**Outlook:**Questerre Energy Corporation is assigned short-term Caa2 & long-term B1 estimated rating.

**Dominant Strategy :**Speculative Trend

**Time series to forecast n: 22 Jun 2023**for 4 Weeks

**Methodology :**Supervised Machine Learning (ML)

## Summary

Questerre Energy Corporation prediction model is evaluated with Supervised Machine Learning (ML) and Linear Regression^{1,2,3,4}and it is concluded that the QEC:TSX stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.

**According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Speculative Trend**

## Key Points

- What is the use of Markov decision process?
- What is the use of Markov decision process?
- What are the most successful trading algorithms?

## QEC:TSX Target Price Prediction Modeling Methodology

We consider Questerre Energy Corporation Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of QEC:TSX 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(Linear Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ 4 Weeks $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of QEC:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Supervised Machine Learning (ML)

Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.### Linear Regression

In statistics, linear regression is a method for estimating the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Linear regression assumes that the relationship between the dependent variable and the independent variables is linear. This means that the dependent variable can be represented as a straight line function of the independent variables.

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?

## QEC:TSX Stock Forecast (Buy or Sell) for 4 Weeks

**Sample Set:**Neural Network

**Stock/Index:**QEC:TSX Questerre Energy Corporation

**Time series to forecast n: 22 Jun 2023**for 4 Weeks

**According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Speculative Trend**

**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 Questerre Energy Corporation

- When an entity, consistent with its hedge documentation, frequently resets (ie discontinues and restarts) a hedging relationship because both the hedging instrument and the hedged item frequently change (ie the entity uses a dynamic process in which both the hedged items and the hedging instruments used to manage that exposure do not remain the same for long), the entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component is separately identifiable—only when it initially designates a hedged item in that hedging relationship. A hedged item that has been assessed at the time of its initial designation in the hedging relationship, whether it was at the time of the hedge inception or subsequently, is not reassessed at any subsequent redesignation in the same hedging relationship.
- For floating-rate financial assets and floating-rate financial liabilities, periodic re-estimation of cash flows to reflect the movements in the market rates of interest alters the effective interest rate. If a floating-rate financial asset or a floating-rate financial liability is recognised initially at an amount equal to the principal receivable or payable on maturity, re-estimating the future interest payments normally has no significant effect on the carrying amount of the asset or the liability.
- When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
- However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.

*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

Questerre Energy Corporation is assigned short-term Caa2 & long-term B1 estimated rating. Questerre Energy Corporation prediction model is evaluated with Supervised Machine Learning (ML) and Linear Regression^{1,2,3,4} and it is concluded that the QEC:TSX stock is predictable in the short/long term. ** According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Speculative Trend**

### QEC:TSX Questerre Energy Corporation Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Caa2 | B1 |

Income Statement | C | Baa2 |

Balance Sheet | C | Caa2 |

Leverage Ratios | B3 | C |

Cash Flow | Caa2 | Ba2 |

Rates of Return and Profitability | Ba3 | 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

## References

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- Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.

## Frequently Asked Questions

Q: What is the prediction methodology for QEC:TSX stock?A: QEC:TSX stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Linear Regression

Q: Is QEC:TSX stock a buy or sell?

A: The dominant strategy among neural network is to Speculative Trend QEC:TSX Stock.

Q: Is Questerre Energy Corporation stock a good investment?

A: The consensus rating for Questerre Energy Corporation is Speculative Trend and is assigned short-term Caa2 & long-term B1 estimated rating.

Q: What is the consensus rating of QEC:TSX stock?

A: The consensus rating for QEC:TSX is Speculative Trend.

Q: What is the prediction period for QEC:TSX stock?

A: The prediction period for QEC:TSX is 4 Weeks

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