The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. ** We evaluate Deutsche Telekom prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Independent T-Test ^{1,2,3,4} and conclude that the DTE.DE 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 DTE.DE stock.**

**DTE.DE, Deutsche Telekom, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- What are main components of Markov decision process?
- Market Signals
- Prediction Modeling

## DTE.DE Target Price Prediction Modeling Methodology

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We consider Deutsche Telekom Stock Decision Process with Independent T-Test where A is the set of discrete actions of DTE.DE 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(Independent T-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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of DTE.DE 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?

## DTE.DE Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**DTE.DE Deutsche Telekom

**Time series to forecast n: 17 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold DTE.DE 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

Deutsche Telekom assigned short-term Ba3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Independent T-Test ^{1,2,3,4} and conclude that the DTE.DE 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 DTE.DE stock.**

### Financial State Forecast for DTE.DE Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | B2 |

Operational Risk | 40 | 35 |

Market Risk | 90 | 35 |

Technical Analysis | 81 | 80 |

Fundamental Analysis | 53 | 64 |

Risk Unsystematic | 68 | 36 |

### Prediction Confidence Score

## References

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- Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
- D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
- Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
- N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.

## Frequently Asked Questions

Q: What is the prediction methodology for DTE.DE stock?A: DTE.DE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Independent T-Test

Q: Is DTE.DE stock a buy or sell?

A: The dominant strategy among neural network is to Hold DTE.DE Stock.

Q: Is Deutsche Telekom stock a good investment?

A: The consensus rating for Deutsche Telekom is Hold and assigned short-term Ba3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of DTE.DE stock?

A: The consensus rating for DTE.DE is Hold.

Q: What is the prediction period for DTE.DE stock?

A: The prediction period for DTE.DE is (n+4 weeks)