Prediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market
Payal Shrivastava1, Chandan Kumar Verma2
1Payal Shrivastava, Department of Applied Mathematics, MANIT, Bhopal, MP, India.
2Chandan Kumar Verma, Department of Applied Mathematics, MANIT, Bhopal, MP, India.
Manuscript received on 01 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 3231-3241 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2683078219/19©BEIESP | DOI: 10.35940/ijrte.B2683.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The non-deterministic behavior of stock market creates ambiguities for buyers. The situation of ambiguities always finds the loss of user financial assets. The variations of price make a very difficult task to predict the option price. For the prediction of option used various non-parametric models such as artificial neural network, machine learning, and deep neural network. The accuracy of prediction is always a challenging task of for individual model and hybrid model. The variation gap of hypothesis value and predicted value reflects the nature of stock market. In this paper use the bagging method of machine learning for the prediction of option price. The bagging process merge different machine learning algorithm and reduce the variation gap of stock price.
Index Terms: Stock Market, NSE, Ensemble, SVM, Machine Learning, Glowworm.
Scope of the Article: Machine Learning