When to Sell and When to Hold DTE Stock

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market's movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. We evaluate DTE Energy prediction models with Modular Neural Network (CNN Layer) and Factor^1,2,3,4 and conclude that the DTE 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 stock.

Keywords: DTE, DTE Energy, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Is it better to buy and sell or hold?
2. Investment Risk
3. How do you know when a stock will go up or down?

DTE Target Price Prediction Modeling Methodology

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We consider DTE Energy Stock Decision Process with Factor where A is the set of discrete actions of DTE 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(Factor)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of DTE 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 Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: DTE DTE Energy
Time series to forecast n: 08 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 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

DTE Energy assigned short-term Ba3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Factor^1,2,3,4 and conclude that the DTE 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 stock.

Financial State Forecast for DTE Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Baa2
Operational Risk	50	87
Market Risk	42	62
Technical Analysis	90	53
Fundamental Analysis	57	85
Risk Unsystematic	89	86

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 598 signals.

References

1. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
2. Artis, M. J. W. Zhang (1990), "BVAR forecasts for the G-7," International Journal of Forecasting, 6, 349–362.
3. Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
4. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
5. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
6. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
7. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for DTE stock?
A: DTE stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Factor
Q: Is DTE stock a buy or sell?
A: The dominant strategy among neural network is to Hold DTE Stock.
Q: Is DTE Energy stock a good investment?
A: The consensus rating for DTE Energy is Hold and assigned short-term Ba3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of DTE stock?
A: The consensus rating for DTE is Hold.
Q: What is the prediction period for DTE stock?
A: The prediction period for DTE is (n+4 weeks)

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