Analytics Edge Helps Commodities Pros

Commodity Traders Get Analytics Edge

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Commodities professionals have had a rough go of it over the past few years.  Deutsche Bank, JP Morgan Chase & Co.  and Morgan Stanley all slashed commodities trading jobs in 2013, according to Bloomberg.  Weak performance in the power, gas and investor products sectors as well as increased regulatory compliance costs are to blame. However, the news isn’t all bad. In the new era of big data, the information traders need is at their fingertips. Smart commodity professionals are learning to use an analytics edge to make decisions about which companies to trade, as well as to test the effect of potential trades on their portfolios.  As they’ve quickly discovered, while efficiently analyzing data may be the key to their success, the real value lies in the questions the data points to, NOT the answers.

Thanks, Dodd Frank

The Dodd Frank Wall Street Reform and Consumer Protection Act’s strict trade record management rules may be to thank.  The new financial service regulations such as those introduced by the act  mean that record keeping for all financial records and trades are now longer and more detailed, taking more time to complete. However, there’s gold in them thar records – as long as you know where to look.

Analytics edge is in the questions, NOT the answers

And that’s where the analytics edge comes into play.

Rapid advances in technology mean that  more business transactions are happening faster than ever before.  The more detail recorded for each of these transactions, the more information produced to show trends and create forecasts. However, until recently, we just didn’t have the capabilities to process this information fast enough to apply it to trades.

Efficient cloud-based processing applications and cutting-edge analytics programs are changing that. This powerful combination is a godsend for the transaction-heavy commodity trading industry, according to an article in Banking Technology Magazine.  Modeling activities that used to take hours can now occur in a matter of minutes, and may reveal previously overlooked possibilities.  Analytics  allows unlimited iterations of the question  “What if we do this?” to be answered faster than ever before. This gives traders using analytics an edge over their non-analytics-savvy peers.

Analyze This: Commodity Trading Desk Performance

The silver lining for commodities pros grumbling over the detail required by the new Dodd-Frank regulations for each trade is that the detail makes the data more useful.  These records, combined with the lightning fast processing software and powerful analytics programs  generate a clearer picture of  a commodities firm’s performance than ever before.

For example, a commodity trader who wants to model the impact of various trading strategies on the company’s EPS must input relevant and specific data points. These include things like  current trading positions, current and historical settlement, forward pricing, trading volumes, mark-to-market valuations and interest rates. Once the data is there in the form of transactional records, modeling using analytics programs becomes much less time-consuming.

Alternatively, evaluating trading department performance by comparing the return on investment (ROI) against internal benchmarks or the ROI of other companies’ trading departments is easier with the combination of big data and analytics.

Since financial firms use large-scale records management systems to handle the massive amounts of data they generate, it’s easy to organize and find the records required for each modeling exercise. Now, with cloud technology, better record keeping and new financial analytics programs,  it takes mere minutes for a model to emerge – a model that can point to the best trading strategy with respect to EPS.

These new insights may lead to bigger profits for commodities trading departments, boosting revenue and share prices of financial institutions that are suffering from increased costs of meeting new regulatory requirements and the volatility of the commodities markets.

Analyze That: Study Commodity Company Behavior

Commodity trading professionals have discovered that the main business goal of a firm is a good starting point in deciding whether to add the business to its book.

Analytics programs lead them down data roads that were once too complex or expensive to pursue. One way traders make use of analytics is to get a clear picture of how well a company’s spending aligns with its business goals.  They do this by using cash forecasting tools and publicly available working capital figures from financial statements.

Commodities traders also rate the logistical efficiency of a company by including publicly available data on transportation costs,  government fees and tariffs, as well as company-specific data on inventory. In the same way that banks use big data and analytics to uncover opportunities for new markets, commodities pros comb through public and private information to uncover new regions, companies, and commodities to invest in.

For analytics programs to work effectively, however, they must focus on specific goals, and most importantly, they must analyze the right information.

Getting to the right data quickly is the key to developing questions that will ultimately lead to more business opportunities. Despite the increased workload that Dodd Frank compliance brings to trading professionals, the improved record keeping may make it easier to find quality data. Additionally, the rapid development of new automated analytics programs is helping to uncover new areas of opportunity for trading firms large and small.

 

Analytics Edge Helps Commodities Proshttp://www.marketinganalyticstoday.com/wp-content/uploads/2014/08/axstj-tradedsc_2548-stj2-1013-6526-1024x682.jpghttp://www.marketinganalyticstoday.com/wp-content/uploads/2014/08/axstj-tradedsc_2548-stj2-1013-6526-300x300.jpg Sarita Harbour AnalyticsFinance ProsSaaSStartups,
Commodities professionals have had a rough go of it over the past few years.  Deutsche Bank, JP Morgan Chase & Co.  and Morgan Stanley all slashed commodities trading jobs in 2013, according to Bloomberg.  Weak performance in the power, gas and investor products sectors as well as increased regulatory...
Commodities professionals have had a rough go of it over the past few years.  Deutsche Bank, JP Morgan Chase & Co.  and Morgan Stanley all slashed commodities trading jobs in 2013, according to <a href="http://www.bloomberg.com/news/2013-12-05/deutsche-bank-to-shrink-in-commodities-as-revenue-slides.html">Bloomberg</a>.  Weak performance in the power, gas and investor products sectors as well as increased regulatory compliance costs are to blame. However, the news isn't all bad. In the new era of big data, the information traders need is at their fingertips. Smart commodity professionals are learning to use an analytics edge to make decisions about which companies to trade, as well as to test the effect of potential trades on their portfolios.  As they've quickly discovered, while efficiently analyzing data may be the key to their success, the real value lies in the questions the data points to, NOT the answers. <h2>Thanks, Dodd Frank</h2> The Dodd Frank Wall Street Reform and Consumer Protection Act's strict trade record management rules may be to thank.  The new financial service regulations such as those introduced by the act  mean that record keeping for all financial records and trades are now longer and more detailed, taking more time to complete. However, there's gold in them thar records - as long as you know where to look. <h2>Analytics edge is in the questions, NOT the answers</h2> And that's where the analytics edge comes into play. Rapid advances in technology mean that  more business transactions are happening faster than ever before.  The more detail recorded for each of these transactions, the more information produced to show trends and create forecasts. However, until recently, we just didn't have the capabilities to process this information fast enough to apply it to trades. Efficient cloud-based processing applications and cutting-edge analytics programs are changing that. This powerful combination is a godsend for the transaction-heavy commodity trading industry, according to an article in <a href="http://www.bankingtech.com/180501/commodity-trading-analytics-exploiting-a-new-set-of-options/?utm_source=rss&utm_medium=rss&utm_campaign=commodity-trading-analytics-exploiting-a-new-set-of-options" target="_blank">Banking Technology Magazine</a>.  Modeling activities that used to take hours can now occur in a matter of minutes, and may reveal previously overlooked possibilities.  Analytics  allows unlimited iterations of the question  "What if we do this?" to be answered faster than ever before. This gives traders using analytics an edge over their non-analytics-savvy peers. <h2>Analyze This: Commodity Trading Desk Performance<strong> </strong></h2> The silver lining for commodities pros grumbling over the detail required by the new Dodd-Frank regulations for each trade is that the detail makes the data more useful.  These records, combined with the lightning fast processing software and powerful analytics programs  generate a clearer picture of  a commodities firm's performance than ever before. For example, a commodity trader who wants to model the impact of various trading strategies on the company's EPS must input relevant and specific data points. These include things like  current trading positions, current and historical settlement, forward pricing, trading volumes, mark-to-market valuations and interest rates. Once the data is there in the form of transactional records, modeling using analytics programs becomes much less time-consuming. Alternatively, evaluating trading department performance by comparing the return on investment (ROI) against internal benchmarks or the ROI of other companies' trading departments is easier with the combination of big data and analytics. Since financial firms use large-scale records management systems to handle the massive amounts of data they generate, it's easy to organize and find the records required for each modeling exercise. Now, with cloud technology, better record keeping and new financial analytics programs,  it takes mere minutes for a model to emerge - a model that can point to the best trading strategy with respect to EPS. These new insights may lead to bigger profits for commodities trading departments, boosting revenue and share prices of financial institutions that are suffering from increased costs of meeting new regulatory requirements and the volatility of the commodities markets. <h2>Analyze That: Study Commodity Company Behavior</h2> Commodity trading professionals have discovered that the main business goal of a firm is a good starting point in deciding whether to add the business to its book. Analytics programs lead them down data roads that were once too complex or expensive to pursue. One way traders make use of analytics is to get a clear picture of how well a company's spending aligns with its business goals.  They do this by using cash forecasting tools and publicly available working capital figures from financial statements. Commodities traders also rate the logistical efficiency of a company by including publicly available data on transportation costs,  government fees and tariffs, as well as company-specific data on inventory. In the same way that <a title="Bank’s Use of Big Data and Analytics Holds Key Lessons for Entrepreneurs" href="http://www.marketinganalyticstoday.com/big-data-and-analytics-hold-lessons-for-entrepreneurs/">banks use big data and analytics</a> to uncover opportunities for new markets, commodities pros comb through public and private information to uncover new regions, companies, and commodities to invest in. For analytics programs to work effectively, however, they must focus on specific goals, and most importantly, they must analyze the right information. Getting to the right data quickly is the key to developing questions that will ultimately lead to more business opportunities. Despite the increased workload that Dodd Frank compliance brings to trading professionals, the improved record keeping may make it easier to find quality data. Additionally, the rapid development of new automated analytics programs is helping to uncover new areas of opportunity for trading firms large and small.  

Sarita Harbour

Sarita Harbour is the owner of Harbour Content Development and founder of Marketing Analytics Today. She's crazy about her family, the great Canadian North, and Skor Blizzards.

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