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Roy, Shuvashish
- Identifying Homogeneity of Small-Cap Stocks in Indian Market:A Data Mining Approach
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Authors
Affiliations
1 Hazrat Khajar Bashir Unani Ayurvedic Medical College & Hospital Foundation, Jamalpur, BD
2 Institute of Business Management, The National Council of Education Bengal, Kolkata, West Bengal, IN
1 Hazrat Khajar Bashir Unani Ayurvedic Medical College & Hospital Foundation, Jamalpur, BD
2 Institute of Business Management, The National Council of Education Bengal, Kolkata, West Bengal, IN
Source
International Journal of Business Analytics and Intelligence, Vol 7, No 1 (2019), Pagination: 53-63Abstract
Investors in equity shares look for two basic aspects while investing i.e. consistently rising returns with a decreasing or at least stabilized level of risk involved. Amidst the numerous stocks available in the market which differ widely on the basis of different aspects i.e. segment, sector, industry, market capitalization etc. it becomes a challenge for the investor to form a diversified portfolio where heterogeneity of the constituent stocks is the main criterion. Thus it is imperative that the basis be finalized on which the heterogeneity of the stocks shall be determined. Traditionally portfolios have been constituted on the basis of low coefficient of correlation of returns from the constituent stocks. The dissimilarity of co-movement of returns from stocks has traditionally been attempted to be maximized in portfolios. Another method of grouping similar stocks by using data mining approach is fast gaining popularity. This approach uses clustering technique to group homogeneous stocks on the basis of a set of selected criteria. These criteria can be financial ratios, indices or any other related matrices. Advanced versions of this technique can group homogeneous time series data as well. This paper attempts to identify homogeneous clusters of companies in the Indian small-cap segment based on valuation ratios. Valuation ratios have been selected to be the grouping criteria as these were not been used in earlier studies by researchers and scholars. The small cap companies in India have been chosen for this study for its better resilience and recovering potential compared to mid cap and large cap companies. The companies constituting the CNX NIFTY Small Cap 50 Index have been considered in the study.Keywords
Cluster Analysis, Valuation Ratios, Small Cap Sector, CNX NIFTY Mid Cap 50 Index.
References
- Alexandra, H., Joldes, C., & Gabriel, D. D. (2019). A cluster analysis of financial performance in central and Eastern Europe, 289–294. Retrieved from https://www.researchgate.net/publication/237262963
- Aghabozorgi, S., & Teh, Y. W. (2014). Stock market comovement assessment using a three-phase clustering method. Expert Systems with Applications, 41(2014), 1301–1314.
- Babu, M. S., Geethanjali, N., & Satyanarayana, B. (2012). Clustering approach to stock market prediction. International Journal of Advanced Networking and Applications, 3(4), 1281–1291.
- Banerjee, R., & Hofmann, B. (2018). The rise of zombie firms: Causes and consequences. BIS Quarterly Review, 67–78.
- Cai, F., Le-Khac, N.-A., & Kechadi, M.-T. (2016). Clustering approaches for financial data analysis: A survey. Retrieved from https://arxiv.org/ftp/arxiv/ papers/1609/1609.08520.pdf
- Costa Jr., N., Cunha, J., & Silva, S. D. (2005). Stock selection based on cluster analysis. Economics Bulletin, 13(1), 1−9.
- Dias, A., Pinto, C., Batista, J., & Neves, E. (2016). Signaling tax evasion, financial ratios & cluster analysis. BIS Quarterly Review. Working Paper No. 51, 2016.
- Ding, K., Hoogduin, L., Peng, X., Vasarhelyi, M. A., & Wang, Y. (n.d.). Clustering based peer selection with financial ratios. Rutgers, The State University of New Jersey. Retrieved from http://raw.rutgers.edu/docs/wcars/40wcars/Presentations/KexingXuan Yunsen.pdf
- Ferrando, A., & Lekpek, S. (2018). Access to finance and innovative activity of EU firms: A cluster analysis. European Investment Bank: Working Papers, 2018/02.
- Ferst, R., & Seres, D. (2012). Clustering austrian banks’ business models and peer groups in the European Banking Sector. Financial Stability Report, 24 December 2012.
- Fodor, A., Jorgensen, Randy, D., & Stowe, J. D. (2015). Forming stock groups with a cluster analysis of common size statements. Southwestern Finance Association Annual Conference.
- Goudarzi, S., Jafari, M. J., & Afsar, A. (2017). A hybrid model for portfolio optimization based on stock clustering and different investment strategies. International Journal of Economics and Financial Issues, 7(3).
- Gruener, A., & Schoenenberger, F. (2015). Risk cluster framework: How to analyse companies by operating leverage. Retrieved from https://efmaefm.org/0efmameetings/efma%20annual%20meetings/2015-Amsterdam/papers/efma2015_0219_ fullpaper.pdf
- Hou, B. (2016). Financial distress prediction of k-means clustering based on genetic algorithm and rough set theory. Chemical Engineering Transactions, 51.
- Lemos, C. A. A., Lins, M. P. E., & Ebecken, N. F. F. (2005). DEA implementation and clustering analysis using the K-Means algorithm. WIT Transactions on Information and Communication Technologies, 35, 321–329.
- Li, H., & Sun, J. (2011). Mining business failure predictive knowledge using two-step clustering. African Journal of Business Management, 5(11), 4107–4120.
- Marvin, K. (2015). Creating diversified portfolios using cluster analysis.
- Momeni, M., Mohseni, M., & Soofi, M. (2015). Clustering stock market companies via K-means algorithm. Arabian Journal of Business and Management Review, 4(5).
- Perisa, A., Kurnoga, N., & Sopta, M. (2017). Multivariate analysis of profitability indicators for selected companies of Croatian market. UTMS Journal of Economics, 8(3), 231–242.
- Setty, D. V., Rangaswamy, T. M., & Suresh, A. V. (2010). Analysis and clustering of nifty companies of share market using data mining tools. Journal of Engineering Research and Studies, 1(1), 152–164.
- Setyaningsih, S. (2012). Using cluster analysis study to examine the successful performance entrepreneur in Indonesia. Procedia Economics and Finance, 4, 286–298.
- Szucs, G. (2015). The financial analysis of the hungarian automotive industry based on profitability and capital structure ratios. Central European Business Review, 4(1).
- Temouri, Y. (2012). The cluster scoreboard: Measuring the performance of local business clusters in the knowledge economy. OECD Local Economic and Employment Development (LEED) Working Papers. 2012/13, OECD Publishing.
- Tufan, E., & Hamarat, B. (2003). Clustering of financial ratios of the quoted companies through fuzzy logic method. Journal of Naval Science and Engineering, 1(2), 123–140.
- Wang, Y.-J., & Lee, H.-S. (2008). A clustering method to identify representative financial ratios. Information Sciences, 178(2008), 1087–1097.