Outlook: Royal Bank of Canada assigned short-term B2 & long-term Caa1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 07 Dec 2022 for (n+8 weeks)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

## Abstract

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine-learning models in a stock market. (Lee, T.K., Cho, J.H., Kwon, D.S. and Sohn, S.Y., 2019. Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Systems with Applications, 117, pp.228-242.) We evaluate Royal Bank of Canada prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Lasso Regression1,2,3,4 and conclude 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 to Hold RY:TSX stock.

## Key Points

1. Nash Equilibria
2. What statistical methods are used to analyze data?
3. How can neural networks improve predictions?

## RY:TSX Target Price Prediction Modeling Methodology

We consider Royal Bank of Canada Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) 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(Lasso 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

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: 07 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold RY:TSX stock.

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 (Yellow to Green): *Technical Analysis%

## Adjusted IFRS* Prediction Methods for Royal Bank of Canada

1. Changes in market conditions that give rise to market risk include changes in a benchmark interest rate, the price of another entity's financial instrument, a commodity price, a foreign exchange rate or an index of prices or rates.
2. For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
3. The methods used to determine whether credit risk has increased significantly on a financial instrument since initial recognition should consider the characteristics of the financial instrument (or group of financial instruments) and the default patterns in the past for comparable financial instruments. Despite the requirement in paragraph 5.5.9, for financial instruments for which default patterns are not concentrated at a specific point during the expected life of the financial instrument, changes in the risk of a default occurring over the next 12 months may be a reasonable approximation of the changes in the lifetime risk of a default occurring. In such cases, an entity may use changes in the risk of a default occurring over the next 12 months to determine whether credit risk has increased significantly since initial recognition, unless circumstances indicate that a lifetime assessment is necessary
4. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.

*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

Royal Bank of Canada assigned short-term B2 & long-term Caa1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Lasso Regression1,2,3,4 and conclude 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 to Hold RY:TSX stock.

### Financial State Forecast for RY:TSX Royal Bank of Canada Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Caa1
Operational Risk 6933
Market Risk5753
Technical Analysis3238
Fundamental Analysis5630
Risk Unsystematic6244

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 709 signals.

## References

1. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
2. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
3. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
4. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
5. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
6. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
7. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
Frequently Asked QuestionsQ: What is the prediction methodology for RY:TSX stock?
A: RY:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Lasso 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 assigned short-term B2 & long-term Caa1 forecasted stock 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)