It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values.** We evaluate Deutsche Post prediction models with Transfer Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the DPW.DE 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 Buy DPW.DE stock.**

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

*Keywords:*## Key Points

- Decision Making
- What is the best way to predict stock prices?
- Fundemental Analysis with Algorithmic Trading

## DPW.DE 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 Deutsche Post Stock Decision Process with Logistic Regression where A is the set of discrete actions of DPW.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(Logistic Regression)

^{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(Transfer Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of DPW.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?

## DPW.DE Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**DPW.DE Deutsche Post

**Time series to forecast n: 13 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy DPW.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 Post assigned short-term Baa2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the DPW.DE 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 Buy DPW.DE stock.**

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

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

Outlook* | Baa2 | B1 |

Operational Risk | 70 | 49 |

Market Risk | 86 | 78 |

Technical Analysis | 71 | 48 |

Fundamental Analysis | 80 | 34 |

Risk Unsystematic | 79 | 86 |

### Prediction Confidence Score

## References

- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
- Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]

## Frequently Asked Questions

Q: What is the prediction methodology for DPW.DE stock?A: DPW.DE stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Logistic Regression

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

A: The dominant strategy among neural network is to Buy DPW.DE Stock.

Q: Is Deutsche Post stock a good investment?

A: The consensus rating for Deutsche Post is Buy and assigned short-term Baa2 & long-term B1 forecasted stock rating.

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

A: The consensus rating for DPW.DE is Buy.

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

A: The prediction period for DPW.DE is (n+6 month)

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