Loading...
|
Please use this identifier to cite or link to this item:
https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/111374
|
| Title: | A study on mining group stock portfolio by using grouping genetic algorithms |
| Other Titles: | 利用群組遺傳演算法探勘群組股票投資組合之研究 |
| Authors: | 林政邦;Lin, Cheng-Bon |
| Contributors: | 淡江大學資訊工程學系碩士在職專班
陳俊豪;Chen, Chun-Hao |
| Keywords: | 資料探勘;遺傳演算法;分組遺傳演算法;股票投資組合;分組問題;股票投資組合最佳化;data mining;Genetic Algorithms;Grouping genetic algorithms;Stock portfolio;grouping problem;stock portfolio optimization |
| Date: | 2016 |
| Issue Date: | 2017-08-24 23:51:09 (UTC+8) |
| Abstract: | 由於金融市場的多變性，金融資料探勘一直是一個吸引人且具有挑戰性的研究議題。例如，股價預測與股票投資組合探勘等都是金融資料探勘的範疇。其中，股票投資組合探勘問題不難理解是一個最佳化問題，故在過去幾十年，很多基於最佳化技術的演算法被提出來解決股票投資組合的問題。然而，現有方法的癥結點在於只提供一組股票投資組合。只提供一組投資組合，在現實應用上則會遇到許多問題。例如：投資者認為股票價格到達相對高點而不願意購買或該股票漲停無法買入。另外，探勘投資組合除了需滿足客觀的目標外，投資者亦常會有個人的主觀需求。客觀目標是指投資組合需要風險低且報酬高，而投資者預計購買股票家數與總投資金額等為投資者的主觀需求。
故本論文提出了二個演算來解決上述問題。首先，我們提出群組遺傳演算法為基礎的群組股票投資組合(Group stock portfolio, GSP)探勘技術，其目標為將n家股票分成K個群組。在群組股票投資組合中，同一個群組中股票是具有相似的特性。為達此目的，每個染色體是由三個部分組成，分別為群組、股票和股票投資組合。群組與股票部分表示如何將n家股票分成K個群組。每一群組在股票投資組合部分則利用兩實數表示是否為購買群組與購買單位。而評估函數則由染色體的群組平衡(Group balance)及投資組合的滿意度(Portfolio satisfaction)組成。群組平衡是用來測量每個群組中的股票家數是否相似，投資組合滿意度則是用於評估群組股票投資組合是否滿足客觀需求與投資者的主觀需求。由此，探勘所得的群組股票投資組合可以提供多種不同的投資組合給投資者。接著，為了提升群組的相似度與獲利穩定度，我們接著提出第二個演算法。在第二個演算法中，額外設計染色體的價格平衡(Price balance)與購買單位平衡(Unit balance)，主要目標分別為量測群組中股票的股價與購買單位是否相似。
最後，所提出的兩個方法透過台灣50中選出來的31家股票進行驗證，包含：群組投資組合分析、適合度函數對於群組投資組合的影響與投資報酬分析。實驗結果顯示，所提的兩個方法都可以探勘出高於標準利潤並提供有用的群組投資組合。
Due to variance of financial market, the financial data mining is always an attractive research issue and a real challenge to researches. For example, stock price prediction and stock portfolio mining are topics of financial data mining. It is easily to understand that the stock portfolio mining problem is an optimization problem. Hence, in the past decades, based on genetic algorithms, many approaches were proposed to deal with it. However, the problem of them is that only one stock portfolio is suggested. When only one stock portfolio is provided, some problems may happen in real application. For example, investors may think the price of the suggested stock is too high to buy, or the stock price reach the daily limit such that investor cannot buy it. Besides, stock portfolio mining should not only consider the objective criteria but also investors'' subjective criteria. The objective criteria are return on investment (ROI) and value at risk (VaR).
This thesis two approaches for solving the mentioned problems. Firstly, we propose a grouping genetic algorithm based approach for mining group stock portfolio (GSP) and its goal is to divide n stocks into K groups. Stocks in the same group means that they have similar properties. To achieve this goal, a chromosome consists of three parts. They are grouping part, stock part and stock portfolio parts. The grouping and stock parts are used to represent how n stock are divided into K groups. For each group in the stock portfolio part uses two real number to indicate whether it is purchased group and it’s purchased units. Each chromosome is then evaluated by the group balance and portfolio satisfaction. The group balance is used to make the number of stocks in groups can as similar as possible. The portfolio satisfaction is utilized to measure the satisfaction degree of objective and subjective criteria of a chromosome. As a result, the derived GSP can provide various stock portfolios to investors. Then, to improve the similarity of groups and profit stability, the second algorithm has been proposed. In second approach, the price balance and unit balance are designed to make the stock prices of stocks and purchased units in groups can as similar as possible.
At last, the two proposed algorithms are verified on 31 stocks which are selected from Taiwan 50 ETF, including the derived GSP analysis, impact of the fitness functions to the derived GSPs and the ROI of the derived GSPs. The experimental results show that the two proposed algorithms can mine GSPs that provide higher ROI than benchmark. |
| Appears in Collections: | [資訊工程學系暨研究所] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| index.html | | 0Kb | HTML | 144 | View/Open |
|
All items in 機構典藏 are protected by copyright, with all rights reserved.
|
