TST Energy Platform

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  System Objective

 Major Directional Changes

One of the most valuable functions of THINKSheet models is to signal major directional changes.  This signal is usually a major variation / difference / divergence between the price forecasts by the Fixed Drivers and Dynamic Drivers Models.  The catalysts for major directional changes are usually QL factors not yet reflected in the values of the QT/fixed drivers model.  Skilled professionals can identify directional changes using a logical approach that includes the QL factors.  An example is the qualitative process John Paulsen used to short the sub-prime bubble, which Gregory Zuckerman described in The Greatest Trade Ever.  THINKSheet helps identify these directional changes by showing users the QL and QT outputs in an easy-to-understand, structured format. This systematic approach is far superior to relying solely on ad hoc or textual analysis.

An example of how THINKSheet signals major movements is the Morgan Stanley TST Equities model below.

System Components

Collaboration System Components

TST Energy Systems includes a set of components that work together to help your teams collaborate more effectively with colleagues, third parties and constituents.  You can better manage the information, drivers, thought processes and answers you share internally and with others with maximum efficiency due to THINKSheet’s intrinsic organization advantage and ability to quantify and visualize the most important thought process at each step in the process.

The components include a Best-Thought-Process methodology and a set of tools for implementing it.  THINKSheet’s versatility in quantifying language enables us to model all types of analysis – deductive & inductive, qualitative & quantitative, comparative & individual analysis, Big Data – using any type of language – numbers, statistics, true/false, lists, ranges, metaphors, acronyms,  and hierarchies.  THINKSheet’s software tools enable us to quantify the drivers in the QL+QT models, build databases of the drivers, make our research reports shorter yet more comprehensive, develop and visualize histories and indices, and make presentations in multi-media formats, including PPT, photographs and videos.


The Drivers (QL+QT) Management System – The Back End System

Due to THINKSheet’s ability to quantify virtually any type of language, the Dynamic Drivers (QL+QT) Back-End System includes the same types of databases and performance histories as quantitative systems.  models

  • drivers database system
  • price and forecashhistories
  • prediction enhancement system..

The Drivers Database System

TST Oil Prices includes a Drivers Database with the following components:

  • 100+ drivers
  • Tier 1 & 2 categories
  • Criteria & definitions.
  • Ranking & filtering features.

The Drivers Database helps us manage both the QL and QT drivers of our decisions in one efficient process.  The database is organized based on the drivers taxonomy above. The taxonomy and tools enable us to (1) organize the drivers, (2) rank them by relative importance, (3) filter them into the taxonomy categories, and (4) feed the most important ones into TST models. The Drivers Management System enables us to streamline all of our information management, analytical and communications processes. We provide the taxonomy and drivers for our clients to use as a starting point, and we work with you to extend, refine and update them to meet your specific needs.

Prediction Enhancement System

We use our proprietary Prediction Enhancement System to track, evaluate, and improve our system forecasts.  We are back-testing the Fixed Drivers (QT) Model over a 5-year period.  However, the Dynamic Drivers (QL) Model cannot be back-tested because there is no historical set of QL equivalents to use.  For example, the future economic policies of the Xi Jinping leadership in China will have a major impact on the performance of the Chinese economy, which in turn will have a major impact on the Chinese demand for oil, and thus oil prices.  No data sets exist that would enable us to back-test these policies.  The same is true for the impact on supply of the connections among the US, Saudi Arabia, Iran, Iraq, Yemen, ISIS, Al Queda, and the Houthis.

Our solution to this problem is to systematically store our Dynamic Drivers Model forecasts and logic in a database and analyze them each week, to see which judgments for each driver were right and which, if any, were wrong.  We then document the analysis and incorporate the lessons learned into the Dynamic Drivers Model on a week-by-week basis.

The methodology for testing the Dynamic Drivers is based in part on Westlaw, the extraordinarily systematic approach used by the $300-billion-a-year legal profession to develop its precedent system over the past hundred years.  The Westlaw system conceptualizes and organizes the entire body of American law, using a taxonomy and key number system that includes 100,000+ topics.  This enables legal researchers to find the issues and language relevant to their cases quickly.  We have enhanced the Westlaw approach by (1) building a taxonomy and key number system similar to Westlaw’s and (2) adding the quantification of the individual drivers and the overall logic using best quantitative practices from finance.

Our process includes these steps:

  1. Decide which models, charts, text & other raw materials to use
  2. Copy models, charts, and text analysis into a database
  3. Analyze the forecasts and logic relative to actual prices and drivers
  4. Document the analysis in terms of lessons learned
  5. Enhance the models with the lessons learned
  6. Refine the criteria for the drivers
  7. Add new drivers to the Drivers Database and rerank them
  8. Determine the interconnections among the drivers
  9. Enhance the Drivers Database taxonomy and key number system.

The benefits of this system are constantly improving models, drivers, criteria, weights, rankings, judgments with respect to impact and probabilities, logic and language.  The Prediction Enhancement System is accessible to our clients and our team via our THINKSheet Cloud, enabling everyone to collaborate with maximum productivity.

Our Expertise & Use

THINKSheet Energy Expertise

THINKSheet Energy Systems, Inc. is a 50%/50% joint venture between THINKSheet, Inc and Energeia LLC, a London commodities trading company and enterprise software developer.  Energeia’s founder, Dr. Timothy McGarvey, heads the development of our TST Energy Systems product.  Dr. McGarvey has over 15 years of experience in energy, commodity markets (oil, natural gas, electricity), and software development.  He was also a nuclear power engineer with the US Navy, managing the nuclear operations of a nuclear submarine for 3 years.

Nuclear Sub 2

Energeia’s team includes Dr. Sanatan Rai, Chief Quantitative Architect, and  Dr. Guy Bormann, Head of Quantitative Development, who have 15 and 10 years, respectively, of experience in enterprise-level software development and energy applications.

See Team for bios of Energeia and THINKSheet.teams.

We Plan To Use TST Energy to Trade Futures

We plan to use TST Energy to trade energy futures for Energeia’s trading fund.   We will work together with Energeia to operate the model with Dr. McGarvey making the final trading decision.  We are in the process of testing the system now with the expectation of actually trading with it sometime in September.

You can use TST Energy Systems and TST Equities Systems together to manage your stock selection process for your energy stocks.  You can use TST Energy Systems to model the macro factors impacting your stocks, and to model the individual stocks. TST Equities is our most highly developed product in terms of model templates.  We have 20 model templates for a variety of industry groups and diversified portfolios of several kinds.  We have co-developed these templates with sell- and buy-side analysts, portfolio managers and traders.  The templates enable you to rank any set of energy stocks by relative risk/reward based on any desired set of QL+QT metrics and scenarios.

See TST Equities Systems

TST Equities Systems share the same platform as TST Energy Systems.  It enables you to forecast major directional changes in your stocks and highlight the reasons why.  For example, Morgan Stanley Exhibit 4 displays 1 long-term column, Our Rating, and 3 short-term ranking columns: Overall Ranking, Quantitative Ranking, and Critical Issues Ranking.  The rankings highlight the stocks with the greatest potential movement in the short term in contrasting green and red colors. You can see at a glance that the analyst anticipates that Moody’s and Knight Ridder will move significantly: Moody’s down (3/17) and Knight Ridder up (15/1). Other major movers are Meredith (4/17), The NYTimes (20/3) and Dow Jones (19/6).


The analyst described his reasoning that the QT factors are efficiently priced in whereas the qualitative factors are not.

In re-introducing and, we believe, enhancing our stock ranking methodology, eleven valuation inputs and fourteen financial measures (all individually ranked and scaled) represent only 60% of the weighted score for each stock.  The balance, or 40% of the weighted score, is the quantification of critical/subjective issues or catalysts, risks, and — what we call — the “X-factor” (or unique attribute per stock).  We are attempting to supplement largely efficiently priced-in valuation and financial measures for each stock by quantifying our major thoughts / concerns / expectations for each company/stock in a systematic method.

You can see a complete description of the Morgan Stanley THINKSheet models at TST Equities Systems.