial crisis began in 2007 when the subprime mortgage crisis had begun. As a result of the impact, the Bear Stearns Companies, the fifth-largest investment bank in the United States, disclosed that two subprime mortgage funds had failed and was subsequently sold to JPMorgan Chase. However, none of the banks or financial institutions involved with the subprime mortgage all around the world was spared from this financial crisis. The loss was disastrous.
Looking back then, Taiwan Stock Exchange Weighted Index (TWSE) came to 9000 points, but less than six months, in early 2008 it fell over 2000 points and ended at around 7000. In other words, it indicated that the market value had lost nearly six trillion New Taiwan dollar.
The second financial crisis took place 2008 when Lehman Brothers Holdings, the largest bank in the United States then, declared bankruptcy. The global stock market collapsed. At the end of 2008, Taiwan Stock Exchange Weighted Index (TWSE) had just around 4000 points. What after effect, however, has this kind of crisis has brought? For example, Iceland was bankrupt; global unemployment rate reached a new high, at 5.75%; even Citigroup stock fell to US$1. Several small or medium size organizations in Taiwan collapsed and all business fell in depression.
When we review and look into this tremendous global financial crisis, we have discovered one of the important factors. That is the financial operating leverage long taken by the financial industry with financial trading products. This method causes a minimum of 1 or 2 times of financial losses which can reach up to 100 or 200 times in a more serious situation.
The main purpose of this research paper is to conduct a Data Mining through transaction data during the financial crisis and non-financial crisis period, aiming to provide clients a more reference-based transaction portfolio, according to their KYC (Know Your Customer) index. This research uses the transaction data of derivatives from Commercial Bank H between 2007 and 2017 and conducts a Data Mining by means of K-means of Cluster Analysis Rule and Apriori algorithm of Association rule. It takes the current investment environment as variables and based on the risk attributes of clients, determines a transaction mode of Forward Option Deals. Therefore, in the investment environment where the risk is high, it is recommended to take Forward Option portfolio as a premium and to carry out a transaction in cash. On the other hand, clients are recommended to take the transaction ration for their premiums in a lower risk environment to carry out a transaction.
關鍵字(中) ★ 金融海嘯
★ 衍生性金融商品
★ 分群法則
★ 關聯法則
關鍵字(英) ★ K-means
★ Apriori
★ DataMining
★ WEKA
論文目次 摘要 III
ABSTRACT V
致謝 VII
第一章 緒論 1
1.1 研究動機與目的 1
1.2研究範圍及研究流程 2
1.3 論文架構 3
第二章 文獻探討 4
2.1 衍生性金融商品 4
2.1.1 衍生性金融商品定義 4
2.1.2 衍生性金融商品的分類 5
2.2選擇權契約 6
2.2.1 利率型選擇權 6
2.2.2匯率型選擇權 8
2.3資料探勘 8
2.3.1 資料探勘的來源 8
2.3.2 資料探勘 8
2.3.3 資料探勘的流程 10
2.4 分群法則 11
2.4.1 K-平均演算法(K-means) 11
2.5 關聯式規則法則 14
2.5.1 Apriori algorithm 14
第三章 研究方法 18
3.1 研究設計與架構 18
3.2 資料來源 20
3.3 分群法則(K-MEANS演算法) 23
3.4 關聯法則(APRIORI演算法) 24
第四章 研究結果與分析 26
4.1資料的前置處理 26
4.2 分群法則 26
4.3 關聯法則 38
4.4 實驗討論 41
第五章 研究結論與建議 43
5.1 研究結論 43
5.2 研究貢獻 43
5.3 未來研究方向與建議 44
參考文獻 45
英文文獻 45
中文文獻 48
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指導教授 蔡志豐 審核日期 2018-7-18
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