Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate Rogers Communications Inc. prediction models with Reinforcement Machine Learning (ML) and Stepwise Regression1,2,3,4 and conclude that the RCI.B stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold RCI.B stock.

Keywords: RCI.B, Rogers Communications Inc., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. What is neural prediction?
2. What are main components of Markov decision process?
3. Which neural network is best for prediction?

## RCI.B Target Price Prediction Modeling Methodology

Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. We consider Rogers Communications Inc. Stock Decision Process with Stepwise Regression where A is the set of discrete actions of RCI.B 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(Stepwise 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(Reinforcement Machine Learning (ML)) X S(n):→ (n+4 weeks) $∑ i = 1 n r i$

n:Time series to forecast

p:Price signals of RCI.B stock

j:Nash equilibria

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?

## RCI.B Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: RCI.B Rogers Communications Inc.
Time series to forecast n: 07 Oct 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold RCI.B 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%

## Conclusions

Rogers Communications Inc. assigned short-term Baa2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Stepwise Regression1,2,3,4 and conclude that the RCI.B stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold RCI.B stock.

### Financial State Forecast for RCI.B Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Baa2Ba2
Operational Risk 3483
Market Risk8088
Technical Analysis7537
Fundamental Analysis9063
Risk Unsystematic8471

### Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 689 signals.

## References

1. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
2. 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
3. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
4. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
5. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
6. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
7. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
Frequently Asked QuestionsQ: What is the prediction methodology for RCI.B stock?
A: RCI.B stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Stepwise Regression
Q: Is RCI.B stock a buy or sell?
A: The dominant strategy among neural network is to Hold RCI.B Stock.
Q: Is Rogers Communications Inc. stock a good investment?
A: The consensus rating for Rogers Communications Inc. is Hold and assigned short-term Baa2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of RCI.B stock?
A: The consensus rating for RCI.B is Hold.
Q: What is the prediction period for RCI.B stock?
A: The prediction period for RCI.B is (n+4 weeks)

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