TUL TULLA RESOURCES PLC (Forecast)

Outlook: TULLA RESOURCES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 14 Jan 2023 for (n+16 weeks)
Methodology : Transfer Learning (ML)

Abstract

TULLA RESOURCES PLC prediction model is evaluated with Transfer Learning (ML) and Factor^1,2,3,4 and it is concluded that the TUL 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. Is it better to buy and sell or hold?
2. Probability Distribution
3. Trust metric by Neural Network

TUL Target Price Prediction Modeling Methodology

We consider TULLA RESOURCES PLC Decision Process with Transfer Learning (ML) where A is the set of discrete actions of TUL 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(Factor)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of TUL 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?

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

Sample Set: Neural Network
Stock/Index: TUL TULLA RESOURCES PLC
Time series to forecast n: 14 Jan 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 TULLA RESOURCES PLC

1. An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.
2. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.
3. Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
4. The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.

*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

TULLA RESOURCES PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. TULLA RESOURCES PLC prediction model is evaluated with Transfer Learning (ML) and Factor^1,2,3,4 and it is concluded that the TUL 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

TUL TULLA RESOURCES PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Ba3
Balance Sheet	C	C
Leverage Ratios	C	C
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	Baa2	B2

*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: 86 out of 100 with 641 signals.

References

1. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
2. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
3. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
4. Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
5. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
6. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
7. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.

Frequently Asked Questions

Q: What is the prediction methodology for TUL stock?
A: TUL stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Factor
Q: Is TUL stock a buy or sell?
A: The dominant strategy among neural network is to Buy TUL Stock.
Q: Is TULLA RESOURCES PLC stock a good investment?
A: The consensus rating for TULLA RESOURCES PLC is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of TUL stock?
A: The consensus rating for TUL is Buy.
Q: What is the prediction period for TUL stock?
A: The prediction period for TUL is (n+16 weeks)

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