MGU MAGNUM MINING AND EXPLORATION LIMITED

Outlook: MAGNUM MINING AND EXPLORATION LIMITED assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 19 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (CNN Layer)

Abstract

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model.(Pahwa, K. and Agarwal, N., 2019, February. Stock market analysis using supervised machine learning. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 197-200). IEEE.) We evaluate MAGNUM MINING AND EXPLORATION LIMITED prediction models with Modular Neural Network (CNN Layer) and Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the MGU stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

Key Points

1. What statistical methods are used to analyze data?
2. Dominated Move
3. What are the most successful trading algorithms?

MGU Target Price Prediction Modeling Methodology

We consider MAGNUM MINING AND EXPLORATION LIMITED Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of MGU 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(Wilcoxon Sign-Rank Test)^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) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of MGU 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?

MGU Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: MGU MAGNUM MINING AND EXPLORATION LIMITED
Time series to forecast n: 19 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

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 MAGNUM MINING AND EXPLORATION LIMITED

1. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
2. A contractual cash flow characteristic does not affect the classification of the financial asset if it could have only a de minimis effect on the contractual cash flows of the financial asset. To make this determination, an entity must consider the possible effect of the contractual cash flow characteristic in each reporting period and cumulatively over the life of the financial instrument. In addition, if a contractual cash flow characteristic could have an effect on the contractual cash flows that is more than de minimis (either in a single reporting period or cumulatively) but that cash flow characteristic is not genuine, it does not affect the classification of a financial asset. A cash flow characteristic is not genuine if it affects the instrument's contractual cash flows only on the occurrence of an event that is extremely rare, highly abnormal and very unlikely to occur.
3. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.
4. Paragraph 4.1.1(b) requires an entity to classify a financial asset on the basis of its contractual cash flow characteristics if the financial asset is held within a business model whose objective is to hold assets to collect contractual cash flows or within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets, unless paragraph 4.1.5 applies. To do so, the condition in paragraphs 4.1.2(b) and 4.1.2A(b) requires an entity to determine whether the asset's contractual cash flows are solely payments of principal and interest on the principal amount outstanding.

*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

MAGNUM MINING AND EXPLORATION LIMITED assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Wilcoxon Sign-Rank Test^1,2,3,4 and conclude that the MGU stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

MGU MAGNUM MINING AND EXPLORATION LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	C
Balance Sheet	Baa2	Ba2
Leverage Ratios	B1	C
Cash Flow	Baa2	B1
Rates of Return and Profitability	Baa2	Baa2

*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: 85 out of 100 with 769 signals.

References

1. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
2. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
3. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
4. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
5. Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
6. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
7. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier

Frequently Asked Questions

Q: What is the prediction methodology for MGU stock?
A: MGU stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Wilcoxon Sign-Rank Test
Q: Is MGU stock a buy or sell?
A: The dominant strategy among neural network is to Sell MGU Stock.
Q: Is MAGNUM MINING AND EXPLORATION LIMITED stock a good investment?
A: The consensus rating for MAGNUM MINING AND EXPLORATION LIMITED is Sell and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of MGU stock?
A: The consensus rating for MGU is Sell.
Q: What is the prediction period for MGU stock?
A: The prediction period for MGU is (n+3 month)

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