Review Of Global Industry Classification

This paper introduces the financial market implied industry classification standard. Besides current industry classifications we propose a spectral clustering based quantitative methodology. The main drawback of current standards come from their qualitative classification techniques which can be eliminated in this purely mathematical concept. Calculating the market implied clusters and compering them with global industry classification explores that market implied classification provides better statistical results. However it also turns out that different clustering techniques provide similar classifications, moreover, both methods determine “Real Estate” as a cluster. INTRODUCTION Practitioners are using Global Industry Classification Standards (GICS) to keep track of sector based moves. It is widely accepted that firms with similar business activities have similar macro and micro factor sensitivities. Thus, building portfolios with names from the same industry lets us to eliminate specific risk. Sector based bundling gives the opportunity to tailor the macro factor exposure, understand the contamination of macro level shocks and determine regulatory standards. Moreover, it also helps to identify firms which under or outperform the industry hence it helps to separate systematic and non-systematic risks. Asset managers use industry classification standards for their asset allocation and risk management strategies. Moreover, industry classification is also important for regulators, governments and labour economists to have a deeper understanding of the given state of the economy and be able to implement policies. GICS is the mainly used classification. The methodology was developed by Morgan Stanley Capital International Inc. (MSCI) and Standard and Poor's (S&P). The categorization combines quantitative and qualitative techniques (MSCI 2015.) to obtain a market oriented economically thoughtful classification standard. In this article we introduce a spectral clustering based (Nagy and Ormos 2016.) purely quantitative model to unveil the Financial Market Implied Classification (FMIC). Using daily closing prices, we show that the normalized modularity cut (Bolla 2011.) implied clusters and GICS are highly comparable. Moreover, standard Capital Asset Pricing Model based regressions give further evidences for using spectral clustering. The results show that GICS can be looked on as an approximation of the spectral clustering based classification. In addition, the clusters support the latest review of GICS in which Real Estate was added as distinct asset class. The article structured as follows: Section 2 is a brief overview of industry classification standards. In Section 3 we introduce our spectral clustering based concept. In Section 4 we present the market implied clusters, compare them with groups given by GICS and carry out regression analysis to investigate the risk explanation power of FMIC. Section 5. summarizes the article. INDUSTRY CLASSIFICATION STANDARDS Investors are using different classification techniques from long ago to identify industries and characterize their specific behaviour. In 19th century all the market participants had their own classifications which were mainly used to evaluate risk, understand the macro factor sensitivities and build investment strategies (Vose 1916). However, there was no officially accepted framework. Hence, it was unmanageable to aggregate sector level sensitivities and implement economic policies. Standard Industrial Classification After the Great Depression in 1937, the US Central Statistical Board (Kolesnikoff 1940.) unified the different industry classifications and established the Standard Industry Classification. SIC was the main Proceedings 32nd European Conference on Modelling and Simulation ©ECMS Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen (Editors) ISBN: 978-0-9932440-6-3/ ISBN: 978-0-9932440-7-0 (CD) guideline of the Federal Government, banks and investors during the 20th century, in addition, the U.S. Securities and Exchange Commission (SEC) still uses it to its industry classification. The system had to be revised several times, in 1958, 1963, 1967, 1972, 1977 and 1987, but it still had several limitations. The main drawbacks of SIC are that it is designed for the economy of early 20th century, it is hard to identify new groups and follow the changes of global economy. North American Industrial Classification System NAICS is and industry classification standard which was designed to response to the increasing criticism of SIC (Executive Office of the President Office of Management and Budget. 2017). In 1980s the rapid changes of world economy forced the Office of Management and Budget (OMB) to overhaul SIC. Thus in 1997 the Economic Classification Policy Committee (ECPC) in Nacional the cooperation with Mexico's Instituto de Estadística, Geografía e Informática (now the Instituto Nacional de Estadística y Geografía, INEGI) and Statistics Canada suggested a new industry classification system which supplanted SIC. They agreed in that NAICS should be reviewed in every five years to reflect economic changes. The current form of NAICS defines 20 sectors and 1,057 industries. The classification standard is used for various administrative, regulatory, and taxation purposes. Global Industry Classification Standards Besides the SIC and NAICS in 1999 Morgan Stanley Capital International Inc. and Standard and Poor's created the widely used Global Industry Classification Standard. The primary goal of S&P was to enhance its business with introducing sector indices of S&P 500 index. In order to achieve this ambition an adequate industry categorization rule was needed. The classification has a market oriented nature, which incorporates quantitative and qualitative techniques. As GICS is used in the sub-index decomposition of S&P 500 thus it should follow all the changes of the market, hence it has to be reviewed at least annually. At first GICS introduced 10 sectors, 23 industry groups, 59 industries and 123 sub-industries. The classification was revised several times, currently it stands from 11 sectors, 24 industry groups, 68 industries and 157 subindustries. Each sector, namely Consumer Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information Technology, Materials, Real Estate, Telecommunication Services and Utilities represents an economically understandable market segment which is the key of the popularity of the classification. GICS is the most famous actively used standard which is assiduously reviewed by the financial market. Because, almost all U. S. market participants benchmark their positions against the performance of GICS sector indices. Moreover, the most liquid products in the world are the Standard & Poor's Depositary Receipts (SPDR) ETFs which tracks the S&P 500 index and sub-indices. Thus, the behaviour of GICS must be in line with the market because like a stock it is continuously reviewed by the market and like earning season at least annually revised by MSCI and S&P. In this article we propose a purely quantitative technique (FMIC) to analyse the connections between financial market and GICS and study the efficiency of Global Industry Classification Standards. SPECTRAL CLUSTERING The original concept of classifying the market, defining sub-groups, distinguishing sectors raises the fundamental question: Who can judge the market? If we do not want to make any a priori assumptions then we have to look at the data and dismiss other subjective classification guidelines. Mathematically it is possible to represent a datasets as a graph, hence, we can construct an abstract network of stocks. The most straightforward method would be representing stocks with nodes, connection strengths with weights. Thus, we can define the network with G( , ) graph where represents the set of stocks and contains the connection information. It is widely used that the adjacency matrix represents the graph, thus all the structure information is embedded in the matrix. Note that if we normalize the sum of each row to one, then we get the transition matrix of the random walk on the graph (Luxburg 2007.);