Outlook: White Mountains Insurance Group Ltd. Common Stock assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 24 Dec 2022 for (n+8 weeks)
Methodology : Modular Neural Network (DNN Layer)

## Abstract

Finance is one of the pioneering industries that started using Machine Learning (ML), a subset of Artificial Intelligence (AI) in the early 80s for market prediction. Since then, major firms and hedge funds have adopted machine learning for stock prediction, portfolio optimization, credit lending, stock betting, etc. In this paper, we survey all the different approaches of machine learning that can be incorporated in applied finance.(Chen, K., Zhou, Y. and Dai, F., 2015, October. A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data) (pp. 2823-2824). IEEE.) We evaluate White Mountains Insurance Group Ltd. Common Stock prediction models with Modular Neural Network (DNN Layer) and Pearson Correlation1,2,3,4 and conclude that the WTM stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

## Key Points

1. Understanding Buy, Sell, and Hold Ratings
2. How do you know when a stock will go up or down?
3. Is Target price a good indicator?

## WTM Target Price Prediction Modeling Methodology

We consider White Mountains Insurance Group Ltd. Common Stock Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of WTM 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(Modular Neural Network (DNN Layer)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of WTM 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?

## WTM Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: WTM White Mountains Insurance Group Ltd. Common Stock
Time series to forecast n: 24 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) 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 White Mountains Insurance Group Ltd. Common Stock

1. If subsequently an entity reasonably expects that the alternative benchmark rate will not be separately identifiable within 24 months from the date the entity designated it as a non-contractually specified risk component for the first time, the entity shall cease applying the requirement in paragraph 6.9.11 to that alternative benchmark rate and discontinue hedge accounting prospectively from the date of that reassessment for all hedging relationships in which the alternative benchmark rate was designated as a noncontractually specified risk component.
2. An entity that first applies these amendments after it first applies this Standard shall apply paragraphs 7.2.32–7.2.34. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
3. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.
4. An entity that first applies these amendments at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.31–7.2.34.

*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

White Mountains Insurance Group Ltd. Common Stock assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Pearson Correlation1,2,3,4 and conclude that the WTM stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

### WTM White Mountains Insurance Group Ltd. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB2B2
Balance SheetCaa2Ba3
Leverage RatiosB1C
Cash FlowCB1
Rates of Return and ProfitabilityCaa2Ba3

*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: 91 out of 100 with 771 signals.

## References

1. Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
2. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
3. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
4. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
5. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
6. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
7. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
Frequently Asked QuestionsQ: What is the prediction methodology for WTM stock?
A: WTM stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Pearson Correlation
Q: Is WTM stock a buy or sell?
A: The dominant strategy among neural network is to Buy WTM Stock.
Q: Is White Mountains Insurance Group Ltd. Common Stock stock a good investment?
A: The consensus rating for White Mountains Insurance Group Ltd. Common Stock is Buy and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of WTM stock?
A: The consensus rating for WTM is Buy.
Q: What is the prediction period for WTM stock?
A: The prediction period for WTM is (n+8 weeks)

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