Outlook: Bitfarms Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 06 Feb 2023 for (n+4 weeks)
Methodology : Multi-Instance Learning (ML)

## Abstract

Bitfarms Ltd. prediction model is evaluated with Multi-Instance Learning (ML) and Paired T-Test1,2,3,4 and it is concluded that the BITF: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: Hold

## Key Points

2. How do predictive algorithms actually work?
3. What are the most successful trading algorithms?

## BITF:TSX Target Price Prediction Modeling Methodology

We consider Bitfarms Ltd. Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of BITF: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(Paired T-Test)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(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of BITF: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?

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

Sample Set: Neural Network
Stock/Index: BITF:TSX Bitfarms Ltd.
Time series to forecast n: 06 Feb 2023 for (n+4 weeks)

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

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 Bitfarms Ltd.

1. That the transferee is unlikely to sell the transferred asset does not, of itself, mean that the transferor has retained control of the transferred asset. However, if a put option or guarantee constrains the transferee from selling the transferred asset, then the transferor has retained control of the transferred asset. For example, if a put option or guarantee is sufficiently valuable it constrains the transferee from selling the transferred asset because the transferee would, in practice, not sell the transferred asset to a third party without attaching a similar option or other restrictive conditions. Instead, the transferee would hold the transferred asset so as to obtain payments under the guarantee or put option. Under these circumstances the transferor has retained control of the transferred asset.
2. 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.
3. For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
4. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.

*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

Bitfarms Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating. Bitfarms Ltd. prediction model is evaluated with Multi-Instance Learning (ML) and Paired T-Test1,2,3,4 and it is concluded that the BITF: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: Hold

### BITF:TSX Bitfarms Ltd. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Ba3
Balance SheetBa1Baa2
Leverage RatiosBaa2Caa2
Cash FlowBaa2Ba1
Rates of Return and ProfitabilityBa3B1

*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: 90 out of 100 with 566 signals.

## References

1. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
2. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
3. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
4. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
5. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
6. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
7. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
Frequently Asked QuestionsQ: What is the prediction methodology for BITF:TSX stock?
A: BITF:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Paired T-Test
Q: Is BITF:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold BITF:TSX Stock.
Q: Is Bitfarms Ltd. stock a good investment?
A: The consensus rating for Bitfarms Ltd. is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of BITF:TSX stock?
A: The consensus rating for BITF:TSX is Hold.
Q: What is the prediction period for BITF:TSX stock?
A: The prediction period for BITF:TSX is (n+4 weeks)