Optimal Trading and Shipping of Agricultural Commodities

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

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Does Commodity Derivatives Function Effectively? A lengthy Discussion

IIM Kozhikode Society & Management Review ◽

10.1177/2277975220985746 ◽

2021 ◽

pp. 227797522098574

Author(s):

Bhabani Sankar Rout ◽

Nupur Moni Das ◽

K. Chandrasekhara Rao

Keyword(s):

Price Discovery ◽

Spot Market ◽

Futures Market ◽

The Other ◽

Volatility Spillover ◽

Agricultural Commodities ◽

Commodity Derivatives ◽

Derivative Market ◽

Negative Shock ◽

Lengthy Discussion

The present work has been designed to intensely investigate the capability of the commodity futures market in achieving the aim of price discovery. Further, the downside of the cash and futures market and transfer of the risk to other markets has also been studied using VaR, and Bivariate EGARCH. The findings of the work point that the metal commodity derivative market helps in the efficient discovery of price in the spot market except for nickel. But, in the case of the agricultural commodities, the spot is found to be leading and thus there is no price discovery except turmeric. On the other hand, the volatility spillover is bidirectional for both agri and metal commodities except copper, where volatility spills only from futures to spot. Further, the effect of negative shock informational bias differs from commodity to commodity, irrespective of metal or agriculture.

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Forecasting spot prices of agricultural commodities in India: Application of deep‐learning models

Intelligent Systems in Accounting Finance & Management ◽

10.1002/isaf.1487 ◽

2021 ◽

Cited By ~ 1

Author(s):

Manogna R L ◽

Aswini Kumar Mishra

Keyword(s):

Deep Learning ◽

Agricultural Commodities ◽

Learning Models ◽

Spot Prices

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