投资组合管理:基于多属性决策方法的股票排序

H. V. Dedania, Vipul Shah, R. Sanghvi
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引用次数: 14

摘要

投资者希望用代表不同行业的股票建立一个平衡的投资组合。许多研究者用不同的方法尝试了投资组合选择问题。其中许多方法将不同行业的公司放在一起考虑。然而,可以认为,影响公司增长的属性因行业而异。因此,将一家公司与同一行业的公司进行比较是明智的。从特定行业中选择股票有很多选择。在随机环境下,利用基于综合业绩的MADM方法,提出了一种股票排序方法。在许多MADM方法中,应用了SAW、AHP、TOPSIS和VIKOR。通常,在实现TOPSIS和VIKOR方法时考虑欧几里得距离(2范数)。在本文中,将该范数推广到p-范数,其中p- > - 1。该模型以13家在印度国家证券交易所上市的信息技术领域(IT)公司为对象进行了测试,并以13个标准作为公司的绩效指标。开发了MATLAB图形化界面系统,并给出了TOPSIS法和VIKOR法几种p值的计算结果。结果表明,在一定范围内,不同的p值对排序影响不大。此外,较高的p值对排序有不利影响。与同类股票相比,所提出的模型能够提供有关特定股票整体表现的更好信息。通过各种方法得到的结果清楚地区分了好公司和差公司,尽管确切的顺序略有不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Portfolio Management: Stock Ranking by Multiple Attribute Decision Making Methods
An investor would like to build a balanced portfolio with stocks representing different sectors. Several researchers have attempted the portfolio selection problem by different methods. Many of these methods consider companies of different sectors together. However, it can be argued that the attributes affecting the company’s growth vary for different sectors. Therefore, it is advisable to compare a company with the companies of the same sector. There are many options for the selection of a stock from a particular sector. A stock ranking method is proposed by using MADM methods based on overall performance under a stochastic environment. Of many MADM methods, SAW, AHP, TOPSIS, and VIKOR are applied. Usually, Euclidean distances (2-norm) are considered in the implementation of TOPSIS and VIKOR methods. In this work, this norm is generalized to p-norm, where p > 1. The model is tested for 13 companies in the field of Information Technology sector (IT) listed on National Stock Exchange in India and 13 criteria as performance indicators of a company. A MATLAB GUI system is developed and the results are obtained for several values of p in case of TOPSIS and VIKOR methods besides other methods. As the result indicates, the ordering is not much affected by different values of p in certain range. Moreover, higher values of p have adverse effect on the ordering. The proposed model is able to provide better information on the overall performance of a particular stock in comparison with its peers. The results obtained by various methods clearly separate good companies from inferior companies though the exact ordering slightly differs.
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