History Never Repeats…. Or does it?

History Never Repeats…. Or does it?

Mark Twain is quoted as saying that “History never repeats but it often rhymes”.   Whether history repeats or it rhymes, it is historical data that is causing many energy trading organizations to take a look at Predictive Analytics. So, what exactly is Predictive Analytics and why do we care about it when we think about for the Energy Trading and Risk Management systems and services?   Predictive Analytics is a line of advanced analytics that makes predictions about future events based on data collected from present and the past.   Predictive Analytics is enabled from a variety of methods including data mining, statistical modeling, machine learning, and artificial intelligence to examine the existing data we currently have in order to uncover patterns and correlations that can be used to calculate probabilities of the same or similar events occurring again in the future.

BP was recently quoted in a Bloomberg article that there is a “huge amount of information” about supply and demand that others do not have access to. We know that in-house trading arms of oil companies will often take speculative positions based on the information that they have at the time.   Having a huge amount of information and combining it with patterns which occurred in the past is arguably one way to give some energy traders an edge. That is of course Big Data in action.

Typically the larger companies have access to more data, because they are conducting more trading transactions than the smaller companies.   If that is the case then those large companies can have more insight in terms of historical patterns, as well as more information about current events that will be impacted by historical patterns.

Developing predictive analytical models from the Big Data that the larger companies are collecting is one way for the larger companies to figure out how, when, and what to trading in the future.

Or course energy traders cannot just look at the past or even the recent past, they need to have good information about the current environment too. Having good data about the current reality is also an important aspect of Big Data, as it provides more opportunities to make use of Predictive Analytics that have been developed by an organization.   If we don’t have good data to tell us the current reality, we could possibly misapply the analytical information we have generated in the past.

It could be argued that that Big Data favors the largest companies in world of the energy trading. Still the fact that Energy Trading and Risk Management systems are available to energy traders of all shapes and sizes does help the smaller firms continue to compete in this environment. Most of the Energy Trading and Risk Management packages available today have the ability or option to run live simulations of possible trading events and changes without risking the trading firm’s portfolio.

Companies utilizing these systems in this fashion can then examine and interpret the results of simulations in order make changes to the variables they do have control over. Those firms do this in an effort to optimize the potential outcomes or reduce the risk of their trading strategies before actually executing trades.

Of course not all Energy Trading and Risk Management packages are created equal.  Cost and functionality of the various Energy Trading and Risk Management packages can significantly impact the types of simulations available. Here again, smaller companies with smaller budgets may be at a disadvantage when it comes to the ability to leverage data and predictive models.

  • Posted by Lanshore
  • On August 22, 2015
Tags: Data, ETRM, gas, nearshore, oil, Trading