Open AccessPrediction of Option Price using Ensemble of Machine Learning Algorithms for Indian Stock Market
Author(s) -
Payal Shrivastava,
Chandan Kumar Verma
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2683.078219
Subject(s) - machine learning , computer science , artificial intelligence , artificial neural network , merge (version control) , stock price , stock market , stock (firearms) , econometrics , economics , engineering , series (stratigraphy) , mechanical engineering , paleontology , horse , biology , information retrieval
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.