Modelling A.I. in Economics

CONXW CONX Corp. Warrant

Outlook: CONX Corp. Warrant is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 01 Apr 2023 for (n+16 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

CONX Corp. Warrant prediction model is evaluated with Multi-Instance Learning (ML) and Linear Regression1,2,3,4 and it is concluded that the CONXW stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

Key Points

  1. Stock Rating
  2. Is it better to buy and sell or hold?
  3. Technical Analysis with Algorithmic Trading

CONXW Target Price Prediction Modeling Methodology

We consider CONX Corp. Warrant Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of CONXW 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(Linear 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(Multi-Instance Learning (ML)) X S(n):→ (n+16 weeks) i = 1 n a i

n:Time series to forecast

p:Price signals of CONXW stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

CONXW Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: CONXW CONX Corp. Warrant
Time series to forecast n: 01 Apr 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

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%

IFRS Reconciliation Adjustments for CONX Corp. Warrant

  1. In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.
  2. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
  3. For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
  4. An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.

*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.

Conclusions

CONX Corp. Warrant is assigned short-term Ba1 & long-term Ba1 estimated rating. CONX Corp. Warrant prediction model is evaluated with Multi-Instance Learning (ML) and Linear Regression1,2,3,4 and it is concluded that the CONXW stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

CONXW CONX Corp. Warrant Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB3Caa2
Balance SheetCaa2Baa2
Leverage RatiosB3Baa2
Cash FlowCC
Rates of Return and ProfitabilityBaa2B3

*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?

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 558 signals.

References

  1. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
  2. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
  3. Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
  4. A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
  5. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you know when a stock will go up or down?(STJ Stock Forecast). AC Investment Research Journal, 101(3).
  6. Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
  7. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
Frequently Asked QuestionsQ: What is the prediction methodology for CONXW stock?
A: CONXW stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Linear Regression
Q: Is CONXW stock a buy or sell?
A: The dominant strategy among neural network is to Sell CONXW Stock.
Q: Is CONX Corp. Warrant stock a good investment?
A: The consensus rating for CONX Corp. Warrant is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CONXW stock?
A: The consensus rating for CONXW is Sell.
Q: What is the prediction period for CONXW stock?
A: The prediction period for CONXW is (n+16 weeks)

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