Modelling A.I. in Economics

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Outlook: GEL Genesis Energy L.P. Common Units is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
ML Model Testing : Statistical Inference (ML)
Hypothesis Testing : Pearson Correlation
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.


Key Points

Genesis Energy L.P. stock may rise due to increased demand for electricity, bolstering its revenue. The company's focus on clean energy initiatives may attract investors seeking sustainable investments. However, potential regulatory changes in the energy sector could impact Genesis's operations and financial performance.

Summary

Genesis Energy is an independent power producer and marketer of energy commodities, providing wholesale electricity, natural gas, and related services to customers in the United States. The company owns and operates a fleet of fossil-fueled and renewable energy generation facilities, and utilizes advanced technologies to optimize energy production and delivery. They provide innovative solutions to meet the evolving energy needs of their customers.


Genesis Energy plays a crucial role in the energy sector, ensuring reliable and efficient power generation while transitioning towards a cleaner energy future. Focused on sustainability, the company invests in renewable energy sources and incorporates environmental responsibility into its operations. As an industry leader, Genesis Energy is committed to providing clean, affordable, and reliable energy solutions to its customers and contributing to the overall energy security of the United States.

GEL
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ML Model Testing

F(Pearson Correlation)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Statistical Inference (ML))3,4,5 X S(n):→ 3 Month e x rx

n:Time series to forecast

p:Price signals of GEL stock

j:Nash equilibria (Neural Network)

k:Dominated move of GEL stock holders

a:Best response for GEL target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

GEL Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

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Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Income StatementBaa2B2
Balance SheetCaa2Ba3
Leverage RatiosB3C
Cash FlowB3Baa2
Rates of Return and ProfitabilityCaa2Ba2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.This exclusive content is only available to premium users.

References

  1. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
  2. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
  3. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
  4. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
  5. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
  6. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
  7. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press

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