【摘要】：自美國注冊(cè)會(huì)計(jì)師查爾斯霍夫曼1998年開創(chuàng)性的將有豐富語義表達(dá)能力的可擴(kuò)展標(biāo)記語言技術(shù)應(yīng)用于財(cái)務(wù)報(bào)告，并逐漸形成了可擴(kuò)展商業(yè)報(bào)告語言（eXtensible Business Reporting Language，，簡稱XBRL）的概念以來，XBRL在全球范圍內(nèi)實(shí)踐和發(fā)展經(jīng)歷了十五載。 目前，XBRL財(cái)務(wù)報(bào)告分類標(biāo)準(zhǔn)（簡稱分類標(biāo)準(zhǔn)）的發(fā)展趨勢已經(jīng)由制定和實(shí)施逐步過渡到評(píng)價(jià)和改進(jìn)。制定和實(shí)施方面現(xiàn)有的研究已經(jīng)碩果累累，但是關(guān)于評(píng)價(jià)和改進(jìn)的研究還有不少問題一直有待完善。第一個(gè)問題是“分類標(biāo)準(zhǔn)的微觀結(jié)構(gòu)是什么？”，它是評(píng)價(jià)和改進(jìn)研究的基礎(chǔ)。現(xiàn)有的研究者認(rèn)為分類標(biāo)準(zhǔn)的最基本單元是財(cái)務(wù)信息元素，但是隨著維度建模方法的大量使用，分類標(biāo)準(zhǔn)的微觀結(jié)構(gòu)正在發(fā)生著變化，該問題的解決不僅有助于深化了財(cái)務(wù)信息元素理論，而且為評(píng)價(jià)分類標(biāo)準(zhǔn)的質(zhì)量指明了研究方向。第二個(gè)問題是“如何評(píng)價(jià)分類標(biāo)準(zhǔn)的質(zhì)量？”，它是評(píng)價(jià)和改進(jìn)研究的核心�，F(xiàn)有的研究者多是從分類標(biāo)準(zhǔn)的完整性角度來評(píng)價(jià)分類標(biāo)準(zhǔn)的質(zhì)量，而缺乏從創(chuàng)建和擴(kuò)展視角評(píng)價(jià)分類標(biāo)準(zhǔn)質(zhì)量。對(duì)創(chuàng)建和擴(kuò)展分類標(biāo)準(zhǔn)的不同模式進(jìn)行評(píng)價(jià)的目的是發(fā)現(xiàn)它們的優(yōu)點(diǎn)和缺點(diǎn)，為進(jìn)一步完善分類標(biāo)準(zhǔn)提供定性和定量的依據(jù)。第三個(gè)問題是“如何改進(jìn)分類標(biāo)準(zhǔn)的質(zhì)量？”，它是評(píng)價(jià)和改進(jìn)研究的成果。現(xiàn)有的研究者就通用層級(jí)分類標(biāo)準(zhǔn)的改進(jìn)提出過不少政策建議和實(shí)施框架，但是無論怎么改進(jìn)，都不可避免地要在分類標(biāo)準(zhǔn)的完整性和可比性之間做出取舍，這種巴別塔式的困境隨著行業(yè)層級(jí)分類標(biāo)準(zhǔn)的引入有所改觀，但現(xiàn)有研究尚缺乏創(chuàng)建行業(yè)層級(jí)分類標(biāo)準(zhǔn)的有效方法，這為分類標(biāo)準(zhǔn)的質(zhì)量改進(jìn)蒙上了一層陰影。一套可操作的創(chuàng)建行業(yè)層級(jí)分類標(biāo)準(zhǔn)的方法將有助于改進(jìn)分類標(biāo)準(zhǔn)的質(zhì)量，改善財(cái)務(wù)和其他經(jīng)濟(jì)信息在經(jīng)濟(jì)實(shí)體之間的相互交換，以滿足報(bào)告使用者對(duì)高質(zhì)量財(cái)務(wù)報(bào)告的要求。這三個(gè)基本問題是實(shí)踐中分類標(biāo)準(zhǔn)制定者、實(shí)例文檔創(chuàng)建者、財(cái)務(wù)信息監(jiān)管者乃至投資者都關(guān)注的問題，同時(shí)也是XBRL財(cái)務(wù)報(bào)告理論研究的焦點(diǎn)。 本文以微觀經(jīng)濟(jì)、財(cái)務(wù)會(huì)計(jì)，以及集合論、矩陣論、概率論和數(shù)理統(tǒng)計(jì)等理論為基礎(chǔ)，結(jié)合實(shí)證檢驗(yàn)的方法，圍繞評(píng)價(jià)與改進(jìn)分類標(biāo)準(zhǔn)質(zhì)量為主題展開研究。 第一個(gè)研究問題是“分類標(biāo)準(zhǔn)的微觀結(jié)構(gòu)是什么？”。采用逆向工程的研究方法，對(duì)分類標(biāo)準(zhǔn)中信息元素的遴選和歸集方法做了對(duì)比分析。從創(chuàng)建和擴(kuò)展的視角分析和比較了分類標(biāo)準(zhǔn)的微觀結(jié)構(gòu)，對(duì)分類標(biāo)準(zhǔn)的微觀結(jié)構(gòu)進(jìn)行了形式化描述。 第二個(gè)研究問題是“如何評(píng)價(jià)分類標(biāo)準(zhǔn)的質(zhì)量？”。從創(chuàng)建和擴(kuò)展分類標(biāo)準(zhǔn)的角度，分別構(gòu)造了衡量分類標(biāo)準(zhǔn)質(zhì)量的評(píng)價(jià)標(biāo)準(zhǔn)和指標(biāo)體系。關(guān)于分類標(biāo)準(zhǔn)的創(chuàng)建質(zhì)量，構(gòu)建了創(chuàng)建質(zhì)量測度，針對(duì)元組模式和維度模式，深化了該創(chuàng)建質(zhì)量模型，最后以財(cái)務(wù)報(bào)表附注的信息元素為樣本，對(duì)不同的創(chuàng)建模式（元組模式選取上交所上市公司分類標(biāo)準(zhǔn)，維度模式選取通用分類標(biāo)準(zhǔn)）進(jìn)行了創(chuàng)建效率、語義信息完整性和創(chuàng)建質(zhì)量的度量和評(píng)價(jià)。關(guān)于分類標(biāo)準(zhǔn)的擴(kuò)展質(zhì)量，構(gòu)建了擴(kuò)展質(zhì)量測度，針對(duì)直接擴(kuò)展模式和行業(yè)擴(kuò)展模式，深化了該擴(kuò)展質(zhì)量模型，最后以34家石油行業(yè)上市公司財(cái)務(wù)報(bào)告附注的信息元素為樣本，對(duì)不同的擴(kuò)展模式進(jìn)行了完整性、效率性和可比性的度量、評(píng)價(jià)和穩(wěn)健性檢驗(yàn)。 第三個(gè)研究問題是“如何改進(jìn)分類標(biāo)準(zhǔn)的質(zhì)量？”。從實(shí)務(wù)角度提出了一套可操作的創(chuàng)建行業(yè)層級(jí)分類標(biāo)準(zhǔn)的理論和方法，并以資本市場中占比最高的制造業(yè)為例，選取了153家制造業(yè)上市公司財(cái)務(wù)報(bào)告附注信息做為樣本，來創(chuàng)建該行業(yè)分類標(biāo)準(zhǔn)。 本文的主要研究結(jié)論如下： 1、提出了在不同的創(chuàng)建模式下分類標(biāo)準(zhǔn)的最基本單元不同的觀點(diǎn)。元組模式下財(cái)務(wù)信息元素是構(gòu)建分類標(biāo)準(zhǔn)的最基本單元；維度模式下結(jié)構(gòu)信息元素（表頭、軸成員和列報(bào)項(xiàng)目等）是構(gòu)建分類標(biāo)準(zhǔn)的最基本單元；由軸成員和列報(bào)項(xiàng)目信息元素構(gòu)造了影子財(cái)務(wù)信息元素。 2、從創(chuàng)建質(zhì)量方面看，分類標(biāo)準(zhǔn)的維度模式優(yōu)于元組模式；從擴(kuò)展質(zhì)量方面看，分類標(biāo)準(zhǔn)的行業(yè)擴(kuò)展模式優(yōu)于直接擴(kuò)展模式。創(chuàng)建質(zhì)量方面，在假定信息元素空間的單位創(chuàng)建成本相同，信息元素空間的單位披露收益相同，創(chuàng)建效率和語義信息完整性同等重要的前提下，總體上維度模式分類標(biāo)準(zhǔn)的創(chuàng)建質(zhì)量優(yōu)于元組模式分類標(biāo)準(zhǔn)；維度模式分類標(biāo)準(zhǔn)的創(chuàng)建效率優(yōu)于元組模式分類標(biāo)準(zhǔn)；元組模式分類標(biāo)準(zhǔn)的語義信息完整性優(yōu)于維度模式分類標(biāo)準(zhǔn)。擴(kuò)展質(zhì)量方面，不同擴(kuò)展模式的完整性、效率性和可比性在統(tǒng)計(jì)上有顯著的差異；行業(yè)擴(kuò)展模式在經(jīng)濟(jì)上有顯著的完整性、效率性和可比性優(yōu)勢。平均而言，行業(yè)擴(kuò)展模式的可比性比直接擴(kuò)展模式好四成左右，而且優(yōu)勢不會(huì)隨著信息元素集合的縮小而發(fā)生變化，反映出評(píng)價(jià)上市公司可比性的測度模型是穩(wěn)健的。 3、提出了一套創(chuàng)建行業(yè)分類標(biāo)準(zhǔn)的方法，創(chuàng)建了制造業(yè)分類標(biāo)準(zhǔn)。 在拓展信息元素空間理論的基礎(chǔ)上，以報(bào)告使用者決策有用性為目標(biāo)，提出了基于頻數(shù)遴選信息元素的方法，構(gòu)造了經(jīng)濟(jì)意義上確定頻數(shù)的可比性效用最優(yōu)理論模型和統(tǒng)計(jì)意義確定頻數(shù)的直觀方法。以制造業(yè)樣本為例，分別計(jì)算了整體信息元素的擴(kuò)展頻數(shù)、擴(kuò)展密度、查詢成本調(diào)節(jié)因子、平均可比性、修正可比性、累計(jì)擴(kuò)展元素?cái)?shù)量和累計(jì)擴(kuò)展元素比例等相關(guān)指標(biāo)，通過可比性效用最優(yōu)理論確定了最優(yōu)擴(kuò)展頻數(shù)為66；最后選取擴(kuò)展頻數(shù)大于等于66的信息元素集合，創(chuàng)建了制造業(yè)行業(yè)分類標(biāo)準(zhǔn)。 本文的創(chuàng)新點(diǎn)主要體現(xiàn)在以下三個(gè)方面： 1、拓展了分類標(biāo)準(zhǔn)財(cái)務(wù)信息元素理論，重構(gòu)了信息元素空間理論。引入集合論描述了分類標(biāo)準(zhǔn)的微觀結(jié)構(gòu)，比較了分類標(biāo)準(zhǔn)的創(chuàng)建模式和擴(kuò)展模式，拓展了現(xiàn)有的財(cái)務(wù)信息元素理論，為評(píng)價(jià)分類標(biāo)準(zhǔn)的質(zhì)量奠定了理論基礎(chǔ)。將信息元素的頻數(shù)引入到信息元素空間中，重新定義了信息元素空間，提出了頻數(shù)-密度空間和頻數(shù)-概率密度空間。將元素空間理論由一維擴(kuò)展到多維，將元素域擴(kuò)展到了元素-頻數(shù)域、頻數(shù)-密度域和頻數(shù)-概率密度域；建立了元素-頻數(shù)-密度-概率密度的函數(shù)映射關(guān)系；為從實(shí)務(wù)角度遴選信息元素，構(gòu)造行業(yè)分類標(biāo)準(zhǔn)奠定了理論基礎(chǔ)。 2、構(gòu)造了度量分類標(biāo)準(zhǔn)的創(chuàng)建和擴(kuò)展質(zhì)量測度，評(píng)價(jià)了分類標(biāo)準(zhǔn)的創(chuàng)建和擴(kuò)展質(zhì)量。創(chuàng)建質(zhì)量方面，基于成本收益原則和信息完整性構(gòu)建了創(chuàng)建質(zhì)量測度，嘗試從創(chuàng)建效率、語義信息完整性和整體創(chuàng)建質(zhì)量三個(gè)角度評(píng)價(jià)了分類標(biāo)準(zhǔn)的不同創(chuàng)建模式，為創(chuàng)建分類標(biāo)準(zhǔn)的實(shí)踐提供量化依據(jù)。擴(kuò)展質(zhì)量方面，與目前研究者們基本上采用信息匹配的方法來評(píng)價(jià)分類標(biāo)準(zhǔn)的完整性不同，本文引入頻數(shù)統(tǒng)計(jì)法評(píng)價(jià)分類標(biāo)準(zhǔn)的擴(kuò)展質(zhì)量；采用累計(jì)擴(kuò)展量來評(píng)價(jià)分類標(biāo)準(zhǔn)的完整性；采用累計(jì)復(fù)用量來評(píng)價(jià)分類標(biāo)準(zhǔn)的效率性；以累計(jì)復(fù)用量為基礎(chǔ)構(gòu)造了分類標(biāo)準(zhǔn)的可比性測度；在假設(shè)分類標(biāo)準(zhǔn)擴(kuò)展質(zhì)量中完整性、效率性和可比性同等重要的前提下，構(gòu)造了整體擴(kuò)展質(zhì)量測度；嘗試從完整性、效率性和可比性三個(gè)角度評(píng)價(jià)了分類標(biāo)準(zhǔn)的不同擴(kuò)展模式，為擴(kuò)展分類標(biāo)準(zhǔn)的實(shí)踐提供了量化依據(jù)。 3、構(gòu)造了統(tǒng)計(jì)意義上依指定概率和經(jīng)濟(jì)意義上依可比性效用最優(yōu)篩選信息元素、創(chuàng)建行業(yè)分類標(biāo)準(zhǔn)的方法。從實(shí)務(wù)披露角度出發(fā)，采用頻數(shù)法遴選分類標(biāo)準(zhǔn)的信息元素，構(gòu)造行業(yè)分類標(biāo)準(zhǔn)。對(duì)財(cái)務(wù)報(bào)告附注中的信息元素進(jìn)行頻數(shù)統(tǒng)計(jì)；通過轉(zhuǎn)換得到對(duì)應(yīng)頻數(shù)的復(fù)用密度和擴(kuò)展密度，通過單位化變換得到對(duì)應(yīng)頻數(shù)的概率密度分布；按一定的統(tǒng)計(jì)意義或經(jīng)濟(jì)意義確定頻數(shù)下限，篩選出大于等于該頻數(shù)下限的信息元素構(gòu)成行業(yè)分類標(biāo)準(zhǔn)。其中，統(tǒng)計(jì)意義通過信息元素的比例直接篩選信息元素，經(jīng)濟(jì)意義通過效用最優(yōu)來間接篩選信息元素。該方法彌補(bǔ)了實(shí)務(wù)法遴選信息元素方法上的不足。
[Abstract]:Since Charles Hoffman, an American CPA, pioneered the application of extensible markup language (XBRL) in financial reporting in 1998, and gradually formed the concept of eXtensible Business Reporting Language (XBRL), XBRL has been practiced and developed worldwide. For fifteen years.
At present, the development trend of XBRL Financial Reporting Classification Standard (XBRL Financial Reporting Classification Standard) has gradually changed from formulation and implementation to evaluation and improvement. Current researchers believe that the basic unit of classification criteria is financial information elements, but with the extensive use of dimension modeling methods, the microstructure of classification criteria is changing, and the solution of this problem not only helps deepen the theory of financial information elements, but also helps to deepen the theory of financial information elements. The second question is "how to evaluate the quality of classification criteria?", which is the core of evaluating and improving research. Most of the existing researchers evaluate the quality of classification criteria from the perspective of the integrity of classification criteria, but lack of evaluating the quality of classification criteria from the perspective of creation and expansion. The purpose of creating and expanding different models of classification standards is to discover their strengths and weaknesses and to provide qualitative and quantitative basis for further improvement of classification standards. Many policy suggestions and implementation frameworks have been put forward for improvement, but no matter how it is improved, it is inevitable to make a choice between the integrity and comparability of classification standards. This kind of Babel-style dilemma has been improved with the introduction of industry-level classification standards, but the existing research still lacks the effectiveness of establishing industry-level classification standards. A set of operational methods for creating industry-level classification standards will help to improve the quality of classification standards and improve the exchange of financial and other economic information between economic entities to meet the reporting users'requirements for high-quality financial reporting. This problem is the focus of XBRL financial reporting theory research, which is concerned by the classification standard setters, case document creators, financial information supervisors and even investors in practice.
Based on the theories of micro-economy, financial accounting, set theory, matrix theory, probability theory and mathematical statistics, and combined with the method of empirical test, this paper focuses on the evaluation and improvement of the quality of classification standards.
The first research question is "what is the micro-structure of classification criteria?". The method of selecting and collecting information elements in classification criteria is compared and analyzed by means of reverse engineering. The micro-structure of classification criteria is analyzed and compared from the perspective of creation and expansion, and the micro-structure of classification criteria is formally described. Say.
The second research question is "how to evaluate the quality of classification criteria?". From the angle of creating and expanding classification criteria, the evaluation criteria and index systems for evaluating the quality of classification criteria are constructed respectively. Finally, taking the information elements of financial statements as samples, we measure and evaluate the creation efficiency, semantic information integrity and creation quality of different creation modes (tuple mode selecting the classification standards of listed companies on the Shanghai Stock Exchange, dimension mode selecting the general classification standards). Finally, we take the information elements of 34 listed companies in the oil industry as samples to measure the completeness, efficiency and comparability of different expansion modes, evaluate and test their robustness.
The third research question is "how to improve the quality of classification standards?". From the practical point of view, this paper puts forward a set of operable theory and method of establishing industry hierarchical classification standards, and takes the manufacturing industry with the highest proportion in the capital market as an example, selects the financial report annotations of 153 manufacturing listed companies as samples to create the industry. Classification standard.
The main conclusions of this paper are as follows:
1. Put forward different viewpoints on the basic unit of classification standard under different creation modes. Financial information element is the basic unit to construct classification standard under tuple mode; structural information element (table head, axis member and presentation item, etc.) under dimension mode is the basic unit to construct classification standard; axis member and presentation item are the basic unit to construct classification standard. The information element constructs the shadow financial information element.
2. In terms of creating quality, the dimension model of classification standard is superior to tuple model; in terms of extending quality, the industry expansion model of classification standard is superior to direct expansion model. On the premise that information integrity is equally important, the quality of creating dimension schema classification standard is better than tuple schema classification standard on the whole; the efficiency of creating dimension schema classification standard is better than tuple schema classification standard; the semantic information integrity of tuple schema classification standard is better than dimension schema classification standard. There are significant statistical differences in the completeness, efficiency and comparability of the extended model; there are significant economic completeness, efficiency and comparability advantages of the industrial expansion model. The change shows that the measurement model for evaluating the comparability of listed companies is robust.
3, we put forward a set of methods to establish industry classification standard and set up a classification standard for manufacturing industry.
On the basis of expanding the theory of information element space and aiming at reporting the usefulness of users in decision-making, a method of selecting information elements based on frequency is proposed. The optimal theoretical model of comparability utility for determining frequency in economic sense and the intuitive method for determining frequency in statistical sense are constructed. Taking manufacturing sample as an example, the whole system is calculated separately. Based on the theory of comparability utility optimization, the optimal spread frequency is determined to be 66. Finally, the set of information elements whose spread frequency is greater than or equal to 66 is selected and created. The classification standard of manufacturing industry.
The innovation of this paper is mainly reflected in the following three aspects:
1. Extended the theory of financial information elements of classification standards and reconstructed the theory of information element space. Introduced set theory to describe the micro-structure of classification standards, compared the creation mode and expansion mode of classification standards, expanded the existing theory of financial information elements, laid a theoretical foundation for evaluating the quality of classification standards. Frequency is introduced into information element space, and the information element space is redefined. Frequency-density space and frequency-probability density space are proposed. Element space theory is extended from one dimension to multi-dimension, element domain to element-frequency domain, frequency-density domain and frequency-probability density domain. The function mapping relation of degree has laid a theoretical foundation for selecting information elements from practical point of view and constructing industry classification standards.
2. Constructed the creation and extension quality measures of the measurement classification standards, evaluated the creation and extension quality of the classification standards. Extended quality is different from the method of information matching that researchers basically use to evaluate the integrity of classification standards. This paper introduces frequency statistics to evaluate the extended quality of classification standards, and uses cumulative expansion to evaluate the integrity of classification standards. The efficiency of classification criteria is evaluated by cumulative reuse quantity; the comparability measure of classification criteria is constructed on the basis of cumulative reuse quantity; the whole extended quality measure is constructed on the premise that integrity, efficiency and comparability are equally important in the extended quality of classification criteria; and the integrity, efficiency and comparability are attempted This paper evaluates the different extension modes of classification standards from three perspectives, which provides a quantitative basis for the practice of extending classification standards.
3. Construct the method of selecting the information elements according to the specified probability and the comparability utility in the economic sense to create the industry classification standard. The multiplexing density and spreading density of corresponding frequencies are obtained by conversion, and the probability density distribution of corresponding frequencies is obtained by unit transformation; the lower limit of frequencies is determined according to certain statistical meaning or economic meaning, and the information elements larger than or equal to the lower limit of the frequency are selected to form the industry classification standard. This method makes up for the shortcomings of practical method in selecting information elements.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：F232

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