Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. We evaluate Rogers Communications Inc. prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the RCI.B stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy 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 the best way to predict stock prices?
2. Can statistics predict the future?
3. Which neural network is best for prediction?

## RCI.B Target Price Prediction Modeling Methodology

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We consider Rogers Communications Inc. Stock Decision Process with Wilcoxon Rank-Sum 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(Wilcoxon Rank-Sum Test)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+1 year) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

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+1 year)

Sample Set: Neural Network
Stock/Index: RCI.B Rogers Communications Inc.
Time series to forecast n: 22 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy 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 Caa2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the RCI.B stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy RCI.B stock.

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

Rating Short-Term Long-Term Senior
Outlook*Caa2Ba2
Operational Risk 5778
Market Risk3682
Technical Analysis4753
Fundamental Analysis4643
Risk Unsystematic3480

### Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 662 signals.

## References

1. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
2. Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
3. Robins J, Rotnitzky A. 1995. Semiparametric efficiency in multivariate regression models with missing data. J. Am. Stat. Assoc. 90:122–29
4. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
5. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
6. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
7. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
Frequently Asked QuestionsQ: What is the prediction methodology for RCI.B stock?
A: RCI.B stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Wilcoxon Rank-Sum Test
Q: Is RCI.B stock a buy or sell?
A: The dominant strategy among neural network is to Buy RCI.B Stock.
Q: Is Rogers Communications Inc. stock a good investment?
A: The consensus rating for Rogers Communications Inc. is Buy and assigned short-term Caa2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of RCI.B stock?
A: The consensus rating for RCI.B is Buy.
Q: What is the prediction period for RCI.B stock?
A: The prediction period for RCI.B is (n+1 year)