Outlook: HURRICANE ENERGY PLC assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 02 Jan 2023 for (n+6 month)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

## Abstract

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price.(Kim, S., Ku, S., Chang, W. and Song, J.W., 2020. Predicting the direction of US stock prices using effective transfer entropy and machine learning techniques. IEEE Access, 8, pp.111660-111682.) We evaluate HURRICANE ENERGY PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the LON:HUR stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

## Key Points

1. Trust metric by Neural Network
2. How can neural networks improve predictions?
3. Is now good time to invest?

## LON:HUR Target Price Prediction Modeling Methodology

We consider HURRICANE ENERGY PLC Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of LON:HUR 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(Stepwise Regression)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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of LON:HUR 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:HUR Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:HUR HURRICANE ENERGY PLC
Time series to forecast n: 02 Jan 2023 for (n+6 month)

According to price forecasts for (n+6 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 HURRICANE ENERGY PLC

1. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
2. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
3. Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).
4. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.

*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

HURRICANE ENERGY PLC assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the LON:HUR stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

### LON:HUR HURRICANE ENERGY PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa2B1
Balance SheetBaa2B3
Leverage RatiosB3Ba2
Cash FlowCaa2C
Rates of Return and ProfitabilityB2Baa2

*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: 88 out of 100 with 810 signals.

## References

1. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
2. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
3. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
4. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
5. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
6. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
7. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:HUR stock?
A: LON:HUR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Stepwise Regression
Q: Is LON:HUR stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:HUR Stock.
Q: Is HURRICANE ENERGY PLC stock a good investment?
A: The consensus rating for HURRICANE ENERGY PLC is Sell and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:HUR stock?
A: The consensus rating for LON:HUR is Sell.
Q: What is the prediction period for LON:HUR stock?
A: The prediction period for LON:HUR is (n+6 month)