How do you determine buy or sell? (RCI.B Stock Forecast)

The categorization of high dimensional data present a fascinating challenge to machine learning models as frequent number of highly correlated dimensions or attributes can affect the accuracy of classification model. In this paper, the problem of high dimensionality of stock exchange is investigated to predict the market trends by applying the principal component analysis (PCA) with linear regression. PCA can help to improve the predictive performance of machine learning methods while reducing the redundancy among the data. We evaluate Rogers Communications Inc. prediction models with Transfer Learning (ML) and Sign Test^1,2,3,4 and conclude that the RCI.B stock is predictable in the short/long term. According to price forecasts for (n+3 month) 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. Operational Risk
2. Is Target price a good indicator?
3. What are the most successful trading algorithms?

RCI.B Target Price Prediction Modeling Methodology

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. We consider Rogers Communications Inc. Stock Decision Process with Sign Test 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(Sign Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+3 month) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

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+3 month)

Sample Set: Neural Network
Stock/Index: RCI.B Rogers Communications Inc.
Time series to forecast n: 21 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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 Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Sign Test^1,2,3,4 and conclude that the RCI.B stock is predictable in the short/long term. According to price forecasts for (n+3 month) 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*	Ba2	Ba3
Operational Risk	80	43
Market Risk	39	56
Technical Analysis	60	75
Fundamental Analysis	83	79
Risk Unsystematic	78	61

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 697 signals.

References

1. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
2. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
3. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
4. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
5. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
6. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
7. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014

Frequently Asked Questions

Q: What is the prediction methodology for RCI.B stock?
A: RCI.B stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Sign Test
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 Ba2 & long-term Ba3 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+3 month)

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