TY  - DATA
AU  - Kalra, Akash
AU  - Rajpurohit,Girish
AU  - Sarkar, Souradeep
TI  - Application of data analytics in agriculture commodity trading 
U1  - SP2023/3608 
PY  - 2023///
CY  - Ahmedabad
PB  - Indian Institute of Management
KW  - Data analytics
KW  - Agriculture commodity trading
KW  - Commodity trading - Data analysis
N1  - Submitted to: Prof. Indranil Bose
Submitted by: Akash Kalra, Girish Rajpurohit, Souradeep Sarkar


N2  - Introduction:
In the Indian agricultural context commodity trading emerged as one of the means to mitigate risk associated with agricultural production and trade. As we know, agriculture has historically been susceptible to uncertainties like weather change, pest attacks, market price fluctuations, and geopolitical factors, impacting both farmers and traders. Commodity trading amidst all this aimed to address the above challenges, providing a platform where agriculture commodities could be traded (bought or sold) at predetermined prices at a particular time in the future. This process, also known as futures trading, allows stakeholders, like farmers and investors, to hedge against price changes. Commodity trading of wheat and rice played a crucial role in assuring guaranteed prices for their produce through the MSPs, which acted as a form of trading in the future. In India, the establishment of commodity exchanges- Multi Commodity Exchange (MCX) and National Commodity & Derivatives Exchange Limited (NCDEX) moderated the buying and selling of agricultural commodities like wheat, rice, spices, pulses, etc. by using these platforms traders mitigated their risks by locking futures prices of delivery, ensuring price stability in otherwise a volatile market

ER  -