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Stock prediction and mutual fund portfolio management using curve fitting techniques
G. Maji, D. Mondal, N. Dey, N.C. Debnath,
Published in Springer Science and Business Media Deutschland GmbH
Investment in the share market helps generate more profit than the other financial instruments but has the threat of market risk that might lead to a high loss. This risk factor refrains many potential investors from investing in the share market directly. Instead, they invest in different mutual funds that are being managed by experienced portfolio managers. To avoid the risk factors and increase the gain, they put the accumulated capital in multiple stocks. They need to perform many calculations and predictions to overcome the uncertainties and unpredictability and need to ensure higher gains to the investors of that mutual fund. In this research work initially, a data mining based approach employs a curve fitting/regression technique to forecast the individual stock price. Based on the above analysis, we propose a framework to diversify the investment of the capital fund. This method employs buy and hold strategy using both statistical features and basic domain knowledge of the share market. The proposed framework distributes the capital first, by distributing sector-wise, and then for each sector, investing company-wise, as a diversified approach among different stocks for higher return but maintaining lower risks. Experimental results show that the proposed framework performs well and generates a good yield compared to some benchmark and ranked mutual funds in the Indian stock market. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetJournal of Ambient Intelligence and Humanized Computing
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH