基于序化机理的稳健型股票价值投资研究
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摘要
2007年源于金融衍生产品过度膨胀的全球金融危机爆发,继而引发了人们对股票投资决策的重新审视。基础资产的有效选择是否更为重要?股票选择策略究竟能否战胜市场?面对日益重要但风险共生的股票市场,这些问题成为学术界、实务界共同关注的重点命题,而“股票选择决策”也就成为“股票投资决策”关键的基础研究问题。在众多的股票选择策略中,价值投资最受关注。基于经典的价值投资理论,在现实的投资实践中,股票可以被分为：价值型股票、成长型股票。从现有研究来看,大量证据表明价值型股票具有价值溢价效应,能够产生超额收益。然而,关于价值溢价效应的争论却从未停止,或者有证据显示基于经典价值特征的价值溢价效应呈现不稳定性,或者认为价值型股票为存在危机因素的公司。可以看出,不管何种争论都表明基于经典价值投资理论的价值型股票投资模式并不具备“稳健型”特征。显然,面对当前全球实体经济衰退、资本市场风险加剧的背景,“稳健型”股票价值投资模式的研究具有重要的理论意义和现实的应用价值。
对于决策者来说,股票的市场业绩是其进行股票选择决策时关注的核心目标。然而,在实际的股票市场中,影响股票市场业绩的因素不胜枚举。那么,什么样的影响因素才是最本质的?什么样的模式才是最稳健的?本文认为企业经济运行效率作为企业价值的根本,才是股票市场业绩影响的本质因素；而具有“公司经营业绩与股票市场业绩一致趋优”特性的股票才是最为“可靠”和“稳健”的。
基于“稳健型”特征的考量,本文以“会计信息：公司经营业绩的全面度量”为基础,以“稳健特性：公司经营业绩与股票市场业绩一致趋优”为核心,以“序化求解：稳健型股票价值投资模式”为脉络建立了“稳健型股票价值投资的理论框架”。本质上来看,稳健型股票价值投资是典型的多属性排序决策问题。为了更为全面、有效的刻画数据特征,本文以区间数据为基本的数据表示形式；为了有效的建立序化求解模型,本文着眼于“稳健性”、“局部性”与“全局性”三个特性建立了多属性排序决策的序化机理；为了有效的开展序化问题求解,关键是要解决如下三个核心科学问题：
·如何建立有效的区间数据全序化方法?
·如何建立科学的特征评价方法?
·如何进行区间序决策表的关键特征选择?
基于稳健型股票价值投资理论框架,围绕上述三个核心科学问题,本文在粗糙集理论框架下,以区间序信息系统为稳健型股票价值投资决策问题的基本描述框架,建立了“稳健型股票价值投资排序决策方法”,获得的主要研究成果和创新概括如下：
1、基于盈利能力、现金流量能力、营运能力、发展能力和偿债能力五个方面的会计信息全面度量公司经营业绩；基于“公司经营业绩与股票市场业绩一致趋优”特性,建立了稳健型股票价值投资模式,进一步丰富和发展了价值投资理论。
2、基于粗糙集理论框架下的优势关系,考虑人类“局部序化”的认知模式,引入优势度排序作为优先级准则；基于“全局性”序化特性,着眼于更为精细的刻画对象之间的优劣程度,提出了有向距离指数排序,并将其作为次优先级准则,进而建立了区间数据两级排序决策的全序化方法。
3、面向区间序信息系统,考虑信息增益的补集特性,提出了区间序互补熵、区间序互补互信息和区间序互补条件熵等系列概念,建立了区间序信息系统不确定性表示的熵度量体系,为稳健型股票价值投资排序决策提供了有效的特征选择与特征评价方法。
总体来看,本文基于序化机理开展了稳健型股票价值投资决策的理论与技术研究。以上证180指数成分股为样本的实证研究表明：所提出的稳健型股票价值投资模式可以获得稳定的超额收益,进而验证了稳健型股票价值投资模式的有效性和稳健型股票价值投资排序决策方法的有效性。更具一般性的,本文特征选择、特征评价、全序化建模方法的集成,为风险厌恶型决策者提供了稳健型的全序化技术,也进一步丰富和发展了人工智能决策的理论和方法。
The global financial crisis in2007derived from excessive expansion of financial derivative products causes people to re-examine the stock in-vestment decisions. Is the effective choice for the underlying assets more important? Can stock selection strategy beat the market? As the stock market is becoming increasingly important but more risky as well, these issues has become the major focus of academics and practitioners, and de-cision making of stock selection has naturally become a key basic problem of stock investment decisions. Among the large number of stock investing strategies, value investing attracts most attention. Based on the clas-sic value investing theory, the stocks are generally categorized as value stocks and growth stocks in the practice of real investment. The exist-ing researches has provided us with a lot of evidence suggesting that value stocks have value premium effect which can generate excess returns. How-ever, the debate on the value premium effect has never stopped. There is evidence showing that value premium effect based on the classical value characteristics is instable, or that value stocks are the companies with risk factors. It can be seen all the controversy shows that value stock invest-ment model based on the classic value investing theory does not have the characteristics of being robust. Obviously, in the background of the reces-sion of global real economy and exacerbated capital market risk, study on robust stock value investing model has important theoretical significance and practical value.
For decision-makers, the stock performance are their major concern when selecting stocks. However, in real stock market, the factors affecting the stock performance are too numerous to mention. So, what kind of factors is the most essential? And what kind of mode is the most robust? This dissertation holds that the enterprises'economic operation efficiency, as basis of business value, is the essential factor of stock performance, and stocks excellent in both operation and stock market are the most "reliable" and "robust" ones.
Taking the features of being "robust" into consideration, this disser-tation chooses the accounting information:a comprehensive measure of companies'performance as basis, robust features:excellent performance of operating and stock as the core, and ordered solving:robust stock value investing model as context to establish the theoretical framework of robust stock value investing. Essentially, robust stock value investing is a typical problem of multiple attribute ranking decision making. To ensure a more comprehensive and effective portrait of the data characteristics, this dissertation uses interval data as the basic representation of data. In order to effectively establish the ordered solving model, this disserta-tion focuses on the three characteristics-robust, localized and overall-to establish the ordered mechanism of multiple attribute ranking decision making; for the purpose of effectively carrying out the ordered solving, it is crucial to address the following core issues:
●How to build effective complete rank method of interval data?
●How to establish scientific method of feature evaluation?
●How to select the key features of interval ordered decision table?
On the basis of the theoretical framework of robust stock value in-vesting, focusing on the three core scientific issues mentioned above, this dissertation uses interval ordered information system as description frame-work of decisions problems of robust stock value investing under the frame-work of rough set theory, and attains methods of robust stock value invest-ing ranking decision. The main results and innovations can be summarized as follows:
1. Based on five aspects of accounting information including prof-itability, cash flow capacity, operating capacity, development capacity and debt-paying ability, a company's operation performance can be measured comprehensively. Based on the feature of excellent both in operation and stock, robust stock value investing model can be built, which further en-riches and develops the theory of value investing.
2. Based on the dominance relation under the framework of rough set theory, this dissertation considers human cognitive model of local order-ing, and introduces superiority degree ranking as priority criterion. Based on the overall ordering characteristic, and focusing on a more precise por-tray of preferability degree among objects, this dissertation introduces the directional distance index, and thereby establishes the complete rank methods of interval data's two-grade ranking decision.
3. Facing the interval ordered information system, and considering the complementary set feature of information gain, this dissertation pro-poses concepts of complementary entropy, complementary mutual infor-mation and complementary conditional entropy of interval ordered, estab-lishes the entropy measure system of uncertainty representation of interval ordered information system, and provides an effective feature selection and feature evaluation method for robust stock value investing ranking decision.
Overall, based on the ordered mechanism, this dissertation carries out the research for robust stock value investing decision from theoretical and technical perspectives. The empirical study on Shanghai stock exchange180index components suggests that:the robust stock value investing model can obtain a stable excess return, which verifies its validity and robustness of robust stock value investing ranking decision. More gener-ally, the integration of feature selection, feature evaluation, and modeling method of complete rank provides a robust complete rank for risk-averse decision-makers, and further enriches the theories and methods of artifi-cial intelligence in decision-making.
对于决策者来说,股票的市场业绩是其进行股票选择决策时关注的核心目标。然而,在实际的股票市场中,影响股票市场业绩的因素不胜枚举。那么,什么样的影响因素才是最本质的?什么样的模式才是最稳健的?本文认为企业经济运行效率作为企业价值的根本,才是股票市场业绩影响的本质因素；而具有“公司经营业绩与股票市场业绩一致趋优”特性的股票才是最为“可靠”和“稳健”的。
基于“稳健型”特征的考量,本文以“会计信息：公司经营业绩的全面度量”为基础,以“稳健特性：公司经营业绩与股票市场业绩一致趋优”为核心,以“序化求解：稳健型股票价值投资模式”为脉络建立了“稳健型股票价值投资的理论框架”。本质上来看,稳健型股票价值投资是典型的多属性排序决策问题。为了更为全面、有效的刻画数据特征,本文以区间数据为基本的数据表示形式；为了有效的建立序化求解模型,本文着眼于“稳健性”、“局部性”与“全局性”三个特性建立了多属性排序决策的序化机理；为了有效的开展序化问题求解,关键是要解决如下三个核心科学问题：
·如何建立有效的区间数据全序化方法?
·如何建立科学的特征评价方法?
·如何进行区间序决策表的关键特征选择?
基于稳健型股票价值投资理论框架,围绕上述三个核心科学问题,本文在粗糙集理论框架下,以区间序信息系统为稳健型股票价值投资决策问题的基本描述框架,建立了“稳健型股票价值投资排序决策方法”,获得的主要研究成果和创新概括如下：
1、基于盈利能力、现金流量能力、营运能力、发展能力和偿债能力五个方面的会计信息全面度量公司经营业绩；基于“公司经营业绩与股票市场业绩一致趋优”特性,建立了稳健型股票价值投资模式,进一步丰富和发展了价值投资理论。
2、基于粗糙集理论框架下的优势关系,考虑人类“局部序化”的认知模式,引入优势度排序作为优先级准则；基于“全局性”序化特性,着眼于更为精细的刻画对象之间的优劣程度,提出了有向距离指数排序,并将其作为次优先级准则,进而建立了区间数据两级排序决策的全序化方法。
3、面向区间序信息系统,考虑信息增益的补集特性,提出了区间序互补熵、区间序互补互信息和区间序互补条件熵等系列概念,建立了区间序信息系统不确定性表示的熵度量体系,为稳健型股票价值投资排序决策提供了有效的特征选择与特征评价方法。
总体来看,本文基于序化机理开展了稳健型股票价值投资决策的理论与技术研究。以上证180指数成分股为样本的实证研究表明：所提出的稳健型股票价值投资模式可以获得稳定的超额收益,进而验证了稳健型股票价值投资模式的有效性和稳健型股票价值投资排序决策方法的有效性。更具一般性的,本文特征选择、特征评价、全序化建模方法的集成,为风险厌恶型决策者提供了稳健型的全序化技术,也进一步丰富和发展了人工智能决策的理论和方法。
The global financial crisis in2007derived from excessive expansion of financial derivative products causes people to re-examine the stock in-vestment decisions. Is the effective choice for the underlying assets more important? Can stock selection strategy beat the market? As the stock market is becoming increasingly important but more risky as well, these issues has become the major focus of academics and practitioners, and de-cision making of stock selection has naturally become a key basic problem of stock investment decisions. Among the large number of stock investing strategies, value investing attracts most attention. Based on the clas-sic value investing theory, the stocks are generally categorized as value stocks and growth stocks in the practice of real investment. The exist-ing researches has provided us with a lot of evidence suggesting that value stocks have value premium effect which can generate excess returns. How-ever, the debate on the value premium effect has never stopped. There is evidence showing that value premium effect based on the classical value characteristics is instable, or that value stocks are the companies with risk factors. It can be seen all the controversy shows that value stock invest-ment model based on the classic value investing theory does not have the characteristics of being robust. Obviously, in the background of the reces-sion of global real economy and exacerbated capital market risk, study on robust stock value investing model has important theoretical significance and practical value.
For decision-makers, the stock performance are their major concern when selecting stocks. However, in real stock market, the factors affecting the stock performance are too numerous to mention. So, what kind of factors is the most essential? And what kind of mode is the most robust? This dissertation holds that the enterprises'economic operation efficiency, as basis of business value, is the essential factor of stock performance, and stocks excellent in both operation and stock market are the most "reliable" and "robust" ones.
Taking the features of being "robust" into consideration, this disser-tation chooses the accounting information:a comprehensive measure of companies'performance as basis, robust features:excellent performance of operating and stock as the core, and ordered solving:robust stock value investing model as context to establish the theoretical framework of robust stock value investing. Essentially, robust stock value investing is a typical problem of multiple attribute ranking decision making. To ensure a more comprehensive and effective portrait of the data characteristics, this dissertation uses interval data as the basic representation of data. In order to effectively establish the ordered solving model, this disserta-tion focuses on the three characteristics-robust, localized and overall-to establish the ordered mechanism of multiple attribute ranking decision making; for the purpose of effectively carrying out the ordered solving, it is crucial to address the following core issues:
●How to build effective complete rank method of interval data?
●How to establish scientific method of feature evaluation?
●How to select the key features of interval ordered decision table?
On the basis of the theoretical framework of robust stock value in-vesting, focusing on the three core scientific issues mentioned above, this dissertation uses interval ordered information system as description frame-work of decisions problems of robust stock value investing under the frame-work of rough set theory, and attains methods of robust stock value invest-ing ranking decision. The main results and innovations can be summarized as follows:
1. Based on five aspects of accounting information including prof-itability, cash flow capacity, operating capacity, development capacity and debt-paying ability, a company's operation performance can be measured comprehensively. Based on the feature of excellent both in operation and stock, robust stock value investing model can be built, which further en-riches and develops the theory of value investing.
2. Based on the dominance relation under the framework of rough set theory, this dissertation considers human cognitive model of local order-ing, and introduces superiority degree ranking as priority criterion. Based on the overall ordering characteristic, and focusing on a more precise por-tray of preferability degree among objects, this dissertation introduces the directional distance index, and thereby establishes the complete rank methods of interval data's two-grade ranking decision.
3. Facing the interval ordered information system, and considering the complementary set feature of information gain, this dissertation pro-poses concepts of complementary entropy, complementary mutual infor-mation and complementary conditional entropy of interval ordered, estab-lishes the entropy measure system of uncertainty representation of interval ordered information system, and provides an effective feature selection and feature evaluation method for robust stock value investing ranking decision.
Overall, based on the ordered mechanism, this dissertation carries out the research for robust stock value investing decision from theoretical and technical perspectives. The empirical study on Shanghai stock exchange180index components suggests that:the robust stock value investing model can obtain a stable excess return, which verifies its validity and robustness of robust stock value investing ranking decision. More gener-ally, the integration of feature selection, feature evaluation, and modeling method of complete rank provides a robust complete rank for risk-averse decision-makers, and further enriches the theories and methods of artifi-cial intelligence in decision-making.
引文
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