本篇論文以固定比例投資組合保險 (CPPI) 的架構為基礎，遺傳程式規劃 (Genetic Programming) 為工具，提出一個可以動態調整風險偏好乘數的投資策略，並將之命名為 Dynamic Proportion Portfolio Insurance (DPPI)。 由於CPPI中的風險偏好乘數是依據投資人對市場的期望而自行訂定的固定常數，雖然它輔助投資人簡易地瞭解要配置多少比例的資金於風險性資產上，但若投資者對市場趨勢的判斷是錯誤的話，極可能因採用不適當的風險偏好乘數而造成日後的投資失利，另外當市場波動變得劇烈時，一開始採用大乘數的投資策略則可能失去保險的功能。 我們經由參考過去文獻，蒐集各種與市場波動相關的因子，當作遺傳程式規劃的輸入變數，由遺傳程式規劃隨著因子數值的變化學習計算出適當的投資風險偏好乘數，使投資人能夠因應市場波動更彈性地配置資金於風險性資產上。實驗結果證明動態調整風險偏好乘數的DPPI其平均投資績效的確優於使用固定常數的CPPI。 另外由於遺傳程式規劃每次所建構的算式樹皆不相同，大部分的統計方法並不適用於探討所採用的終端節點對產生結果變異的影響，本篇論文以主成份分析探討所採用的各個市場波動因子對風險偏好乘數變異的影響程度，實驗結果發現以無風險利率較其他因子略勝一籌。 This thesis proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. It helps investor easily to understand how to allocate the capital among risky and risk-free assets and straightforward to imple-ment. The risk multiplier in CPPI is predetermined by the investor’s view-point and fixed to the end of investment duration. However, since the market changes constantly, we think that the risk multiplier should change accordingly. When the market becomes volatile, the predetermined large risk multiplier will lead to loss of insurance and DPPI may solve this kind of problem. This research identifies factors re-lating to market volatility. These factors are built into equation trees by genetic programming. We collected five stocks of American companies’ financial data and the market information of New York Stock Exchange as input data feeding genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. Because the equation trees are all different, there is no method to ana-lyze the factor contributions to the results of the risk multiplier. We use principal component analysis to see the effect of factors, and the experi-mental results show that among the market volatility factors, risk-free rate influences the variances of risk multiplier most.
