With the up-gradation of technology and exploration of new machine learning models, the stock market data analysis has gained attention as these models provide a platform for businessman and traders to choose more profitable stocks. As these data are in large volumes and highly complex so a need of more efficient machine learning model for daily predictions is always looked upon. We evaluate BT Group prediction models with Modular Neural Network (CNN Layer) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the BT-A stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BT-A stock.

Keywords: BT-A, BT Group, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Technical Analysis with Algorithmic Trading
2. What is statistical models in machine learning?
3. What is a prediction confidence? ## BT-A Target Price Prediction Modeling Methodology

One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We consider BT Group Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of BT-A 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 (CNN Layer)) X S(n):→ (n+6 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of BT-A 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?

## BT-A Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: BT-A BT Group
Time series to forecast n: 20 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BT-A 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

BT Group assigned short-term Ba3 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the BT-A stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold BT-A stock.

### Financial State Forecast for BT-A Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3B2
Operational Risk 6370
Market Risk3450
Technical Analysis7039
Fundamental Analysis8457
Risk Unsystematic8360

### Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 508 signals.

## References

1. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
2. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
3. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
4. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
5. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
6. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
7. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
Frequently Asked QuestionsQ: What is the prediction methodology for BT-A stock?
A: BT-A stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Wilcoxon Rank-Sum Test
Q: Is BT-A stock a buy or sell?
A: The dominant strategy among neural network is to Hold BT-A Stock.
Q: Is BT Group stock a good investment?
A: The consensus rating for BT Group is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of BT-A stock?
A: The consensus rating for BT-A is Hold.
Q: What is the prediction period for BT-A stock?
A: The prediction period for BT-A is (n+6 month)

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