Outlook: Rumble Inc. Warrant is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 13 Jan 2023 for (n+4 weeks)
Methodology : Modular Neural Network (Market Volatility Analysis)

## Abstract

Rumble Inc. Warrant prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Lasso Regression1,2,3,4 and it is concluded that the RUMBW 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

1. Investment Risk
2. Can machine learning predict?
3. Buy, Sell and Hold Signals

## RUMBW Target Price Prediction Modeling Methodology

We consider Rumble Inc. Warrant Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of RUMBW 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(Lasso 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(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+4 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of RUMBW 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?

## RUMBW Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: RUMBW Rumble Inc. Warrant
Time series to forecast n: 13 Jan 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 Rumble Inc. Warrant

1. Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
2. The characteristics of the hedged item, including how and when the hedged item affects profit or loss, also affect the period over which the forward element of a forward contract that hedges a time-period related hedged item is amortised, which is over the period to which the forward element relates. For example, if a forward contract hedges the exposure to variability in threemonth interest rates for a three-month period that starts in six months' time, the forward element is amortised during the period that spans months seven to nine.
3. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
4. 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).

*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

Rumble Inc. Warrant is assigned short-term Ba1 & long-term Ba1 estimated rating. Rumble Inc. Warrant prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Lasso Regression1,2,3,4 and it is concluded that the RUMBW 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

### RUMBW Rumble Inc. Warrant Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB3B3
Balance SheetBaa2Caa2
Leverage RatiosCaa2Baa2
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityCaa2Baa2

*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: 92 out of 100 with 633 signals.

## References

1. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
2. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
3. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
4. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
5. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
6. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
7. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
Frequently Asked QuestionsQ: What is the prediction methodology for RUMBW stock?
A: RUMBW stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Lasso Regression
Q: Is RUMBW stock a buy or sell?
A: The dominant strategy among neural network is to Sell RUMBW Stock.
Q: Is Rumble Inc. Warrant stock a good investment?
A: The consensus rating for Rumble Inc. Warrant is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of RUMBW stock?
A: The consensus rating for RUMBW is Sell.
Q: What is the prediction period for RUMBW stock?
A: The prediction period for RUMBW is (n+4 weeks)