The never-ending pursuit of alpha in energy and commodity markets these days has driven many utilities, oil and gas firms, corporates, FCMs and hedge funds to widen their data horizons. Driven by weak volatility and compressed margins, energy and commodity traders and portfolio managers are seeking new sources of input, wrapped in more complex analysis. I recently spoke on the webinar panel, hosted by Energy Risk and FIS, titled "Finding Alpha in Uncertain Markets." As I reflect on our discussion, I wanted to share a few takeaways:
1. Internal and external pressures are increasing the need and supply of data in energy markets.
Political uncertainty, increasing regulation, and ongoing market, price and credit risk concerns are forcing energy and commodity trading and risk management executives to improve accountability to regulators and business stakeholders. Thanks to advances in cloud technology, artificial intelligence, the Internet of Things (IoT) and the ability to more accurately track physical assets, an exponentially increasing amount of data is becoming available.
2. The use of data now favors profit-generation over risk-avoidance.
In a live survey taken during the webinar, almost 60 percent of participants indicated that they used data primarily to generate profit, while nearly 40 percent claimed they used data to avoid exposure to risk. This focus on maximizing alpha was seen as a shift from the more conservative, risk-averse days following 2008.
3. Data acquisition and analysis are still done in-house.
When asked how they approach data acquisition and analysis, 43% of the webinar audience said they were more inclined to do both in-house. Conversely, just 16 percent preferred to outsource both – data acquisition and analysis - than commit internal resources to the task.
4. Disruptive, non-traditional sources and solutions are on the horizon.
Given a rapidly evolving energy and commodity data landscape, panelists speculated over what technologies could provide an advantage. With more data sources converging at decision desks, open-source API solutions were seen as highly desirable. Likewise, predictive modeling and machine learning tools could support increased trading frequencies with customized trading algorithms. In addition, non-traditional data, from sources such as social media, GPS and mobile feeds, may soon be providing decision-makers with trending information and pinpoint accuracy.
For energy and commodity traders and portfolio managers seeking to cut risk and maximize alpha, understanding how to effectively leverage available data can make all the difference.