The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions.** We evaluate Siemens prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Logistic Regression ^{1,2,3,4} and conclude that the SIE.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 SIE.DE stock.**

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

*Keywords:*## Key Points

- Can stock prices be predicted?
- Trading Signals
- Can neural networks predict stock market?

## SIE.DE Target Price Prediction Modeling Methodology

Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. We consider Siemens Stock Decision Process with Logistic Regression where A is the set of discrete actions of SIE.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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of SIE.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?

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

**Sample Set:**Neural Network

**Stock/Index:**SIE.DE Siemens

**Time series to forecast n: 18 Oct 2022**for (n+4 weeks)

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

Siemens assigned short-term B2 & long-term B3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Logistic Regression ^{1,2,3,4} and conclude that the SIE.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 SIE.DE stock.**

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

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

Outlook* | B2 | B3 |

Operational Risk | 50 | 52 |

Market Risk | 31 | 55 |

Technical Analysis | 42 | 33 |

Fundamental Analysis | 62 | 36 |

Risk Unsystematic | 84 | 32 |

### Prediction Confidence Score

## References

- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
- 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
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
- Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
- M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002

## Frequently Asked Questions

Q: What is the prediction methodology for SIE.DE stock?A: SIE.DE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Logistic Regression

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

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

Q: Is Siemens stock a good investment?

A: The consensus rating for Siemens is Hold and assigned short-term B2 & long-term B3 forecasted stock rating.

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

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

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

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