Outlook: FORTESCUE METALS GROUP LTD is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 22 Feb 2023 for (n+16 weeks)
Methodology : Reinforcement Machine Learning (ML)

## Abstract

FORTESCUE METALS GROUP LTD prediction model is evaluated with Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the FMG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

## Key Points

1. What is Markov decision process in reinforcement learning?
2. How can neural networks improve predictions?
3. How do you decide buy or sell a stock?

## FMG Target Price Prediction Modeling Methodology

We consider FORTESCUE METALS GROUP LTD Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of FMG 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(Statistical Hypothesis Testing)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of FMG 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?

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

Sample Set: Neural Network
Stock/Index: FMG FORTESCUE METALS GROUP LTD
Time series to forecast n: 22 Feb 2023 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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%

## IFRS Reconciliation Adjustments for FORTESCUE METALS GROUP LTD

1. Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
2. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.
3. For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
4. 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.

*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

FORTESCUE METALS GROUP LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. FORTESCUE METALS GROUP LTD prediction model is evaluated with Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the FMG stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy

### FMG FORTESCUE METALS GROUP LTD Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2B3
Balance SheetCCaa2
Leverage RatiosBaa2Caa2
Cash FlowB3Baa2
Rates of Return and ProfitabilityB1Caa2

*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: 89 out of 100 with 673 signals.

## References

1. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
2. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
3. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
4. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
5. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
6. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
7. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
Frequently Asked QuestionsQ: What is the prediction methodology for FMG stock?
A: FMG stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Statistical Hypothesis Testing
Q: Is FMG stock a buy or sell?
A: The dominant strategy among neural network is to Buy FMG Stock.
Q: Is FORTESCUE METALS GROUP LTD stock a good investment?
A: The consensus rating for FORTESCUE METALS GROUP LTD is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of FMG stock?
A: The consensus rating for FMG is Buy.
Q: What is the prediction period for FMG stock?
A: The prediction period for FMG is (n+16 weeks)