本文選題：屬性約簡　切入點：屬性重復度　出處：《河北師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：資本市場是一個收益和風險并存的市場。在資本市場中投資者面臨的首要問題是如何有效地規(guī)避風險并獲取收益。為了規(guī)避風險,投資者,特別是機構投資者往往會選擇多種風險資產來構造投資組合。目前我國資本市場上的風險資產數以千計,如何將具有投資價值的風險資產遴選出來是構造投資組合并有效規(guī)避風險的核心問題。本文以股票為例,應用屬性約簡理論篩選出能夠有效區(qū)分風險資產的最小屬性集,并以之為依據,利用聚類分析技術對風險資產進行分類,從中選擇表現良好的類別包含的股票構造資產池進行投資,并對遴選出的股票的投資業(yè)績進行檢驗。全文共分五部分,主要內容如下:引言中介紹了選題背景及意義,梳理了國內外有關投資組合模型及屬性約簡算法的重要文獻,提出了研究內容、研究思路、重點難點及創(chuàng)新點等。第二部分首先介紹了屬性約簡的相關理論,在對以往屬性約簡算法進行對比分析的基礎上,針對屬性約簡結果易受到離散化效果的影響,提出了基于屬性重復度的改進的屬性約簡算法,該算法以不同樣本的屬性重復度為離散化依據,以不改變信息系統的不可分辨關系為屬性約簡依據,彌補了以往屬性約簡算法在這兩方面的缺陷。第三部分選擇滬深300指數中2005年前上市且現今仍在滬深300內的股票,詳細介紹了改進的屬性約簡算法的實現過程:首先按行業(yè)分類篩選出表現較好的股票,選擇反映股票收益及公司財務狀況的諸多指標,利用改進的屬性約簡算法約簡屬性,得到各行業(yè)的最小屬性集,并以此為依據對各行業(yè)股票進行聚類分析,選取表現良好的類別包含的股票樣本構建資產池。第四部分分別利用蒙特卡洛隨機模擬法及滾動均值-CVaR模型給出投資組合權重,對改進的屬性約簡算法遴選出的股票樣本的業(yè)績表現進行檢驗,兩種方法均證明本文提出的改進的屬性約簡算法遴選出的股票樣本具有明顯的優(yōu)勢。第五部分總結了本文的主要工作,并對未來研究做出展望。
[Abstract]:Capital market is a market in which income and risk coexist. In capital market, the most important problem investors face is how to avoid risk and gain income effectively. In particular, institutional investors tend to choose a variety of risky assets to construct their portfolios. At present, there are thousands of risky assets in China's capital market. How to select venture assets with investment value is the core problem of constructing investment portfolio and effectively avoiding risk. This paper takes stock as an example, applies attribute reduction theory to screen out the minimum attribute set which can effectively distinguish venture assets. On the basis of it, the paper classifies the risky assets by cluster analysis technology, selects the stock structure asset pool which has good performance, and tests the investment performance of the selected stocks. The paper is divided into five parts. The main contents are as follows: the introduction introduces the background and significance of the topic selection, combs the important literatures on portfolio model and attribute reduction algorithm at home and abroad, and puts forward the research contents and research ideas. The second part introduces the related theory of attribute reduction. Based on the comparison and analysis of the previous attribute reduction algorithms, the result of attribute reduction is easily influenced by the effect of discretization. An improved attribute reduction algorithm based on attribute repetition is proposed. The algorithm takes attribute repetition of different samples as the basis of discretization and takes the indiscernibility relation of information system as the basis of attribute reduction. In the third part, we choose the stocks listed in the CSI 300 index before 2005 and are still in the CSI 300 index. The implementation process of the improved attribute reduction algorithm is introduced in detail. Firstly, according to the classification of the industry, the better performance of the stock is selected, and many indexes reflecting the stock returns and the financial situation of the company are selected, and the improved attribute reduction algorithm is used to reduce the attributes. The minimum attribute set of each industry is obtained, and based on this, the stocks in each industry are analyzed by cluster analysis. In part 4th, the portfolio weights are given by Monte Carlo stochastic simulation and rolling mean-CVaR model. The performance of the stock sample selected by the improved attribute reduction algorithm is tested. Both methods prove that the improved attribute reduction algorithm proposed in this paper has obvious advantages in selecting stock samples. Part 5th summarizes the main work of this paper and makes a prospect for future research.
【學位授予單位】：河北師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.51

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本文編號：1618700