V3I7P76

Operational Data Quality and Machine Learning-Based Financial Forecasting of Price Volatility in Arecanut and Cocoa Markets

Prasanna Hegde^1*, Roopa U²

Abstract

Agricultural commodity markets are particularly vulnerable to shocks and stresses from climatic uncertainty, shifting consumer demand, supply disruption, policy action, and global economic conditions. The importance of accurate commodity price forecasting for farmers, traders, processors, financial analysts and policy makers has thus grown in significance. Research efforts into using machine learning algorithms to predict commodity prices have made significant strides, but few have focused on the quality of operational data that are inputs to these forecasting systems. Even the most sophisticated predictive models can be impaired in their ability to be reliable by the lack of quality in the data that these models are based on.

The effect of ODQ on financial forecasting of price volatility in arecanut and cocoa market is explored in this study using machine learning. The research relies on the price data for the month from 2011 to 2025, and the forecasting trends it studied extended to 2030. The study combines operational analytics with statistical and machine learning methods, such as descriptive analytics, correlation analysis, regression models, hypothesis testing, and time-series forecasting. Special attention is given to the importance of data completeness, consistency, timeliness, reliability and accuracy in forecasting effectiveness.

The results will provide evidence that operational data quality has a strong impact on predictive accuracy and improves the accuracy of commodity market forecasting systems. The study adds to the ever-expanding literature on agricultural analytics by emphasising the need for structured data management of operations to create reliable forecasting frameworks for commodities that are less studied, such as arecanut and cocoa. The framework gives implications that can help commodity traders, agricultural enterprises, policymakers, and researchers boost forecasting accuracy and facilitate data-driven financial decision-making.

The report covers the key areas of Machine Learning, Operational Data Quality, Financial Forecasting, Commodity Price Volatility, Arecanut Market, Cocoa Market, Predictive Analytics, Time Series Forecasting, and Operational Analytics.