Model using Improved Apriori Algorithm to generate Association Rules for Future Contracts of Multi Commodity Exchange (MCX)
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Abstract
Now-a-days commodity markets are getting equal importance as equity market. Moreover, just like stock trend prediction, researchers are taking active interest in forecasting commodity trend. Trend prediction is really a challenging task especially for commodity market. There are so many factors, which affects commodity market returns. This empirical study generates association rules form the data of Multi Commodity Exchange (MCX) of India from November 2003 to December 2016. Data contains End of Day (EOD) prices of each commodity future contract. We have developed a model to analyze MCX data and derive association rules. Using our model, we have found co-movement between fundamentally different commodity future contracts. In our model, we have used improved apriori algorithm. Our result portrays that the contracts of fundamentally different commodities are correlated even if they neither substitutes nor complements each other.
Keywords: MCX, Commodity Future Contracts, Association Rules, Improved Apriori Algorithm
Keywords: MCX, Commodity Future Contracts, Association Rules, Improved Apriori Algorithm
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