The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. ** We evaluate T-Mobile US prediction models with Modular Neural Network (DNN Layer) and Independent T-Test ^{1,2,3,4} and conclude that the TMUS 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 TMUS stock.**

**TMUS, T-Mobile US, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Short/Long Term Stocks
- Operational Risk
- How do you pick a stock?

## TMUS Target Price Prediction Modeling Methodology

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We consider T-Mobile US Stock Decision Process with Independent T-Test where A is the set of discrete actions of TMUS 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 (DNN Layer)) X S(n):→ (n+3 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of TMUS 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?

## TMUS Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**TMUS T-Mobile US

**Time series to forecast n: 23 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold TMUS 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

T-Mobile US assigned short-term B1 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Independent T-Test ^{1,2,3,4} and conclude that the TMUS 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 TMUS stock.**

### Financial State Forecast for TMUS Stock Options & Futures

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

Outlook* | B1 | Baa2 |

Operational Risk | 70 | 73 |

Market Risk | 31 | 88 |

Technical Analysis | 66 | 49 |

Fundamental Analysis | 75 | 89 |

Risk Unsystematic | 67 | 67 |

### Prediction Confidence Score

## References

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- Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
- J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989

## Frequently Asked Questions

Q: What is the prediction methodology for TMUS stock?A: TMUS stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Independent T-Test

Q: Is TMUS stock a buy or sell?

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

Q: Is T-Mobile US stock a good investment?

A: The consensus rating for T-Mobile US is Hold and assigned short-term B1 & long-term Baa2 forecasted stock rating.

Q: What is the consensus rating of TMUS stock?

A: The consensus rating for TMUS is Hold.

Q: What is the prediction period for TMUS stock?

A: The prediction period for TMUS is (n+3 month)

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