Modelling A.I. in Economics

MATX Stock: Set a stop-loss order (Forecast)

Outlook: Matson Inc. Common Stock is assigned short-term B1 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Multiple 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

Matson Inc. Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the MATX 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. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Graph 15

Key Points

  1. What is Markov decision process in reinforcement learning?
  2. What are main components of Markov decision process?
  3. Understanding Buy, Sell, and Hold Ratings

MATX Target Price Prediction Modeling Methodology

We consider Matson Inc. Common Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of MATX 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


F(Multiple Regression)5,6,7= 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):→ 6 Month R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of MATX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Reinforcement Machine Learning (ML)

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.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.

 

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

How do AC Investment Research machine learning (predictive) algorithms actually work?

MATX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: MATX Matson Inc. Common Stock
Time series to forecast: 6 Month

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

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 MATX Stock Prediction Model

  1. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
  2. For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
  3. If an entity previously accounted at cost (in accordance with IAS 39), for an investment in an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) (or for a derivative asset that is linked to and must be settled by delivery of such an equity instrument) it shall measure that instrument at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.
  4. An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.

*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.

MATX Matson Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1B1
Income StatementB3B1
Balance SheetBaa2Baa2
Leverage RatiosB3Caa2
Cash FlowB2B1
Rates of Return and ProfitabilityBa1B1

*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?

Conclusions

Matson Inc. Common Stock is assigned short-term B1 & long-term B1 estimated rating. Matson Inc. Common Stock prediction model is evaluated with Reinforcement Machine Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the MATX stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 594 signals.

References

  1. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
  2. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
  3. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  4. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
  5. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
  6. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
  7. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
Frequently Asked QuestionsQ: What is the prediction methodology for MATX stock?
A: MATX stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Multiple Regression
Q: Is MATX stock a buy or sell?
A: The dominant strategy among neural network is to Hold MATX Stock.
Q: Is Matson Inc. Common Stock stock a good investment?
A: The consensus rating for Matson Inc. Common Stock is Hold and is assigned short-term B1 & long-term B1 estimated rating.
Q: What is the consensus rating of MATX stock?
A: The consensus rating for MATX is Hold.
Q: What is the prediction period for MATX stock?
A: The prediction period for MATX is 6 Month

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