Outlook: Royal Bank of Canada is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 15 Jan 2023 for (n+8 weeks)
Methodology : Deductive Inference (ML)

## Abstract

Royal Bank of Canada prediction model is evaluated with Deductive Inference (ML) and Multiple Regression1,2,3,4 and it is concluded that the RY:TSX stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

## Key Points

1. Can machine learning predict?
2. Technical Analysis with Algorithmic Trading
3. Can statistics predict the future?

## RY:TSX Target Price Prediction Modeling Methodology

We consider Royal Bank of Canada Decision Process with Deductive Inference (ML) where A is the set of discrete actions of RY: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(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(Deductive Inference (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of RY:TSX 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?

## RY:TSX Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: RY:TSX Royal Bank of Canada
Time series to forecast n: 15 Jan 2023 for (n+8 weeks)

According to price forecasts for (n+8 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%

1. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
2. When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
3. Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.
4. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.

*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

Royal Bank of Canada is assigned short-term Ba1 & long-term Ba1 estimated rating. Royal Bank of Canada prediction model is evaluated with Deductive Inference (ML) and Multiple Regression1,2,3,4 and it is concluded that the RY:TSX stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

### RY:TSX Royal Bank of Canada Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3Baa2
Balance SheetBaa2Caa2
Leverage RatiosCBaa2
Cash FlowCBaa2
Rates of Return and ProfitabilityBa3Baa2

*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 473 signals.

## References

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3. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
4. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
5. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
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7. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
Frequently Asked QuestionsQ: What is the prediction methodology for RY:TSX stock?
A: RY:TSX stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Multiple Regression
Q: Is RY:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold RY:TSX Stock.
Q: Is Royal Bank of Canada stock a good investment?
A: The consensus rating for Royal Bank of Canada is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of RY:TSX stock?
A: The consensus rating for RY:TSX is Hold.
Q: What is the prediction period for RY:TSX stock?
A: The prediction period for RY:TSX is (n+8 weeks)