ANALYSIS AND CLUSTERING OF NIFTY COMPANIES OF SHARE MARKET USING DATA MINING TOOLS

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1 Review Article ANALYSIS AND CLUSTERING OF NIFTY COMPANIES OF SHARE MARKET USING DATA MINING TOOLS D. Venugopal Setty 1, Dr.T.M.Rangaswamy 2 and Dr.A.V.Suresh 3 Address for Correspondence 1 Assistant Professor, 2 Professor, 3 Professor and HOD, Department of Industrial Engineering and Management, R.V. College of Engineering, Bangalore , India ID: [email protected] ABSTRACT Data are any facts, numbers, or text that can be processed. Data analysis is to find relationships among the data objects and then perform the remaining analysis like; clustering, classification, or anomaly analysis. A cluster is a set of objects in which each object is closer to every other object, and an entire collection of clusters is referred as clustering. On review of the papers and journals, it was found that the investors are finding difficulty in selecting better performing company for investment. Hence the objective of the research work was set to develop the clusters of NIFTY companies for better investment. Price per earnings ratios were calculated for all the 50 NIFTY companies during years & The specimen calculated Price per earning ratios for Reliance power was and clustering of companies under sector wise were made based on the financial ratio analysis and clustering analysis. It was found that all 50 NIFTY companies were clustered and distributed as 11, 21 & 18 numbers for the P/E ratio <10, P/E ratio between & P/E ratios >20 respectively for the year and 03, 18 & 29 respectively for the year Based on results, an investor is suggested to select a company and sector from the list for better investment. The recommended company for investment is reliance power (power-generation and distribution sector), since this company performed well in the years and KEY WORDS: Nifty, sector, share market INTRODUCTION Data Mining Data are any facts, numbers, or text that can be processed by a computer. One approach to data analysis is to find relationships among the data objects and then perform the remaining analysis using these relationships rather than the data objects themselves. Data Mining is an analytic process designed to explore data (usually large amounts of data, typically business or market related) and in search of consistent patterns and /or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. It is also called as data discovery or knowledge discovery. Data Mining can be used to increase revenue, cuts costs, or both. Data Mining Tasks Data mining tasks are generally divided into two major categories, namely predictive task and descriptive task. The objective of Predictive Task is to predict the value of a particular attribute based on the values of the other attributes and that the objective of Descriptive task is to derive patterns (correlations, trends, clusters, trajectories and anomalies) that summarize the underlying relationships in data.

2 Data mining does the further four important tasks namely; predictive modeling, association analysis, cluster analysis, and anomaly detection. Cluster A cluster is a set of objects in which each object is closer to every other object. The types of clusters includes; well-separated clusters, prototype-based clusters, graph-based clusters, density-based clusters, and shared-property (conceptual clusters). The important characteristics of cluster include; data distribution, shape, different size, different density, poorly separation, relationships among clusters, and subspace. Clustering Clustering is a class or group of objects that share common characteristics and play an important role in how people analyze and describe the world. It is dividing the objects into groups (clustering) and assigning particular objects to these groups (classification). Clustering aims to find useful groups of objects, where usefulness is defined by the goals of the data analysis. An entire collection of clusters is commonly referred to as clustering. There are three types of clustering namely; hierarchical versus partitional, exclusive versus overlapping versus fuzzy and complete versus partial. A partitional clustering is simply a division of the set of data objects into non-overlapping subsets (clusters) such that each object is exactly in one subset. Partitional algorithms typically determine all clusters at once. The partitional clustering can be obtained by taking any member of that sequence. Cluster Analysis It groups data objects based only on information found in the data that describes the objects and their relationships. It is also a class or group of objects that share common characteristics and play an important role in how people analyze and describe. The goal is that the objects within a group be similar to one another and different from the objects in the other groups. The greater the similarity within a group and greater the difference between groups, the better or more distinct is the clustering. Cluster analysis is sometimes referred to as unsupervised classification. When the term classification is used without any qualification within data mining, it typically refers to supervised classification. Financial Market Financial market is a mechanism that allows people to easily buy and sell financial securities, commodities and other fungible items of value at low transaction costs. Financial markets can be domestic or international. The financial markets can be divided into different types namely; capital markets (stock markets, bond markets and commodity markets), money markets, derivatives markets, insurance markets, foreign exchange markets. Financial

3 markets facilitates; raising of capital in the capital markets, transfer of risk in the derivatives markets, international trade in the currency markets, and match those who want capital to those who have it. Stock Market The stock market is one of the most important source for companies to raise money, and is a public market for the trading of company stock and derivatives at an agreed price.the stock market ma be primary, or secondary. In the primary markets, securities are bought by way of the public issue (IPO s) directly from the company, and where as in the secondary market existing outstanding securities are bought and sold. Stock Exchange A stock exchange is a corporation or mutual organization which provides trading facilities for stock brokers and traders. Stock exchanges have multiple roles in the economy namely; raising capital for businesses, mobilizing savings for investment, facilitating company growth, profit sharing, corporate governance, creating investment opportunities for small investors, government capital-raising for development projects, etc. The Bombay Stock Exchange Limited and the National Stock Exchange limited are two largest exchanges in India. Standard and Poor CNX National Fifty (S&P Cnx Nifty) In 1996, the National Stock Exchange of India launched S&P CNX Nifty and CNX Junior Indices that make up 100 most liquid stocks in India. The NSE's key index is the S&P CNX Nifty, known as the Nifty. Nifty is a diversified index of 50 stocks from 25 different economy sectors weighted by market capitalization. S&P CNX NIFTY tracks the behavior of a portfolio of blue chip companies, the largest and most liquid Indian securities. The index has been trading since April 1996 and is well suited for benchmarking. Selection of the index set is based on criteria; impact cost, market capitalization, shares outstanding, and domicile. The index is reviewed every quarter and a sixweek notice is given to the market before making any changes to the index constituents. Stocks may be deleted due to mergers, acquisitions or spin-offs. Stock Market Basics (Shares And Stocks) Stock market basics include shares and stocks. A Share or stock is a document issued by a company, which entitles its holder to be one of the owners of the company. A share is directly issued by a company through IPO or can be purchased from the stock market. By owning a share one can earn a portion of the company s profit called dividend. So, return is the dividend plus the capital gain. A stock is nothing but a collection or a group of shares. The stock may be common stock or preferred stock. Financial Ratio Analysis

4 Financial Ratio analysis uses a company s financial information to predict whether it will meet its future projections of earnings, and it assists the investor in the selection of stocks. These are classified as; profitability ratios, liquidity ratios, activity ratios, debt ratios (leverage ratios), market ratios and coverage ratios. Profitability ratios measure the firm's use of its assets and control of its expenses to generate an acceptable rate of return. These because the profits of a company are important to investors because these earnings are either retained or paid out in dividends to shareholders, both of which affect the stock price. Price/Earnings Ratio (P/E Ratio) Price/Earning ratio gives you fair idea of how a company's share price compares to its earnings. If the price of the share is too much lower than the earning of the company, the stock is under valued and it has the potential to rise in the near future. On the other hand, if the price is way too much higher than the actual earning of the company and then the stock is said to over valued and the price can fall at any point. The most commonly used guide to the relationship between stock prices and earnings is the P/E ratio. P/E ratio is volatile and may fluctuate considerably. The P/E ratios (above 20, thumb rule) are characteristic of growth companies, although with the average market multiple currently around 28, a P/E ratio of 20 almost seems like a value stock. High P/E ratios indicate high risk. If the future anticipated growth of the high P/E ratio stocks is not achieved, their stock prices will be punished and they will fall very quickly. On the other hand, if they live up to their promise, investors will benefit substantially. Low P/E ratio stocks (under 10) are characteristic of either mature company with low growth potential or companies that are undervalued or in financial difficulty. By comparing the P/E ratio of a company with the averages in the industries and the markets, investors can get a feeling for the relative value of the stock. P/E ratios fluctuate considerably, differing among companies due to many factors, from growth rates and popularity to earnings and other financial characteristics. It is calculated by, P/E ratio = Market price of the stock / Earnings per share Earnings per Share (EPS) Earning per share is the profit that the company made per share on the last quarter. It is mandatory for every public company to publish the quarterly report that states the earning per share of the company. The earning per share indicates the amount of earnings allocated to each share of common stock outstanding. EPS figures can be used to compare the growth or lack of growth in earnings from year to year and to project growth in earnings. Decreasing EPS over a period of time generally has a negative impact on stock price. EPS is calculated by,

5 EPS = (Net income Preferred Dividends) / Number of common share outstanding. Where, Number of shares outstanding = Number of shares issued Shares company has bought back. RESEARCH GAP On review of the papers and journals, despite the improving economic environment in the country, the investors are still finding difficult in selecting better performing / appropriate company /sector for investment. Stock analysis is a difficult task due to the nature of the stock data, which is very noisy and time varying. OBJECTIVES OF THE RESEARCH The current research work was carried out with the following objectives: 1. To study and analyse the performance of Share Market of NIFTY companies 2. To develop clusters of the NIFTY Companies using Data Mining Tools (Clustering analysis) and profitability ratio (price per earnings ratio) 3. To help the investor in selection of better performing company and sector for the investment METHODOLOGY ADOPTED 1. To study the stock market. 2. To collect the NIFTY companies for the year and To identify and grouping the Nifty companies under various sectors. 4. To collect the number of outstanding shares, EPS (earnings per share) and closing price data / Market price of the stock of the 50 NIFTY companies for the year and To calculate Price per Earnings ratio (P/E ratio). 6. To group the Nifty companies as clusters. 7. To recommend best company / sector for the investor to investment money. DATA COLLECTION Data to be collected was divided into two parts such as; qualitative data and quantitative data. The qualitative data collection was made using judgmental sampling method. The quantitative data collection was carried out by means of secondary data, and this includes; list of NIFTY companies, earnings per share (EPS) and closing price data / Market price of the stocks of the 50 NIFTY companies for the years and from stock exchanges, internet, magazines and trade journals. The companies deleted under NIFTY in the year are GRASIM and HCL TECH, while added is TVS MOTORS and UCO BANK. TOOLS AND TECHNIQUES USED The tools and techniques used in the analysis and clustering of the NIFTY companies of share

6 market includes: data mining tools - clustering analysis, type of cluster - conceptual cluster (shared-property clusters), type of clustering - partitional clustering, type of cluster analysis / algorithm - agglomerative hierarchical clustering algorithm, and type of financial ratio - profitability ratio DATA ANALYSIS Price per earnings ratios are calculated for the years & and are tabulated in Tble 1 & Tble 2 respectively. TABLE 1 : PRICE PER EARNING RATIOS FOR THE YEAR Company Sector P/E ratio =MV/EPS A B B Electric equipment 19.2 ACC Cement 9.9 AMBUJA CEM. Cement 10.6 AXIS BANK Bank 10.3 B H E L Engineering heavy 25.7 B P C L Refineries - BHARTI AIRTEL Communication 17 CAIRN INDIA Oil drilling & exloration 50 CIPLA Pharmaceuticals 25.4 DLF Construction & contracting 18.5 GAIL (INDIA) Oil drilling & exploration 11.3 GRASIM INDS Diversified 8.4 H D F C Bank 22.1 HCL TECHNOLOGIES Computer software 10.6 HDFC BANK Bank 22.4 HERO HONDA MOTOR Automobiles 19.3 HIND. UNILEVER Personal care 24.2 HINDALCO INDS. Aluminium 3.4 ICICI BANK Bank 11.2 IDEA CELLULAR Communication 18.1 INFOSYS TECH. Computer software 14.9 ITC Cigarettes 22.2 LARSEN & TOUBRO Engineering heavy 20 M & M Automobiles 22.9 MARUTI SUZUKI Automobiles 19.1 NATL. ALUMINIUM Aluminum 9.9 NTPC Power-generation & distribution 21.3 O N G C Oil drilling & exploration 11.6 POWER GRID CORPN Power-generation & distribution 27 PUNJAB NATL BANK Bank 5.2 RANBAXY LABS. Pharmaceuticals - RELIANCE CAPITAL Finance 11 RELIANCE COMM Communication 43.9 RELIANCE INDS 17.7 RELIANCE INFRA Power-generation & distribution 14.9 RELIANCE PETRO Refineries - RELIANCE POWER Power-generation & distribution S A I L Steel 6.9

7 SIEMENS Telecommunication equipment 13.3 ST BK OF INDIA Bank 9.4 STERLITE INDS. Metals 7.2 SUN PHARMA. Pharmaceuticals 22.1 SUZLON ENERGY Engineering heavy 8.1 TATA COMM Communication 70.8 TATA MOTORS Automobiles 9.5 TATA POWER CO. Power-generation & distribution 33.7 TATA STEEL Steel 3.7 TCS Computer software 12.4 UNITECH Construction & contracting 7.6 WIPRO Computer software 13.6 TABLE 2 : PRICE PER EARNING RATIOS FOR THE YEARS Company Sector P/E ratio =MV/EPS ABB Electric equipment ACC-CEMENT Cement AMBUJA CEMENT Cement AXIS BANK Bank BHARTI AIRTEL Telecommunication service BHEL Engineering heavy BPCL Refineries CAIRN INDIA Oil drilling & exploration CIPLA Pharmaceuticals 33.4 DLF Construction & contracting GAIL Oil drilling & exploration HERO HONDA Automobiles HINDALCO Aluminum HUL Personal care ICICI BANK Banks-private sector 27.3 IDEA CELLULAR Telecommunication service IDFC Finance INFOSYS Computer software ITC Cigarettes JAIPRAKASH ASSOCIATION Construction & contracting KOTAK MAHINDRA Bank L&T Engineering heavy MAH & MAH Automobiles MARUTI SUZUKI Automobiles NTPC Power-generation/distribution ONGC Oil drilling & exploration PNB-BANK Bank POWER GRID CORP Power-generation/distribution RANBAXY LABS Pharmaceuticals RELIANCE CAPITAL Finance 19.1 RELIANCE REFINERIES Refineries 10.9 RELIANCE- COMMUNICATION Telecommunication service 14.8 RELIANCE INFRASTRUCTURE Power-generation/distribution RELIANCE POWER Power-generation/distribution SAIL Steel SBI-BANK Bank SUN PHARMA Pharmaceuticals 29.48

8 TATA MOTORS Automobile TATA POWERS Power-generation/distribution TATA STEEL Steel 9.76 TCS Computer software UNITECH Construction & contracting WIPRO Computer software TVS Automobiles STERLITE INDUSTRIES Metals UCO BANK Bank 6.02 Specimen Calculation for Price per Earning Ratio Price per earning ratio, P/E ratio = Market value /Earnings per share For Reliance power (power-generation & distribution), MV = Rs , EPS = Rs.0.3 per share, and P/E ratio = /0.3 = For Amubja cement, MV = Rs , EPS = Rs. 8 per share and P/E ratio = /8 =14.81 For UCO bank, MV = Rs , EPS = RS per share, and P/E ratio =61.103/10.15 = 6.02 For L&T (Heavy engineering), MV= Rs , EPS = Rs per share, and P/E ratio = /59.44 =26.43 Graphical Representation of P/E Ratio Vs Nifty Companies Graphical representation of P/E RATIO vs NIFTY companies for the years & are shown in graph 1 & graph- 2 respectively for better visual presentation. GRAPH 1: P/E RATIO VS NIFTY COMPANIES FOR THE YEAR

9 GRAPH- 2 : P/E RATIO VS NIFTY COMPANIES FOR THE YEAR CLUSTERING OF NIFTY COMPANIES UNDER SECTORS WISE Clustering of NIFTY companies under sectors wise were made based on Price per earning ratios for the years & , and are tabulated in table 3 & table 4 respectively. Pie chart - 1 & pie chart 2 represents clustering of NIFTY companies under sectors wise for the years & respectively. TABLE 3 : CLUSTERING OF NIFTY COMPANIES UNDER SECTORS WISE FOR THE YEAR Sector Price per earning ratio is < >20 Cement Acc cements Ambuja cements Nil Bank SBI, PNB Axis, ICICI HDFC Engineering-heavy Suzlon energy Nil BHEL Pharmaceuticals Nil Ranbaxy CIPLA, Sun pharma Construction Unitec DLF Nil Oil drilling & exploration Nil Gail, ONGC Cairn india Diversified Grasim L&T, Reliance Nil industries Telecommunication Nil Bharati airtel, IDEA Tata communications cellular, SIEMENS Personal care Nil Nil Hindustan unilever Finance Nil Reliance capital, HDFC Nil

10 Computer-software Nil W IPRO, TCS, Nil Infosys, HCL Automobiles Tata motors Hero honda, maruthi Mah & mah, suzuki Aluminum National aluminum, Nil Nil hindalco Cigarette Nil Nil ITC Power Nil Reliance infrastructure NTPC, power grid corporation, r power Metal TATA steel, SAIL, Sterlite Nil Nil TABLE 4: CLUSTERING OF NIFTY COMPANIES UNDER SECTORS WISE FOR THE YEAR Sector Price per earning ratio is < >20 Cement Nil 2(acc,ambuja) Nil Bank 1 (UCO bank) 4(icici,pnb,sbi,kotak) 1(axis bank,) Communication Nil 2(airtel,reliance) 1(idea) Power generator/ distributor Nil Nil 5 (NTPC, power grid, reliance inf, relpower, tata power) Finance Nil 1(HDFC) 2 (IDFC,relliance corp) Cigrettes Nil Nil 1 (itc) Steel 1(tata steel) 1(SAIL) 1(jindal) Automobiles Nil 2(herohonda,tatamotors) 3(mah&mah,maruti,TVS Oil drilling Nil 2(ONGC,CAIRN INDIA) 1(GAIL) Pharmaceuticals Nil 1(Ranbaxy labs) 2(CIPLA,Sun pharma) Construction Nil 1(UNITECH) 2(jaiprakash,DLF) Heavy engg 1(suzlon) Nil 3(bhel,bpcl,l&t) Aluminium Nil 1(hindalco) Nil Software Nil 2(HCL),TCS 2(Infosys,wipro) Refineries Nil 1(rel refineries) Nil Electrical equipment Nil Nil 1(abb)

11 PIE CHART - 1 : CLUSTERING OF COMPANIES UNDER SECTOR WISE FOR THE YEAR PIE CHART - 2: CLUSTERING OF COMPANIES UNDER SECTOR WISE FOR THE YEAR ANALYSIS ON PRICE PER EARNINGS RATIO (P/E RATIO) If P/E ratio< 10, the company does not grow and do not give expected profits / returns and

12 investors lose their investment. Hence such companies mentioned in the list are not recommended for investment. If P/E ratio 10 to 20, the company growth will take time and investor has to wait to get the benefits from his investment. Hence such company mentioned in the list involves risk for investment. If P/E ratio > 20, the company does well, growth is guaranteed, gives maximum profit and high returns. Hence such companies mentioned in the list are recommended for investment. CLUSTER ANALYSIS SUMMARY Summary of Cluster Analysis based on Price per earning ratios for the years are tabulated in table 5. RESULTS AND CONCLUSIONS Companies were clustered under sector wise based on the financial ratio analysis & clustering analysis and for better investment, the investors are strongly recommended to select a company & sector from the list. The recommended NIFTY Company for guaranteed return is reliance power for investment, since this company performed well in the years and Price per earnings ratio < >20 TABLE 5 : SUMMARY OF CLUSTER ANALYSIS For the year For the year ACC cements, SBI, PNB, Suzlon UCO bank, TATA steel, Suzlon energy, Unitec, Grasim India, TATA motors, national aluminum, Hindalco, TATA steel, SAIL, Sterlite Ambuja cements, AXIS bank, ICICI, ACC, Ambuja, ICICI, airtel, reliance Ranbaxy, DLF, GAIL, ONGC, L&T, communication, hdfc, SAIL, TATA Relaiance industries, Bharati airtel, motors, hero honda, ONGC, CAIRN IDEA cellular, Siemens, Rel. capital, W India, Ranbaxy labs, UNITECH, IPRO, TCS, Infosys, HCL, HDFC, Hindalco, HCL, TCS, Reliance Hero honda, maruti suzuki, relaince refineries infrastructure. HDFC, Cipla, BHEL, Sun Axiz bank, kotak mah, NTPC, power pharmaceuticals, Cairn India, TATA grid, reliance infrastructure, tata communications, Hindustan unilever, power, Reliance power, rel mah & mah, ITC, NTPC, power grid corporation, ITC, jindal, mah & mah, corporation, reliance power. TVS, maruthi suzuki, Gail, CIPLA, sun pharma, jay prakash, DLF, BHEL, BPCL, L&T, Infosys, WIPRO, ABB SCOPE FOR FUTURE WORK

13 The cluster analysis carried out for NIFTY companies only and same analysis can be used for the companies listed under National Stock Exchange of India Limited and Bombay Stock Exchange. ACKNOWLEDGEMENT The author s are thankful to the stock exchange, brokers and share holders for providing the data. The author s are also thankful to paper reviewers REFERENCES [1] Calderon T.G., and Cheh J.J., (2002), A roadmap for future neural networks research in auditing and risk assessment, International Journal of Accounting Information Systems, Vol. 3, No. 4, pp Das, S. and Chen, M Yahoo for Amazon: Extracting Market Sentiment from Stock Message Boards, Proceedings of the 8th Asia Pacific Finance Association Annual Conference (APFA 2001), Bangkok, Thailand, July 22-25, 2001 [2] Fanning K. and Cogger K., (1998), Neural Network Detection of Management Fraud Using Published Financial Data, International Journal of Intelligent Systems in Account-ing, Finance & Management, Vol. 7, No. 1, pp [3] R. Gencay, Non-linear prediction of security returns with moving average rules, Journal of Forecasting, vol. 15, no.3, pp , 1996 [4] World Federation of Exchanges. "2007 Annual Report and Market Statistics". Retrieved on [5] Data Mining, Nang-Pan [6] Financial management, IM Pandey [7] [8]