SOU:TSXV Southern Energy Corp.

Outlook: Southern Energy Corp. assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 23 Dec 2022 for (n+6 month)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

Financial markets are fascinating if you can predict them. Also, the traders acting on financial markets produce a vast amount of information to analyse the consequences of investing according to the current market trends. Stock Market prediction is the technique to determine whether stock value will go up or down as it plays an active role in the financial gain of nation's economic status.(Yuan, X., Yuan, J., Jiang, T. and Ain, Q.U., 2020. Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market. IEEE Access, 8, pp.22672-22685.) We evaluate Southern Energy Corp. prediction models with Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression^1,2,3,4 and conclude that the SOU:TSXV stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Key Points

1. What is neural prediction?
2. What is Markov decision process in reinforcement learning?
3. What is prediction model?

SOU:TSXV Target Price Prediction Modeling Methodology

We consider Southern Energy Corp. Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of SOU:TSXV 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of SOU:TSXV 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?

SOU:TSXV Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: SOU:TSXV Southern Energy Corp.
Time series to forecast n: 23 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) 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 Southern Energy Corp.

1. An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.
2. When assessing a modified time value of money element, an entity must consider factors that could affect future contractual cash flows. For example, if an entity is assessing a bond with a five-year term and the variable interest rate is reset every six months to a five-year rate, the entity cannot conclude that the contractual cash flows are solely payments of principal and interest on the principal amount outstanding simply because the interest rate curve at the time of the assessment is such that the difference between a five-year interest rate and a six-month interest rate is not significant. Instead, the entity must also consider whether the relationship between the five-year interest rate and the six-month interest rate could change over the life of the instrument such that the contractual (undiscounted) cash flows over the life of the instrument could be significantly different from the (undiscounted) benchmark cash flows. However, an entity must consider only reasonably possible scenarios instead of every possible scenario. If an entity concludes that the contractual (undiscounted) cash flows could be significantly different from the (undiscounted) benchmark cash flows, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and therefore cannot be measured at amortised cost or fair value through other comprehensive income.
3. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
4. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.

*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

Southern Energy Corp. assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Multiple Regression^1,2,3,4 and conclude that the SOU:TSXV stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

SOU:TSXV Southern Energy Corp. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B1	B2
Balance Sheet	B2	B2
Leverage Ratios	B2	Caa2
Cash Flow	Baa2	B3
Rates of Return and Profitability	Caa2	C

*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: 81 out of 100 with 746 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. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
3. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
4. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
5. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
6. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
7. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.

Frequently Asked Questions

Q: What is the prediction methodology for SOU:TSXV stock?
A: SOU:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression
Q: Is SOU:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Hold SOU:TSXV Stock.
Q: Is Southern Energy Corp. stock a good investment?
A: The consensus rating for Southern Energy Corp. is Hold and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of SOU:TSXV stock?
A: The consensus rating for SOU:TSXV is Hold.
Q: What is the prediction period for SOU:TSXV stock?
A: The prediction period for SOU:TSXV is (n+6 month)

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