Outlook: B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 09 May 2023 for (n+3 month)
Methodology : Active Learning (ML)

## Abstract

B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 prediction model is evaluated with Active Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the RILYL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

## Key Points

1. Dominated Move
2. How accurate is machine learning in stock market?
3. Understanding Buy, Sell, and Hold Ratings

## RILYL Target Price Prediction Modeling Methodology

We consider B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 Decision Process with Active Learning (ML) where A is the set of discrete actions of RILYL 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(Multiple 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(Active Learning (ML)) X S(n):→ (n+3 month) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of RILYL 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?

## RILYL Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: RILYL B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001
Time series to forecast n: 09 May 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

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 B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001

1. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
2. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
3. IFRS 7 defines credit risk as 'the risk that one party to a financial instrument will cause a financial loss for the other party by failing to discharge an obligation'. The requirement in paragraph 5.7.7(a) relates to the risk that the issuer will fail to perform on that particular liability. It does not necessarily relate to the creditworthiness of the issuer. For example, if an entity issues a collateralised liability and a non-collateralised liability that are otherwise identical, the credit risk of those two liabilities will be different, even though they are issued by the same entity. The credit risk on the collateralised liability will be less than the credit risk of the non-collateralised liability. The credit risk for a collateralised liability may be close to zero.
4. When assessing a modified time value of money element, an entity must consider factors that could affect future contractual cash flows. For example, if an entity is assessing a bond with a five-year term and the variable interest rate is reset every six months to a five-year rate, the entity cannot conclude that the contractual cash flows are solely payments of principal and interest on the principal amount outstanding simply because the interest rate curve at the time of the assessment is such that the difference between a five-year interest rate and a six-month interest rate is not significant. Instead, the entity must also consider whether the relationship between the five-year interest rate and the six-month interest rate could change over the life of the instrument such that the contractual (undiscounted) cash flows over the life of the instrument could be significantly different from the (undiscounted) benchmark cash flows. However, an entity must consider only reasonably possible scenarios instead of every possible scenario. If an entity concludes that the contractual (undiscounted) cash flows could be significantly different from the (undiscounted) benchmark cash flows, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and therefore cannot be measured at amortised cost or fair value through other comprehensive income.

*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

B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 is assigned short-term Ba1 & long-term Ba1 estimated rating. B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 prediction model is evaluated with Active Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the RILYL stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

### RILYL B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2B1
Balance SheetBa3Baa2
Leverage RatiosBa2C
Cash FlowCaa2C
Rates of Return and ProfitabilityBa3Ba3

*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: 83 out of 100 with 726 signals. ## References

1. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
2. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
3. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
4. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
5. 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.
6. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
7. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
Frequently Asked QuestionsQ: What is the prediction methodology for RILYL stock?
A: RILYL stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is RILYL stock a buy or sell?
A: The dominant strategy among neural network is to Buy RILYL Stock.
Q: Is B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 stock a good investment?
A: The consensus rating for B. Riley Financial Inc. Depositary Shares each representing 1/1000th in a share of 7.375% Series B Cumulative Perpetual Preferred Stock par value \$0.0001 is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of RILYL stock?
A: The consensus rating for RILYL is Buy.
Q: What is the prediction period for RILYL stock?
A: The prediction period for RILYL is (n+3 month)