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 evaluate Deutsche Börse prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation1,2,3,4 and conclude that the DB1.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 Hold DB1.DE stock.
Keywords: DB1.DE, Deutsche Börse, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Trading Signals
- Can statistics predict the future?
- Can stock prices be predicted?
DB1.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 Deutsche Börse Stock Decision Process with Pearson Correlation where A is the set of discrete actions of DB1.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(Pearson Correlation)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of DB1.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?
DB1.DE Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: DB1.DE Deutsche Börse
Time series to forecast n: 25 Sep 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold DB1.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 Börse assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Pearson Correlation1,2,3,4 and conclude that the DB1.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 Hold DB1.DE stock.
Financial State Forecast for DB1.DE Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B2 |
| Operational Risk | 64 | 65 |
| Market Risk | 75 | 31 |
| Technical Analysis | 42 | 72 |
| Fundamental Analysis | 50 | 40 |
| Risk Unsystematic | 66 | 67 |
Prediction Confidence Score
References
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- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
Frequently Asked Questions
Q: What is the prediction methodology for DB1.DE stock?A: DB1.DE stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation
Q: Is DB1.DE stock a buy or sell?
A: The dominant strategy among neural network is to Hold DB1.DE Stock.
Q: Is Deutsche Börse stock a good investment?
A: The consensus rating for Deutsche Börse is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of DB1.DE stock?
A: The consensus rating for DB1.DE is Hold.
Q: What is the prediction period for DB1.DE stock?
A: The prediction period for DB1.DE is (n+6 month)
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