教師資料查詢 | 類別: 期刊論文 | 教師: 張應華 Ying-hua Chang (瀏覽個人網頁)

標題:Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems
學年100
學期1
出版(發表)日期2011/12/15
作品名稱Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems
作品名稱(其他語言)應用共演化策略動態式多準則評估解決股票交易問題
著者Chang, Ying-hua; Wu, Tz-ting
單位淡江大學資訊管理學系
出版者Philadelphia: Elsevier Inc.
著錄名稱、卷期、頁數Applied Mathematics and Computation 218(8), pp.4075-4089
摘要投資組合中風險和報酬是互相消長,獲取期望報酬的同時必須面臨相對的風險,投資環境的複雜性和決策者的準則動態改變,使得一般投資者不容易預測各投資標的風險和報酬,常因為不當的資金配置造成無法獲取較佳利潤。股票投資為多準則決策問題,但傳統解決多準則的理論有兩大缺點:一是準則無法隨著外在環境的改變而做適當的決策變動,二是各準則間權重的指派過於簡化而不切實際,不符合人類的思維模式。
1965年Rechenberg提出演化策略以解決實數參數最佳化的問題,改善傳統演算法只做單點搜尋且存在高可能性落入區域最佳解的缺點。1992年Hills提出共演化概念,即生物藉由與環境(多準則)的相互演化,不斷改良基因以利生存,並加速演化的進行。因此本研究整合共演化準則評估模式與演化策略於股票投資的多準則決策問題上,由於共演化式演化策略的進化過程有著自我修正的特性,其準則評估可以隨著時間、環境的改變而做適應性調整,符合人類的決策模式(評估準則的動態變化),解決傳統在求多準則決策問題上的缺點(各準則間權重的指派簡化)。
由共演化式演化策略找出最佳資金比例組合,可以協助投資者在有限的資金下依據最佳分配比例做適當的配置,以獲取最高報酬。在本研究實驗中與一般演化策略和類神經預測模型做比較的結果得知,共演化式演化策略優於一般演化策略,應優於類神經預測模型。共演化評估方式改善一般演算法所採用的簡化適應函數和傳統多準則決策問題的準則偏好權重不能隨著環境改變做適應性改變,使各準則依據各染色體變動而有所調整,而染色體也會隨著適應各準則做演化以達成更符合人類決策思考的模式。;Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes.
In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria).
Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns.
關鍵字Co-evolutionary model; Evolution strategies; Artificial neural network; Dynamic stock trading decision making; Optimization; 共演化模式; 演化策略; 類神經網路; 動態股票投資決策; 最佳化
語言英文(美國)
ISSN0096-3003
期刊性質國外
收錄於SCI
產學合作
通訊作者Chang, Ying-hua
審稿制度
國別美國
公開徵稿
出版型式紙本
相關連結
SDGs
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