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FORECASTING OF FUTURES TRADING VOLUME OF SELECTED AGRICULTURAL COMMODITIES USING NEURAL NETWORKS

KrishiKosh

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Title FORECASTING OF FUTURES TRADING VOLUME OF SELECTED AGRICULTURAL COMMODITIES USING NEURAL NETWORKS
 
Creator NAVYA NANAIAH, B
 
Subject apples, markets, retail marketing, supply, sales, marketing, economics, productivity, area, storage
 
Description A study of futures trading of agricultural commodities and the
volume of their trade was done by using forecasting methodologies. The
identified forecasting methodologies for forecasting futures trading
volume of selected agricultural commodities are Regression, ARIMA and
Neural networks. Monthly transactions data for the period of 2003 -
2010 has been collected from National Commodity & Derivatives
Exchange (NCDEX) website for some of the agricultural commodities like
maize, mustard, soybean and pepper.
Volume was taken as dependent variable while open interest,
closing price and mean cash price were taken as independent variables
for analysis. The data obtained for past seven years on volume of
commodities selected is used to predict the future volumes for the first
five months of 2011. Model validation was done using MAPE, RMSE and
Theil’s U statistic .The results indicated that the neural networks
outperforms multiple regression and ARIMA.
This study also helps the policy makers to ascertain the future
requirements. Most of the data agriculture will be having non linear
relationship and hence ANN modeling is most suitable for prediction of
agriculture system.
 
Date 2016-12-15T10:54:35Z
2016-12-15T10:54:35Z
2011-03-23
 
Type Thesis
 
Identifier TH-10126
http://krishikosh.egranth.ac.in/handle/1/90265
 
Language en
 
Format application/pdf
 
Publisher University of Agricultural Sciences GKVK, Bangalore