Quant – the new ‘buzzword’ in Soft & Agricultural Trading

Transition is a common theme in the soft and agricultural markets and, much like the energy markets over the past decade, quantitative and technical strategies are increasingly being employed as a means for revenue generation. Softs and Agricultural traders have for time immortal relied largely upon a strategy that has proved challenging in recent years; that of focusing on a single commodity, and trading directional positions borne out of in-depth knowledge of that single asset and its price drivers.

New entrants to the market are thinking differently, and a new breed of trader has been emerging within the more dynamic and progressive trade houses and financial institutions. These traders tend to be mathematically and econometrically educated, forming views based almost exclusively on technical and quantitative data. These individuals are tech savvy, many systemise their trading protocols and show little regard for the underlying price drivers such as supply and demand and political and economic themes. Some work using a combination of the two approaches – coining the term ‘quantamental’.

A shift towards traders adopting such strategies has in many ways been borne out of necessity. System-driven and algorithmic funds and CTA’s influence on various asset classes has been unprecedented, with trend following black-boxes distorting these markets to a point that view formation and position taking based on fundamental price drivers has become not just difficult, but dangerous. It has become a learned view among some that a human touch applied to technical strategies cannot only navigate the pitfalls of such distorted markets that have befallen fundamental traders, but capitalise upon it. In physical markets where the cost of processing and transporting commodities has seen little to loss making margins and fundamental paper trading yielding little, the ability to employ strategies that rely little upon these activities yet deliver strong returns has proved attractive to the more progressive merchant traders.

These developments are best viewed in the broader context of digitalisation, coupled with our progression in to an age where information is readily available to all, further blunting a fundamental trader’s ability to trade on proprietary information. Proco Commodities have written much on the topics of big data and machine learning and the benefits of being able to process such large volumes of this readily available data. The very same traders and market participants who are adopting the strategies explored in this article are also the first to experiment with and exploit the capabilities afforded by such technology.