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 Test ^{1,2,3,4} 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.**

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

*Keywords:*## Key Points

- Technical Analysis with Algorithmic Trading
- What is statistical models in machine learning?
- 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Wilcoxon Rank-Sum Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

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 Test ^{1,2,3,4} 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* | Ba3 | B2 |

Operational Risk | 63 | 70 |

Market Risk | 34 | 50 |

Technical Analysis | 70 | 39 |

Fundamental Analysis | 84 | 57 |

Risk Unsystematic | 83 | 60 |

### Prediction Confidence Score

## References

- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
- D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.

## Frequently Asked Questions

Q: 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)