Modelling A.I. in Economics

ARODB ASTRO RESOURCES NL (Forecast)

Outlook: ASTRO RESOURCES NL assigned short-term B1 & long-term Ba1 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 14 Dec 2022 for (n+1 year)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

Abstract

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. (Hernández-Nieves, E., Bartolomé del Canto, Á., Chamoso-Santos, P., Prieta-Pintado, F.D.L. and Corchado-Rodríguez, J.M., 2020, June. A machine learning platform for stock investment recommendation systems. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 303-313). Springer, Cham.) We evaluate ASTRO RESOURCES NL prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the ARODB stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

Key Points

  1. Trading Signals
  2. Operational Risk
  3. What is the use of Markov decision process?

ARODB Target Price Prediction Modeling Methodology

We consider ASTRO RESOURCES NL Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of ARODB 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(Stepwise 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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) i = 1 n s i

n:Time series to forecast

p:Price signals of ARODB 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?

ARODB Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: ARODB ASTRO RESOURCES NL
Time series to forecast n: 14 Dec 2022 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

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%

Adjusted IFRS* Prediction Methods for ASTRO RESOURCES NL

  1. When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
  2. If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.
  3. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
  4. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

ASTRO RESOURCES NL assigned short-term B1 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the ARODB stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

Financial State Forecast for ARODB ASTRO RESOURCES NL Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba1
Operational Risk 5287
Market Risk5146
Technical Analysis7490
Fundamental Analysis8267
Risk Unsystematic3557

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 785 signals.

References

  1. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
  2. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
  3. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
  4. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
  5. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
  6. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  7. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
Frequently Asked QuestionsQ: What is the prediction methodology for ARODB stock?
A: ARODB stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Stepwise Regression
Q: Is ARODB stock a buy or sell?
A: The dominant strategy among neural network is to Buy ARODB Stock.
Q: Is ASTRO RESOURCES NL stock a good investment?
A: The consensus rating for ASTRO RESOURCES NL is Buy and assigned short-term B1 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of ARODB stock?
A: The consensus rating for ARODB is Buy.
Q: What is the prediction period for ARODB stock?
A: The prediction period for ARODB is (n+1 year)

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