Outlook: Brookfield Asset Management Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 18 Jun 2023 for 4 Weeks
Methodology : Statistical Inference (ML)

## Abstract

Brookfield Asset Management Inc. prediction model is evaluated with Statistical Inference (ML) and Pearson Correlation1,2,3,4 and it is concluded that the BAM.A:TSX stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold ## Key Points

1. Fundemental Analysis with Algorithmic Trading
2. Operational Risk
3. What are the most successful trading algorithms?

## BAM.A:TSX Target Price Prediction Modeling Methodology

We consider Brookfield Asset Management Inc. Decision Process with Statistical Inference (ML) where A is the set of discrete actions of BAM.A: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(Pearson Correlation)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(Statistical Inference (ML)) X S(n):→ 4 Weeks $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of BAM.A:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Statistical Inference (ML)

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

### Pearson Correlation

Pearson correlation, also known as Pearson's product-moment correlation, is a measure of the linear relationship between two variables. It is a statistical measure that assesses the strength and direction of a linear relationship between two variables. The sign of the correlation coefficient indicates the direction of the relationship, while the magnitude of the correlation coefficient indicates the strength of the relationship. A correlation coefficient of 0.9 indicates a strong positive correlation, while a correlation coefficient of 0.2 indicates a weak positive correlation.

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?

## BAM.A:TSX Stock Forecast (Buy or Sell) for 4 Weeks

Sample Set: Neural Network
Stock/Index: BAM.A:TSX Brookfield Asset Management Inc.
Time series to forecast n: 18 Jun 2023 for 4 Weeks

According to price forecasts for 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 Brookfield Asset Management Inc.

1. Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
2. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
4. Despite the requirement in paragraph 7.2.1, an entity that adopts the classification and measurement requirements of this Standard (which include the requirements related to amortised cost measurement for financial assets and impairment in Sections 5.4 and 5.5) shall provide the disclosures set out in paragraphs 42L–42O of IFRS 7 but need not restate prior periods. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. 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 in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application. However, if an entity restates prior periods, the restated financial statements must reflect all of the requirements in this Standard. If an entity's chosen approach to applying IFRS 9 results in more than one date of initial application for different requirements, this paragraph applies at each date of initial application (see paragraph 7.2.2). This would be the case, for example, if an entity elects to early apply only the requirements for the presentation of gains and losses on financial liabilities designated as at fair value through profit or loss in accordance with paragraph 7.1.2 before applying the other requirements in this Standard.

*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

Brookfield Asset Management Inc. is assigned short-term Ba1 & long-term Ba1 estimated rating. Brookfield Asset Management Inc. prediction model is evaluated with Statistical Inference (ML) and Pearson Correlation1,2,3,4 and it is concluded that the BAM.A:TSX stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Hold

### BAM.A:TSX Brookfield Asset Management Inc. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Caa2
Balance SheetCaa2C
Leverage RatiosBaa2B1
Cash FlowCCaa2
Rates of Return and ProfitabilityB3C

*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: 76 out of 100 with 560 signals.

## References

1. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., How do you decide buy or sell a stock?(SAIC Stock Forecast). AC Investment Research Journal, 101(3).
3. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
4. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
5. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
6. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
7. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
Frequently Asked QuestionsQ: What is the prediction methodology for BAM.A:TSX stock?
A: BAM.A:TSX stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Pearson Correlation
Q: Is BAM.A:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold BAM.A:TSX Stock.
Q: Is Brookfield Asset Management Inc. stock a good investment?
A: The consensus rating for Brookfield Asset Management Inc. is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of BAM.A:TSX stock?
A: The consensus rating for BAM.A:TSX is Hold.
Q: What is the prediction period for BAM.A:TSX stock?
A: The prediction period for BAM.A:TSX is 4 Weeks