Outlook: Electra Battery Materials Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 20 Apr 2023 for (n+1 year)
Methodology : Supervised Machine Learning (ML)

## Abstract

Electra Battery Materials Corporation prediction model is evaluated with Supervised Machine Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the ELBM:TSXV stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

## Key Points

1. What are main components of Markov decision process?
2. How accurate is machine learning in stock market?
3. How do predictive algorithms actually work?

## ELBM:TSXV Target Price Prediction Modeling Methodology

We consider Electra Battery Materials Corporation Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of ELBM: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(Ridge 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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of ELBM: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?

## ELBM:TSXV Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: ELBM:TSXV Electra Battery Materials Corporation
Time series to forecast n: 20 Apr 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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 Electra Battery Materials Corporation

1. For some types of fair value hedges, the objective of the hedge is not primarily to offset the fair value change of the hedged item but instead to transform the cash flows of the hedged item. For example, an entity hedges the fair value interest rate risk of a fixed-rate debt instrument using an interest rate swap. The entity's hedge objective is to transform the fixed-interest cash flows into floating interest cash flows. This objective is reflected in the accounting for the hedging relationship by accruing the net interest accrual on the interest rate swap in profit or loss. In the case of a hedge of a net position (for example, a net position of a fixed-rate asset and a fixed-rate liability), this net interest accrual must be presented in a separate line item in the statement of profit or loss and other comprehensive income. This is to avoid the grossing up of a single instrument's net gains or losses into offsetting gross amounts and recognising them in different line items (for example, this avoids grossing up a net interest receipt on a single interest rate swap into gross interest revenue and gross interest expense).
2. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
3. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
4. 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.

*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

Electra Battery Materials Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating. Electra Battery Materials Corporation prediction model is evaluated with Supervised Machine Learning (ML) and Ridge Regression1,2,3,4 and it is concluded that the ELBM:TSXV stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

### ELBM:TSXV Electra Battery Materials Corporation Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB3B3
Balance SheetB2Baa2
Leverage RatiosBaa2B1
Cash FlowB3C
Rates of Return and ProfitabilityCaa2B3

*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: 73 out of 100 with 625 signals.

## References

1. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
2. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
3. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
4. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
5. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
6. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
7. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
Frequently Asked QuestionsQ: What is the prediction methodology for ELBM:TSXV stock?
A: ELBM:TSXV stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Ridge Regression
Q: Is ELBM:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Sell ELBM:TSXV Stock.
Q: Is Electra Battery Materials Corporation stock a good investment?
A: The consensus rating for Electra Battery Materials Corporation is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ELBM:TSXV stock?
A: The consensus rating for ELBM:TSXV is Sell.
Q: What is the prediction period for ELBM:TSXV stock?
A: The prediction period for ELBM:TSXV is (n+1 year)