Challenges and Opportunities in Price Forecasting for Commodities: A Study of Technical Indicators in the NCR Region

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Pooja Yadav, Jagat Narayan Giri

Abstract

Commodity price forecasting is essential for industries, policymakers, traders, and investors due to the significant fluctuations in prices caused by macroeconomic, microeconomic, and geopolitical factors. In the context of India’s National Capital Region (NCR), which is a major hub for agricultural and energy commodities, accurate price forecasts are vital for strategic decision-making. This study examines the use of three common technical indicators—Moving Averages, Bollinger Bands, and the Relative Strength Index (RSI)—in forecasting commodity prices in this region. To evaluate the effectiveness of Moving Averages, Bollinger Bands, and RSI in commodity price forecasting, and to identify the challenges and opportunities presented by these indicators within the NCR region. A survey was conducted with respondents from the NCR region to assess their understanding and application of the three technical indicators. A Chi-Square test was used to determine the statistical significance of the responses. Additionally, a Pearson correlation matrix was employed to examine relationships between the responses to different questions. Moving Averages were well understood by most respondents (60% correctly identified their purpose), with a strong correlation (0.99) between understanding their general use and the specific type (Exponential Moving Average). Bollinger Bands were less consistently understood, with only 36% correctly identifying their primary function as measuring market volatility. Chi-Square results (p = 0.012) indicate a significant variation in understanding. RSI was the best understood, with 60% of respondents identifying that RSI values above 70 indicate an overbought condition (p = 0.0045). The Pearson correlation for RSI questions was high, showing good comprehension of its application.
While Moving Averages and RSI are well understood and effectively used, there is a gap in understanding Bollinger Bands, indicating a need for more in-depth education on this indicator. Additionally, the study highlights opportunities for integrating technical indicators with machine learning and real-time data to improve the accuracy of commodity price forecasts, especially in volatile markets like those in the NCR region.

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How to Cite
Pooja Yadav, Jagat Narayan Giri. (2025). Challenges and Opportunities in Price Forecasting for Commodities: A Study of Technical Indicators in the NCR Region. European Economic Letters (EEL), 15(1), 1079–1088. Retrieved from https://www.eelet.org.uk/index.php/journal/article/view/2489
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