Heckyl’s Seasonality Screen Detects Hidden Patterns in Commodities
Commodities are driven by demand and supply, which provides a basis for determining price directions. As commodities don’t have to deal with subjective issues like financial performance, management quality etc., they move with their respective business cycles. Commodities are broadly classified into agricultural commodities, metals and energy. Within these broad classifications, each commodity can exhibit its own trend based on respective business cycles.
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For instance, NCDEX Wheat contract prices remained weak during the period of February to April (See image 1). Harvest season for wheat starts in April, which last till May. Small quantities of new wheat stock starts flowing into market as early as February. Later, new wheat stock arrival in the market increased substantially and hits peak level in April.
Similarly, we found that MCX Cotton contract prices dropped every year during the period of September to November [See image 2].
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In 2016, lower wheat market arrival during harvest season compared to 2015 caused rise in prices during April this year. However, in the previous year’s harvest season, wheat saw a surge in market arrival, which resulted in drop in prices during April 2015 (See Image 3).
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Impact of new stock arrival during harvest season on agri-commodity prices is logical and known. By analyzing commodities on our seasonality screen, one can also unearth certain hidden pattern.
For instance, we found that prices of MCX Copper, Aluminium and Zinc fell every year during October in the last 5-years (See image 4), while Lead and Nickel prices for October declined on four out of five occasions in the past 5-years.
[Image 4]
The Bottom-line:
Heckyl’s Seasonality, Market Arrival and Crop Calendar screens can not only helps in understanding business cycles, but also provides key insights on historical price behavior. Most importantly, a trader can discover hidden pattern within commodities on Heckyl’s Commodities Trading platform, which can help in predicting future prices.
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