Modelling A.I. in Economics

ANALYZING MUSA STOCK: A COMPREHENSIVE EVALUATION OF GROWTH POTENTIAL AND INVESTMENT OPPORTUNITIES (Forecast)

Outlook: Murphy USA Inc. Common Stock is assigned short-term Baa2 & long-term B3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Logistic Regression
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.


Summary

Murphy USA Inc. Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the MUSA stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.5 According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy

Graph 41

Key Points

  1. Reinforcement Machine Learning (ML) for MUSA stock price prediction process.
  2. Logistic Regression
  3. Investment Risk
  4. How useful are statistical predictions?
  5. How can neural networks improve predictions?

MUSA Stock Price Forecast

We consider Murphy USA Inc. Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of MUSA stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4


Sample Set: Neural Network
Stock/Index: MUSA Murphy USA Inc. Common Stock
Time series to forecast: 1 Year

According to price forecasts, the dominant strategy among neural network is: Buy


F(Logistic Regression)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(Reinforcement Machine Learning (ML)) X S(n):→ 1 Year e x rx

n:Time series to forecast

p:Price signals of MUSA stock

j:Nash equilibria (Neural Network)

k:Dominated move of MUSA stock holders

a:Best response for MUSA target price


Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.5 In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.6,7

 

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

How do PredictiveAI algorithms actually work?

MUSA 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%

Financial Data Adjustments for Reinforcement Machine Learning (ML) based MUSA Stock Prediction Model

  1. The requirement that an economic relationship exists means that the hedging instrument and the hedged item have values that generally move in the opposite direction because of the same risk, which is the hedged risk. Hence, there must be an expectation that the value of the hedging instrument and the value of the hedged item will systematically change in response to movements in either the same underlying or underlyings that are economically related in such a way that they respond in a similar way to the risk that is being hedged (for example, Brent and WTI crude oil).
  2. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
  3. In accordance with the hedge effectiveness requirements, the hedge ratio of the hedging relationship must be the same as that resulting from the quantity of the hedged item that the entity actually hedges and the quantity of the hedging instrument that the entity actually uses to hedge that quantity of hedged item. Hence, if an entity hedges less than 100 per cent of the exposure on an item, such as 85 per cent, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from 85 per cent of the exposure and the quantity of the hedging instrument that the entity actually uses to hedge those 85 per cent. Similarly, if, for example, an entity hedges an exposure using a nominal amount of 40 units of a financial instrument, it shall designate the hedging relationship using a hedge ratio that is the same as that resulting from that quantity of 40 units (ie the entity must not use a hedge ratio based on a higher quantity of units that it might hold in total or a lower quantity of units) and the quantity of the hedged item that it actually hedges with those 40 units.
  4. As with all fair value measurements, an entity's measurement method for determining the portion of the change in the liability's fair value that is attributable to changes in its credit risk must make maximum use of relevant observable inputs and minimum use of unobservable inputs.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

MUSA Murphy USA Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Baa2B3
Income StatementBa1Ba3
Balance SheetBa3B2
Leverage RatiosBaa2C
Cash FlowBa2B3
Rates of Return and ProfitabilityBaa2C

*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?

References

  1. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
  2. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
  3. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
  4. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
  5. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
  6. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
  7. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
Frequently Asked QuestionsQ: Is MUSA stock expected to rise?
A: MUSA stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Logistic Regression and it is concluded that dominant strategy for MUSA stock is Buy
Q: Is MUSA stock a buy or sell?
A: The dominant strategy among neural network is to Buy MUSA Stock.
Q: Is Murphy USA Inc. Common Stock stock a good investment?
A: The consensus rating for Murphy USA Inc. Common Stock is Buy and is assigned short-term Baa2 & long-term B3 estimated rating.
Q: What is the consensus rating of MUSA stock?
A: The consensus rating for MUSA is Buy.
Q: What is the forecast for MUSA stock?
A: MUSA target price forecast: Buy

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