**Outlook:**Bear Creek Mining Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 14 Mar 2023**for (n+16 weeks)

**Methodology :**Statistical Inference (ML)

## Abstract

Bear Creek Mining Corporation prediction model is evaluated with Statistical Inference (ML) and Polynomial Regression^{1,2,3,4}and it is concluded that the BCM:TSXV stock is predictable in the short/long term.

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- Is it better to buy and sell or hold?
- Operational Risk
- Is Target price a good indicator?

## BCM:TSXV Target Price Prediction Modeling Methodology

We consider Bear Creek Mining Corporation Decision Process with Statistical Inference (ML) where A is the set of discrete actions of BCM: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(Polynomial 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(Statistical Inference (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of BCM: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?

## BCM:TSXV Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**BCM:TSXV Bear Creek Mining Corporation

**Time series to forecast n: 14 Mar 2023**for (n+16 weeks)

**According to price forecasts for (n+16 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 Bear Creek Mining Corporation

- When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
- For example, when the critical terms (such as the nominal amount, maturity and underlying) of the hedging instrument and the hedged item match or are closely aligned, it might be possible for an entity to conclude on the basis of a qualitative assessment of those critical terms that the hedging instrument and the hedged item have values that will generally move in the opposite direction because of the same risk and hence that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6).
- An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.

*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

Bear Creek Mining Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating. Bear Creek Mining Corporation prediction model is evaluated with Statistical Inference (ML) and Polynomial Regression^{1,2,3,4} and it is concluded that the BCM:TSXV stock is predictable in the short/long term. ** According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell**

### BCM:TSXV Bear Creek Mining Corporation Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | Ba1 | B3 |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Baa2 | B1 |

Cash Flow | B2 | Baa2 |

Rates of Return and Profitability | Ba3 | B3 |

*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

- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can neural networks predict stock market?(ATVI Stock Forecast). AC Investment Research Journal, 101(3).
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]

## Frequently Asked Questions

Q: What is the prediction methodology for BCM:TSXV stock?A: BCM:TSXV stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Polynomial Regression

Q: Is BCM:TSXV stock a buy or sell?

A: The dominant strategy among neural network is to Sell BCM:TSXV Stock.

Q: Is Bear Creek Mining Corporation stock a good investment?

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

Q: What is the consensus rating of BCM:TSXV stock?

A: The consensus rating for BCM:TSXV is Sell.

Q: What is the prediction period for BCM:TSXV stock?

A: The prediction period for BCM:TSXV is (n+16 weeks)