LON:BZT BEZANT RESOURCES PLC

Outlook: BEZANT RESOURCES PLC assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 12 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (CNN Layer)

Abstract

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. (Vazirani, S., Sharma, A. and Sharma, P., 2020, October. Analysis of various machine learning algorithm and hybrid model for stock market prediction using python. In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (pp. 203-207). IEEE.) We evaluate BEZANT RESOURCES PLC prediction models with Modular Neural Network (CNN Layer) and Ridge Regression^1,2,3,4 and conclude that the LON:BZT stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Key Points

1. Can machine learning predict?
2. What is neural prediction?
3. How do predictive algorithms actually work?

LON:BZT Target Price Prediction Modeling Methodology

We consider BEZANT RESOURCES PLC Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of LON:BZT 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(Ridge Regression)^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(Modular Neural Network (CNN Layer)) X S(n):→ (n+3 month) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:BZT 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?

LON:BZT Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: LON:BZT BEZANT RESOURCES PLC
Time series to forecast n: 12 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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 BEZANT RESOURCES PLC

1. A net position is eligible for hedge accounting only if an entity hedges on a net basis for risk management purposes. Whether an entity hedges in this way is a matter of fact (not merely of assertion or documentation). Hence, an entity cannot apply hedge accounting on a net basis solely to achieve a particular accounting outcome if that would not reflect its risk management approach. Net position hedging must form part of an established risk management strategy. Normally this would be approved by key management personnel as defined in IAS 24.
2. For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
3. Compared to a business model whose objective is to hold financial assets to collect contractual cash flows, this business model will typically involve greater frequency and value of sales. This is because selling financial assets is integral to achieving the business model's objective instead of being only incidental to it. However, there is no threshold for the frequency or value of sales that must occur in this business model because both collecting contractual cash flows and selling financial assets are integral to achieving its objective.
4. Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.

*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

BEZANT RESOURCES PLC assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Ridge Regression^1,2,3,4 and conclude that the LON:BZT stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for LON:BZT BEZANT RESOURCES PLC Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	39	65
Market Risk	62	53
Technical Analysis	81	68
Fundamental Analysis	37	68
Risk Unsystematic	51	45

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 748 signals.

References

1. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
2. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
3. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
4. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
5. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
6. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
7. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503

Frequently Asked Questions

Q: What is the prediction methodology for LON:BZT stock?
A: LON:BZT stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Ridge Regression
Q: Is LON:BZT stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:BZT Stock.
Q: Is BEZANT RESOURCES PLC stock a good investment?
A: The consensus rating for BEZANT RESOURCES PLC is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of LON:BZT stock?
A: The consensus rating for LON:BZT is Buy.
Q: What is the prediction period for LON:BZT stock?
A: The prediction period for LON:BZT is (n+3 month)

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