It’s no surprise that business pressures can increase the value of optimized trading for energy companies. Specifically, lower commodity prices and more competition in energy markets have put pressure on profits and margins, which, in turn, increase the importance of fast and accurate risk reporting.
Internally, faster reporting and optimization cycles are required to in order to maintain a competitive position. Simultaneously, position reporting must maintain a high level of granularity, as regulators and auditors seek balance at 60-, 30-, and sometimes even 15-minute intervals.
While organizations continue to move toward intraday position reporting, the breadth of detailed data and the frequent time increments in which they are provided are not adequately leveraged through the normal risk reporting process. Consequently, data end up as part of manual portfolio optimization that occurs daily, weekly, or even less frequently.
It is possible, however, to enhance risk reporting with tools found in other industries, allowing for faster integration of detailed supply and demand data. Those companies that look outside of traditional trading tools will be able to rapidly assess, evaluate, and react to this critical data and put themselves in a stronger position to make more timely and informed decisions about future trades and contracts.
The value of integrating supply and demand data at intraday increments to intraday risk reporting lies in reduced risk in the portfolio, lower fines, and/or penalties for being out of balance and, ultimately, in the improved optimization of trading strategy (and by extension, a more competitive advantage in the market). Operationally, integrated data improves a firm’s ability to adapt trading strategies to account for unexpected supply changes more quickly. Furthermore, timelier accounting for supply and demand data will reduce the risk of short- and long-term contracts and provide improved insight into counterparty and contract profitability.
Few organizations are actually using either supply or demand generation data on an intraday basis, despite the obvious benefits. Processes for position reporting are organized around intraday schedules, but supply and demand data are often unaccounted for in these processes. In each case, trading desks are forced to perform a “true up” by means of manual reconciliation of the data to position reporting days or weeks after the fact, sacrificing margin or even moving from profit to loss due to “unexpected” changes.
Therefore, the choice facing trading organizations today is between an analytic capability that allows one to look backward and report what happened, and a capability that allows an organization to understand what is happening as it happens and adapt and react to it in real time.
Current Systems Are Not Sufficient
The difficulty trading organizations face when assessing the data in a more timely fashion stems from a perceived inability to apply the data to risk reporting more frequently, while ensuring that the data is granular enough to revise the position in quarter-hour increments. Attempts at systemic solutions to this problem result in one of two untenable outcomes: either the information can be processed intraday, but at insufficient granularity, which limits the solution’s ability to remove the need for post-day adjustments, or the data is granular but the time to acquire, process, and analyze the data is too long to allow it to be used in the same day.
For most trading organizations, it has not been possible to create a systemic solution for these problems within a time and price range that reflects value. Furthermore, attempts at integrating this data within the risk reporting process have taken more time and cost more money than has been gained in profitability. For those that have been completed, many other attempts have failed or simply been prematurely abandoned due to either the exorbitant expense or lack of demonstrable success.
The myth prevalent within many trading organizations is that these issues cannot be resolved without a massive IT investment. This myth leads trading organizations to believe there are only two possibilities: repeated and increasingly expensive attempts to create tools to integrate this data or giving up altogether and implementing work-arounds or reduce risk limits to allow the enterprise to simply live with the existing problem.
In reality, companies are simply trying to solve the problem with the wrong tools: focusing on using the typical tools of the trading world, which inevitably leads to a dead end and includes implementing enterprise systems such as CTRM (commodity trading and risk management) tools or back-office financial systems. These options, besides being large and complex, also require a significant investment of time and money and cannot be quickly adapted. While Excel-based tools are flexible, they lack both the ability to handle larger volumes of daily incoming data and the transparency and auditing ability needed to provide confidence at scale.
Casting Light on Suitable Solutions
In order to leverage the full range of available supply and demand data on an intraday basis, trading organizations must look outside of typical CTRM or business intelligence tools. As an example, industries such as telecommunications and manufacturing make good use of process-driven analytic tools in order to address analogous issues in their spaces. These process-driven tools have many of the key elements needed to ensure that detailed data is quickly and granularly analyzed. Specifically, they are data-architecture-independent, they allow business users to create and change analytics quickly and without a major IT engagement, and they allow both logic and data to be modeled in the same tool.
Equally important is that process-driven solutions support an analytic methodology where discovery and analysis happen simultaneously—occurring when business users create analytic tools collaboratively even as they investigate the data itself. This combination of technology and methodology enables energy companies to enhance their risk reporting and fulfill their core need for integrating supply and demand data intraday. When looking to acquire this type of technology, organizations should consider the following critical criteria and select a solution that:
- Enables the organization to access and apply data quickly, including acquiring and analyzing data in near-real-time.
- Allows the organization to maintain sufficient data granularity in order to improve position reporting at quarter-hour increments.
- Is able to analyze supply/demand data in the context of trading and contract logic.
- Supports an agile analytic methodology, allowing business teams to adapt and tweak analytics and explore new data sources quickly and easily.
- Creates output that can be audited and tracked—providing confidence in output and reducing the likelihood of needing revisions.
- Delivers value within three to six months and can consistently adapt itself to new data inputs and analytics within days or weeks.
By leveraging this class of tool, it is possible to create a solution that is managed and maintained within the process analytics team or perhaps in the mid-office. This eliminates the risk and expense of a large-scale IT implementation but does so without sacrificing the ability to convert these analytics into standing operational controls and while maintaining transparency and audit capabilities.
Illuminating Profits and Reducing Risks
It is easy to ignore solutions from other industries when attempting to expand the analytic capability for energy trading. Indeed, both heads of trading and IT management often raise objections to such an approach, believing that trading is unique or too mathematically complex to find guidance in other industries, or that only companies steeped in trading and risk management expertise can possibly provide solutions in a timely, cost-effective manner.
However, these objections do not stand up to investigation. In telecommunications, these solutions are already in use, having been proven in maximizing revenue for billion-dollar industries and working with millions of records across multiple systems. Simply put, experience in other verticals has shown that complex analytics can be implemented quickly and efficiently by leveraging solid technology and proven best practices and expertise in process, logic, and data.
In order to successfully adapt solutions from other vertical industries to energy trading, organizations should take an approach based on quick timelines and minimal risk. Big-bang solutions should be avoided; more attention should be focused on small systems that address core parts of the risk-reporting process such as the integration of generation data, analytics, etc.
Companies should begin by working with proofs of concept as a way to confirm a technology’s ability to perform a function and through proofs of value to test logic, data, and analytics. By keeping initial timelines and investments short, organizations will maintain more flexibility to mix and match technologies and avoid becoming trapped in a substandard solution due to large investments of time and money.
Finally, along with these new tools, existing trading and risk management technologies also have a role to play. When investigating technologies, organizations should pay attention to the ease with which new technologies can be integrated into existing architectures and data models. Even the most simple, elegant tool can result in a blown budget due to integration costs.
Market, industry, and organizational pressure all suggest that organizations that successfully improve the accuracy and timeliness of their risk reporting stand to benefit from improved margin, reduced controls, and the ability to more quickly move in the market. New tools from outside the traditional trading or utilities space can provide the needed functionality to quickly and cost-effectively create this expanded analytic capability for any trading desk. With a little technological planning, the future will be very bright not only in terms of profits but also in lowered risk.
—Edward Cuoco is the director of utilities and energy markets and Victor Milligan is chief strategy and marketing officer at Martin Dawes Analytics, a process analytics software provider.