**Outlook:**Enerplus Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 26 Mar 2023**for (n+4 weeks)

**Methodology :**Modular Neural Network (DNN Layer)

## Abstract

Enerplus Corporation prediction model is evaluated with Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing^{1,2,3,4}and it is concluded that the ERF:TSX stock is predictable in the short/long term.

**According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- Trust metric by Neural Network
- Stock Forecast Based On a Predictive Algorithm
- Can we predict stock market using machine learning?

## ERF:TSX Target Price Prediction Modeling Methodology

We consider Enerplus Corporation Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of ERF: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(Statistical Hypothesis Testing)

^{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(Modular Neural Network (DNN Layer)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of ERF:TSX 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?

## ERF:TSX Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**ERF:TSX Enerplus Corporation

**Time series to forecast n: 26 Mar 2023**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) 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 Enerplus Corporation

- An entity shall apply this Standard retrospectively, in accordance with IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors, except as specified in paragraphs 7.2.4–7.2.26 and 7.2.28. This Standard shall not be applied to items that have already been derecognised at the date of initial application.
- An entity shall assess whether contractual cash flows are solely payments of principal and interest on the principal amount outstanding for the currency in which the financial asset is denominated.
- A contractually specified inflation risk component of the cash flows of a recognised inflation-linked bond (assuming that there is no requirement to account for an embedded derivative separately) is separately identifiable and reliably measurable, as long as other cash flows of the instrument are not affected by the inflation risk component.
- To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.

*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

Enerplus Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating. Enerplus Corporation prediction model is evaluated with Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing^{1,2,3,4} and it is concluded that the ERF:TSX stock is predictable in the short/long term. ** According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell**

### ERF:TSX Enerplus Corporation Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | Ba3 | Baa2 |

Balance Sheet | Baa2 | B3 |

Leverage Ratios | C | B1 |

Cash Flow | B2 | Ba1 |

Rates of Return and Profitability | B2 | B2 |

*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

- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
- Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]

## Frequently Asked Questions

Q: What is the prediction methodology for ERF:TSX stock?A: ERF:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Statistical Hypothesis Testing

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

A: The dominant strategy among neural network is to Sell ERF:TSX Stock.

Q: Is Enerplus Corporation stock a good investment?

A: The consensus rating for Enerplus Corporation is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

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

A: The consensus rating for ERF:TSX is Sell.

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

A: The prediction period for ERF:TSX is (n+4 weeks)