In this research article, we aim to build an efficient portfolio by using the following selected variables like Price to Earnings ratio, Total returns, Turnover, Price to Book value ratio and Dividend Yield of Nifty 50 companies. This paper has applied the K-mean clustering approach to construct an optimal portfolio that yields maximum returns. An optimal portfolio satisfies the need for diversifying the investment pattern to minimise risk. We have considered five variables between the periods of April 2015 to March 2018 to cluster these stocks. The efficiency of the three clusters formed was measured using the stock return value of each stock. The total of the stock returns in each cluster where calculated. After the evaluation of these total returns, the first cluster showed the maximum return when compared to the other two clusters.
Cite this article:
Anju Ans Saji, Joshna Joys Maria Joseph, Sathish Kumar B. Portfolio Construction and testing using Cluster Analysis. Asian Journal of Management. 2020;11(2):207-212. doi: 10.5958/2321-5763.2020.00032.3
1. Ammann, M., Coqueret, G., and Schade, J.-P. (2016). Characteristics-based portfolio choice with leverage constraints. Journal of Banking and Finance, 70, 23-37.
2. Anand, A., Chakravarty, S., and Chuwonganant, C. (2009). Cleaning house: Stock reassignments on the NYSE. Journal of Financial Markets, 12, 727-753.
3. Bock, T. (2018, March 28). What is Hierarchical Clustering? Retrieved from DIisplayr Blog: https://www.displayr.com/what-is-hierarchical-clustering/
4. Bonaparte, Y., Korniotis, G. M., and Alok. (2014). Income hedging and portfolio decisions. Journal of Financial Economics, 113(2), 300-324.
5. Bragg, S. (2018, March 27). Turnover ratios. Retrieved from Accounting Tools: https://www.accountingtools.com/articles/ what-is-a-turnover-ratio.html
6. Campbell, J. Y., and Vuolteenaho, T. (2004). Bad Beta, Good Beta. The American Economic Review, 94, 1249-1275.
7. Ciccotello, C., Greene, J., Ling, L., and Rakowski, D. (2011). Capacity and factor timing effects in active portfolio management. Journal of Financial Markets, 14, 277-300.
8. Garvey, R., and Wu, F. (2014). Clustering of intraday order sizes by uninformed traders versus informed traders. Journal of Banking and Finance, 41, 222-235.
9. Gonzalez, A., and Rubio, G. (2017). The joint cross -sectional variation of equity returns and volitalities. Journal of Banking and Finance, 75, 17-34.
10. Huddart, S. (1999). Reputation and performance fee e!ects on portfolio choice by investment advisers. Journal of Financial Markets, 2, 227-271.
11. Ingram, M., and Margetis, S. (2010). A practical method to estimate the cost of equity capital for a firm using cluster analysis. Managerial Finance, 36(2), 160-167.
12. J.Garbade, D. (2018, September 13). Understanding K-means Clustering in Machine Learning. Retrieved from Towards Data Science: https://towardsdatascience.com/understanding-k-means-clustering-in-machine-learning-6a6e67336aa1
13. Jacobs, H., Müller, S., and Weber, M. (2014). How should individual investors diversify? An empirical evaluation of alternative asset allocation policies. Journal of Financial Markets, 19, 62-85.
14. Jiang, C., Ma, Y., and An, Y. (2010). An analysis of portfolio selection with background risk. Journal of Banking and Finance, 34, 3055-3060.
15. Klotz, S., and Lindermeir, A. (2015). Multivariate credit portfolio mangement using cluster analysis. Journal of Risk Finance, 16(2), 145-163.
16. Krishnamurti, C., Sequeira, J. M., and Fangjian, F. (2003). Stock exchange governance and market quality. Journal of Banking and Finance, 27, 1859-1878.
17. L, F., GY, M., and N, M. S. (1981). Cluster Analysis. Acta Oeconomica, 26, 291-334.
18. Lin, Q. (2018). Technical Analysis and Stock Return Predictability:An Aligned Approach. Journal of Financial Markets, 38, 103-123.
19. Markowitz, H. (1952). Porfolio Selection. The Journal of Finance, 7, 77-91.
20. Massa, M., Simonov, A., and Stenkrona, A. (2015). Style representation and portfolio choice. Journal of Financial Markets, 23, 1-25.
21. Mei, X., and Nogales, F. J. (2018). Portfolio Selection with Proportional Transaction Costs and Predictability. Journal of Banking and Finance, 94, 131-151.
22. Merkle, C. (2018). The curious case of negative volatility. Journal of Financial Markets, 40, 92-108.
23. Mirkin, B. (1996). Mathematical Classification and Clustering. Springer Science and Business Media.
24. Morgan, G., and Thomas, S. (1998). Taxes, dividend yields and returns in the UK equity market. Journal of Banking and Finance, 22(4), 405-423.
25. Nanda, S. R., Mahanty, B., and Tiwari, M. K. (2010). Clustering Indian stock market data for protfolio managament. Expert System with Applications, 37, 8793-8798.
26. Ohta, W. (2006). An analysis of intraday patterns in price clustering on the Tokyo Stock Exchange. Journal of Banking and Finance, 30, 1023-1039.
27. Palomino, F., and Sadrieh, A. (2011). Overconfidence and delegated portfolio management. Journal of Financial Intermediation, 20, 159-177.
28. Sathyanarayana, S., and Harish, S. N. (2017). An Empirical study on stability beta in Indian stock market with special reference to CNX Nifty 50. Journal of financial risk management, 14, 16-35.
29. Sha, T. L. (2017). Effects of Price Earnings Ratio, Earnings Per Share, Book to Market Ratio and Gross Domestic Product on Stock Prices of Property and Real Estate Companies in Indonesia Stock Exchange. International Journal of Economic Perspectives, 11, 1743-1754.
30. Sidana, G., and Acharya, D. (2007). Classifying Mutual Funds In India: Some results from clustering. Indian Journal of Economics and Business, 6(1), 71-79.
31. Yang, X., and Zhang, H. (2019). Extreme absolute strength of stocks and performance of momentum strategies. Journal of Financial Markets, 44, 71-90.
32. Zhang, J., Jin, Z., and An, Y. (2017). Dynamic portfolio optimization with ambiguity aversion. Journal of Banking and Finance, 79, 95-109.