臺灣博碩士論文加值系統

本研究使用了遺傳演算法，在不考慮選擇權波動率的情況下，透過基因編碼演化的機器學習方式，以發掘臺指選擇權這項衍生性金融商品的交易策略，並使用時間交叉驗證的方式來證明使用遺傳演算法這個人工智慧方式所訓練出來的交易模型確實可以在臺指選擇權這項衍生性金融商品上得到不錯的獲利效果，透過此研究我們希望能更進一步推動AI於金融投資領域的進展。

In this study, Genetic Algorithm (GA) was employed to search for effective trading strategies of financial derivatives such as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Options. Without considering the volatility of the options, through the GA we intend to investigate how the trading strategies developed by the GA are able to achieve much higher profits on TAIEX Options compared with the buy-and-hold strategy. We provide several ways to examine the experimental results, including temporal validation, cumulative return performance on investment, as well as confusion matrix. These examinations indeed demonstrate that the GA-based methodology is effective and we expect the proposed methodology to advance the current state of artificial intelligence in the application of investment.

摘要
致謝
圖目錄
表目錄
1. 導論
1.1 研究背景
1.2 研究目的
2. 文獻探討
2.1 臺指選擇權相關文獻
2.2 選擇權介紹
2.3 選擇權類型
2.4 選擇權報價方式
2.5 選擇權的價內、價外
2.6 選擇權的損益計算
2.7 Black-Sholes 選擇權評價公式
2.8 人工智慧相關文獻
2.9 遺傳演算法與衍生性金融商品之相關研究文獻
3 研究方法
3.1 資料來源
3.2 遺傳演算法
3.2 改良型的基因編碼方式
3.3 適應性函數
3.4 研究結果
3.5 計算適應函數值並進行選擇及淘汰
3.6 交配
3.7 突變
4. 時間驗證
4.1 時間驗證法
4.2 演算法迭代設定
4.3 驗證結果
5. 結論與建議
6. 參考文獻
附錄A

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