智慧型策略學習模式運用在衍生性金融商品之投資--以選擇權交易為例

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/27785

Title:	智慧型策略學習模式運用在衍生性金融商品之投資--以選擇權交易為例 Intelligent Strategy Learning Model for Investment in Financial Derivatives -- Options Trading
Authors:	王瑀瑄
Contributors:	資訊管理研究所碩士在職專班
Keywords:	衍生性金融商品選擇權避險套利風險管理 Financial derivatives options hedging interest arbitrage risk management
Date:	2006
Issue Date:	2014-08-13 13:35:26 (UTC+8)
Abstract:	近年來衍生性金融商品在台灣的金融市場上的發展相當迅速，它包含有期貨、選擇權及權證等。對一般投資人而言選擇權及權證二者的風險相對的比期貨來得小許多，但是如何操作它們才能做到風險管理、找到避險方法、找到套利的機會等等，這都是我們在投資選擇權及權證時要認真去考量的問題。　　本研究針對選擇權的投資策略，提出一智慧型策略學習模式。首先，利用專業經驗先產生不同互補的買/賣投資策略及組合，經由邏輯判斷式(logical decision rules)及組合，作出適當的買賣決策，之後再運用模擬退火法(Simulated Annealing)做系統模擬，在運用投資策略進行買賣模擬的過程中，逐步調整參數權重值，當投資策略評估分數到達設定標準時，則進行買賣，並且在整個模擬過程中調整投資策略，尋求較佳的獲利機會。最後透過過去的歷史資料，模擬驗證智慧型策略學習模式，以達到投資的最佳獲利及風險管理。 In recent years, the development of financial derivatives, including futures, options and warrants, has been unfolding rapidly in the financial market of Taiwan. For majority of investors in general, the risks of options and warrants are much lower in comparison; however the ways to operate them in order to achieve risk management, or identifying hedging methods and interest arbitrage opportunities, are all consideration factors that we have to seriously reckon with when investing in options and warrants. 　　The present study has proposed an intelligent strategy learning model aimed at the investment strategy of options. First, employ the different complementary buy/sell investment strategies and combinations of professional experience generated, and through logical decision rules and combination, to derive appropriate buy/sell strategies, subsequently, apply Simulated Annealing to conduct system simulation, and during the course of using the investment strategies to carry out the buy/sell simulation, the parameters weighting is progressively adjusted. When the investment strategy assessment grades reached the predetermined levels, then buying/selling is carried out, and the investment strategy is adjusted throughout the entire simulation process, to seek out relatively better profit opportunities. Finally, simulate and verify the intelligent strategy learning model through past historical data, to achieve optimal investment profits and risk management.
Appears in Collections:	[Department of Information Management & Graduate Institute of Information Management] Thesis

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