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Using analytics to transform trading operations

Read how commodity trading organizations are using analytics to identify new revenue streams in today’s volatile economic environment.

Overview

As trading organizations strive to maintain and improve profitability in volatile times, trading technologies can help identify specific areas where cost reduction opportunities exist. Examples of cost-related analytics include commodity transportation costs, inventory fees, tariffs and financing costs.

A strong understanding of a trading organization’s goals and applying cost-related analytics to determine if spending is commensurate with those goals allow organizations to optimally use their existing resources and drive more efficient value creation.

Findings

While there is no comprehensive list of questions that trading organizations can use to unlock the value of their trading technologies, they can focus on understanding the commonalities of each focus group they are serving. Key questions that can be asked to each segment include:

For stakeholders

  • What is the estimated impact of the trading portfolio on earnings per share?

  • How does the return on equity or return on invested capital within the trading business compare with internal targets?

  • What level of catastrophic risk exists within the trading portfolio and how that may impact current and future earnings?

For driving strategy

  • What current trading strategies generate the improved profit for the firm?

  • How or why does the company make or lose money in a particular book or strategy?

  • How do you leverage analytics to assess opportunities within new markets?

  • Is there a need to apply descriptive or predictive analytics to support a particular business situation?

Analysis

The term ‘analytics’ seems to be everywhere these days – across every industry and in just about every facet of technology. Commodity trading organizations are no exception, as many of them are looking to identify new revenue streams in today’s volatile environment.

As trading organizations look to optimize the value of their assets or speculate on future commodity price movements, they employ many forms of analysis to help drive their trading strategies. However, the excitement over these new ways of driving finite strategy has distracted attention from the crucial importance of asking the right questions. With the proliferation of analytical tools in the marketplace, there are now more technology options than ever to support decision-making.

Where many trading firms neglect to focus, however, is in defining the exact questions they are looking to have analytical tools help answer. It is this lack of planning that may result in companies failing to realize the full benefit of the analytics tools or services they purchase. There are opportunities for management and traders alike to learn a great deal by leveraging these technologies provided they are looking to solve meaningful questions.

Future of Electronic Trading

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Recommendations

Though many service providers in the analytics space claim to have packaged solutions that take the guess work out of this process, these tools do not always provide the insights or value firms are looking for. Accenture believes that one size does not fit all. Questions differ for each firm as their corporate and trading strategies vary.

Configuring trading technologies based on a firm’s commodities trading requirements is important to gain effectiveness. The quality of data supplied to any analytical tool is also critical. Many trading organizations employ a corporate data warehouse to aggregate and store all data, and organize and generate necessary inputs.

This provides a central location for users to access, allows seemingly unrelated data to be viewed side-by-side to uncover trends and creates a single place to store verified information. Clients can customize their data warehouse to best suit their needs and align its structure with the key questions they are trying to answer with their analytics engines.

Not only will calibrating analytical models properly save computing time, but it will also offer companies additional time to evaluate the results and refine their initial queries to see how the results may change. This is critical for the trading industry as markets evolve and firms refine trading strategies based on model outputs.

Authors

Faron Schonfeld serves as the North America Trading Operations Lead and is a member of the Accenture Management Consulting - Finance & Enterprise Performance practice. Schonfeld has worked with clients to evaluate and implement trading and risk management strategies and operational solutions across energy, utility and financial clients.

Andrew Vo serves as the Global Trading Operations Lead and is a leader within the Accenture Management Consulting - Finance & Enterprise Performance practice. Vo has worked with leading clients across energy, utility, products and financial clients to evaluate and implement world-class commodities trading and risk management solutions.