Abstract
Investment diversification and portfolio building has been a great interest for share market investors, so as to minimize risk and maximize profit in a sensitive stock market. This paper gives an inside view of application of clustering for grouping 79 stocks (NSE), which can be used to build a diversified portfolio. Manually trying out different groupings to diversify portfolio is a computationally expensive task. In this paper, the closing price, time series of the stocks have been considered. Common effect due to market has been discounted using partial correlation, and a correlation based dissimilarity measure has been used for clustering. An equal investment strategy has been adopted to compare the portfolio’s performance with SENSEX. The empirical results of the portfolios have been studied and presented in details.
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References
Zolhavarieh, S., Aghabozorgi, S., Teh, Y.W.: A Review of Subsequence Time Series Clustering (2014)
Banerjee, D.: Forecasting of Indian stock market-using time-series ARIMA model. In: 2014 2nd International Conference on Business and Information Management (ICBIM), 9 Jan 2014, pp. 131–135. IEEE (2014)
Bini, B.S, Mathew, T.: Clustering and regression technoques for stock prediction. In: International Conference on Emerging Trends in Engineering Science and Technology (ICETEST), pp. 1248–1255 (2015)
Chevalier, J., Ellison, G.: Risk taking by mutual funds as a response to incentives. J. Polit. Econ. 105(6), 1167–1200 (1997)
Kenett, D.Y., Tumminello, M., Maid, A., Gur-Gershgoren, G., Mantegna, R.N.: Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market. PLoSONE 5(12), e15032 (2010). https://doi.org/10.1371/journal.pone.0015032
Dorr, D.H., Denton, A.M.: Establishing relationships among patterns in stock market data. Data Knowl. Eng. 68(3), 318–337 (2009)
Thakur, G.S.M., Bhattacharyya, R., Sarkar (Mondal), S.: Stock Portfolio Selection using Dempster-Shafer evidence theory. Journal of King Saud University – Computer and Information Science (2016)
Guo, C., Jia, H., Zhang, N.: Time series clustering based on ICA for stock data analysis. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008, WiCOM 2008. Oct 12 2008, pp. 1–4. IEEE (2008)
Gong, J., Song, S.: A new approch of stock price trend prediction based on logistic regression model. In: International Conference on New Trends in Information and Service Science. IEEE, Xplore (2009)
Gong, J., Sun, S.: A new approach of stock price prediction based on logistic regression model. In: International conference on New Trends in Information and Service Science, NISS 2009, pp. 1366–1371, June 2009
Lehnertz, K.: Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy—an overview. Int. J. Psychophysiol. 34(1), 45–52 (1999)
Naylor, M.J., Rose, L.C., Moyle, B.J.: Topology of foreign exchange markets using hierarchical structure methods. Phys. A 382, 199–208 (2007)
Mirkin, B.G.: Mathematical Classification and Clustering. Kluwer Academic Publishing The Netherland, Dordrecht (1996)
Nanda, S.R., Mahanty, B., Tiwari, M.K.: Clustering Indian stock market data for portfolio management. Expert Syst. Appl. 37(12), 8793–8798 (2010)
Mantegna, R.N.: Hierarchical structure in finance market. Eur. Phys. J. B 11, 193–197 (1999)
Goswami, S., Mitra, A., Chakroborty, B., Jalan, A., Chakroborti, A.: A time series clustering technique to analyze the stock market movement after the budget announcement. In: International Conference on Time Series ITISE 2017, Granada, Spain, 18–20 September 2017
Jung, S.S., Chang, W.: Clustering stocks using partial correlation coefficients. Phys. A 462, 410–420 (2016)
Aghabozorgi, S., Shirkhorshidin, A.S., Teh,Y.W.: Time series clustering – A decade review (2015)
Li, X., Qiu, T., Chen, G., Zhong, L.X., Wu, X.R.: Market impact and structure dynamic of the chinese stock market based on partial correlation analysis. Phys. A 471, 106–113 (2017)
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Mitra, A., Das, A., Goswami, S., Mustafi, J., Jalan, A.K. (2019). Portfolio Management by Time Series Clustering Using Correlation for Stocks. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_11
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DOI: https://doi.org/10.1007/978-981-13-8581-0_11
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