多區間群組遺傳為基礎之群組股票投資組合最佳化技術

Multi-period GGA-based Grouping Stock Portfolio Optimization Techniques

鄭嘉元(Chia-Yuan Cheng)

指導教授 ： 陳俊豪

淡江大學/工學院/資訊工程學系碩士班/碩士(2019年)

https://doi.org/10.6846/TKU.2019.00285

摘要

由於金融市場對我們生活中的事件具有敏感性，投資組合選擇將會是一個優化問題。過去的許多研究已經提出了許多最佳化方法來推導出不同類型的投資組合，而其中一種即是群組股票投資組合。群組股票投資組合可以在投資者做決策時，提供更有用的投資組合。然而，其中的大多數方法都專注於單時期投資組合的最佳化。在本文中，我們首先提出了一個多時期群組股票投資組合的最佳化框架。根據所提出的框架，我們提出了一種基於分組遺傳的算法，以獲得多時期的群組股票投資組合。通過四個部分：股票歸屬、股票分組、組可用性和權重，將多時期群組股票投資組合編碼到染色體之中。算法中的每條染色體將通過三個因子來進行適合度評分，而這些因子分別為：累進回報因子，累進安全性因子和投資風格因子。除此之外，一個非支配集合池將在演化過程中不斷地被更新與維護，藉此增加母體的多樣性。維護此非支配集合池亦可以在演化計算的最終階段提供更高品質的解。然而，如果算法中待計算的股票數量龐大時，所提出的算法對於多時期群組股票投資組合的最佳化計算將會變得非常耗時。為了增強算法使其減輕運算耗時的問題，一個新的染色體編碼方式亦被設計並提出。本論文亦在三個金融數據集上進行實驗，以顯示所提出方法的優點。

關鍵字

群組遺傳演算法 ； 群組股票投資組合 ； 分組問題 ； 多區間投資組合最佳化問題 ； 多區間群組股票投資組合

並列摘要

Portfolio selection is an optimization issue due to the sensitivity of financial markets to the occurrences around our lives. Lots of optimization methods have been proposed to derive different types of portfolios. A type of them is the group stock portfolio which can provide a more useful portfolio for investors making decisions. However, most of them focus on the single-period portfolio optimization. In this thesis, we present a multi-period group stock portfolio optimization frame-work firstly. Then, based on the presented framework, we thus propose an algorithm to obtain a multi-period group stock portfolio based on using the grouping genetic algorithm is proposed. It encodes a multi-period group stock portfolio into a chromosome by the belonging, grouping, group availability and weight parts. Every chromosome is then evaluated by three factors: the accumulated return, the accumulated safety, and the investment style factors. A front pool which is a set of non-dominated solutions is also maintained to enhance the diversity of the population. It also leads to a higher quality of discovered solutions in the final stage. Be-cause the proposed algorithm is time-consuming for optimizing a multi-period group stock portfolio when the number of stocks is large, the enhanced algorithm is then designed to solve it by a new chromosome encoding method. Experiments were also conducted on the three financial datasets to show the merits of the pro-posed approach.

並列關鍵字

Grouping genetic algorithm ； group stock portfolio ； grouping problem ； multi-period portfolio optimization problem ； multi-period group stock portfolio

參考文獻

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