https://archives.christuniversity.in/files/original/0b10abfd3363c35c4f46fd9a2dbe7ada.pdf 70469ef87041496d80ed067afdc387fa Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MPHIL Mphil Mphil Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comparative Study of Effectiveness of Option Forecasting Models: Black Sholes Vs Simple Hybrid Neural Networks. Creator An entity primarily responsible for making the resource Devakumar Christopher Date A point or period of time associated with an event in the lifecycle of the resource 2012 Source A related resource from which the described resource is derived Commerce Description An account of the resource Many studies have shown that Artificial Neural Networks has the capacity to learn the underlying mechanics of stock markets. In fact, Artificial Neural Networks has been widely used for forecasting financial markets. However, such applications to Indian Stock Markets are scarce. This paper applies neural network models to predict the option prices which are traded in National Stock Exchange. Multilayer perceptron network is used to build the option forecasting model and the network is trained using Back Propagation algorithm. It is found that the predictive power of the network model is not influenced by the neural network using realised volatility. The study shows that satisfactory results can be achieved when applying Hybrid Neural Networks to forecast for the next 30 days. The result shows Black Scholes model outperforms the Hybrid Neural Network models and also when we compared the Hybrid Neural Networks results with the econometric Models such as OLS and EGARCH we saw that the Econometric models give the good results.