Outlook: BRP Group Inc. (Insurance Company) Class A Common Stock assigned short-term Ba1 & long-term Baa2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 12 Dec 2022 for (n+16 weeks)

## Abstract

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators.(Cao, H., Lin, T., Li, Y. and Zhang, H., 2019. Stock price pattern prediction based on complex network and machine learning. Complexity, 2019.) We evaluate BRP Group Inc. (Insurance Company) Class A Common Stock prediction models with Multi-Task Learning (ML) and Chi-Square1,2,3,4 and conclude that the BRP stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

## Key Points

1. What is neural prediction?
2. How do you know when a stock will go up or down?
3. What is the use of Markov decision process?

## BRP Target Price Prediction Modeling Methodology

We consider BRP Group Inc. (Insurance Company) Class A Common Stock Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of BRP 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(Chi-Square)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-Task Learning (ML)) X S(n):→ (n+16 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of BRP 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?

## BRP Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: BRP BRP Group Inc. (Insurance Company) Class A Common Stock
Time series to forecast n: 12 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 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%

## Adjusted IFRS* Prediction Methods for BRP Group Inc. (Insurance Company) Class A Common Stock

1. Rebalancing refers to the adjustments made to the designated quantities of the hedged item or the hedging instrument of an already existing hedging relationship for the purpose of maintaining a hedge ratio that complies with the hedge effectiveness requirements. Changes to designated quantities of a hedged item or of a hedging instrument for a different purpose do not constitute rebalancing for the purpose of this Standard
2. In addition to those hedging relationships specified in paragraph 6.9.1, an entity shall apply the requirements in paragraphs 6.9.11 and 6.9.12 to new hedging relationships in which an alternative benchmark rate is designated as a non-contractually specified risk component (see paragraphs 6.3.7(a) and B6.3.8) when, because of interest rate benchmark reform, that risk component is not separately identifiable at the date it is designated.
3. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
4. Credit risk analysis is a multifactor and holistic analysis; whether a specific factor is relevant, and its weight compared to other factors, will depend on the type of product, characteristics of the financial instruments and the borrower as well as the geographical region. An entity shall consider reasonable and supportable information that is available without undue cost or effort and that is relevant for the particular financial instrument being assessed. However, some factors or indicators may not be identifiable on an individual financial instrument level. In such a case, the factors or indicators should be assessed for appropriate portfolios, groups of portfolios or portions of a portfolio of financial instruments to determine whether the requirement in paragraph 5.5.3 for the recognition of lifetime expected credit losses has been met.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

BRP Group Inc. (Insurance Company) Class A Common Stock assigned short-term Ba1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Chi-Square1,2,3,4 and conclude that the BRP stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell

### Financial State Forecast for BRP BRP Group Inc. (Insurance Company) Class A Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1Baa2
Operational Risk 7884
Market Risk8081
Technical Analysis7390
Fundamental Analysis5665
Risk Unsystematic6485

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 671 signals.

## References

1. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
2. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
3. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
4. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
5. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
6. F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
7. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
Frequently Asked QuestionsQ: What is the prediction methodology for BRP stock?
A: BRP stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Chi-Square
Q: Is BRP stock a buy or sell?
A: The dominant strategy among neural network is to Sell BRP Stock.
Q: Is BRP Group Inc. (Insurance Company) Class A Common Stock stock a good investment?
A: The consensus rating for BRP Group Inc. (Insurance Company) Class A Common Stock is Sell and assigned short-term Ba1 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of BRP stock?
A: The consensus rating for BRP is Sell.
Q: What is the prediction period for BRP stock?
A: The prediction period for BRP is (n+16 weeks)