Modelling A.I. in Economics

AFRIW Forafric Global PLC Warrants (Forecast)

Outlook: Forafric Global PLC Warrants assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 25 Dec 2022 for (n+1 year)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

Abstract

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting.(Jiang, M., Jia, L., Chen, Z. and Chen, W., 2020. The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm. Annals of Operations Research, pp.1-33.) We evaluate Forafric Global PLC Warrants prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Multiple Regression1,2,3,4 and conclude that the AFRIW stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

Key Points

  1. Is it better to buy and sell or hold?
  2. Is it better to buy and sell or hold?
  3. Which neural network is best for prediction?

AFRIW Target Price Prediction Modeling Methodology

We consider Forafric Global PLC Warrants Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of AFRIW 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+1 year) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of AFRIW 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?

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

Sample Set: Neural Network
Stock/Index: AFRIW Forafric Global PLC Warrants
Time series to forecast n: 25 Dec 2022 for (n+1 year)

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

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 Forafric Global PLC Warrants

  1. Credit risk analysis is a multifactor and holistic analysis; whether a specific factor is relevant, and its weight compared to other factors, will depend on the type of product, characteristics of the financial instruments and the borrower as well as the geographical region. An entity shall consider reasonable and supportable information that is available without undue cost or effort and that is relevant for the particular financial instrument being assessed. However, some factors or indicators may not be identifiable on an individual financial instrument level. In such a case, the factors or indicators should be assessed for appropriate portfolios, groups of portfolios or portions of a portfolio of financial instruments to determine whether the requirement in paragraph 5.5.3 for the recognition of lifetime expected credit losses has been met.
  2. When designating a risk component as a hedged item, the hedge accounting requirements apply to that risk component in the same way as they apply to other hedged items that are not risk components. For example, the qualifying criteria apply, including that the hedging relationship must meet the hedge effectiveness requirements, and any hedge ineffectiveness must be measured and recognised.
  3. 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.
  4. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34

*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

Forafric Global PLC Warrants assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Multiple Regression1,2,3,4 and conclude that the AFRIW stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

AFRIW Forafric Global PLC Warrants Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCBaa2
Balance SheetCCaa2
Leverage RatiosCaa2B2
Cash FlowB2Ba3
Rates of Return and ProfitabilityBaa2B1

*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: 76 out of 100 with 520 signals.

References

  1. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
  2. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
  3. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
  4. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
  5. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
  6. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
  7. Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
Frequently Asked QuestionsQ: What is the prediction methodology for AFRIW stock?
A: AFRIW stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Multiple Regression
Q: Is AFRIW stock a buy or sell?
A: The dominant strategy among neural network is to Hold AFRIW Stock.
Q: Is Forafric Global PLC Warrants stock a good investment?
A: The consensus rating for Forafric Global PLC Warrants is Hold and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of AFRIW stock?
A: The consensus rating for AFRIW is Hold.
Q: What is the prediction period for AFRIW stock?
A: The prediction period for AFRIW is (n+1 year)

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