Buy, sell or hold: KMB Stock Forecast (Forecast)

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. We evaluate Kimberly-Clark prediction models with Multi-Task Learning (ML) and Beta^1,2,3,4 and conclude that the KMB 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 Sell KMB stock.

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

Key Points

1. Can statistics predict the future?
2. What is neural prediction?
3. Is Target price a good indicator?

KMB Target Price Prediction Modeling Methodology

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. We consider Kimberly-Clark Stock Decision Process with Beta where A is the set of discrete actions of KMB 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(Beta)^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(Multi-Task Learning (ML)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of KMB 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?

KMB Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: KMB Kimberly-Clark
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 Sell KMB 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

Kimberly-Clark assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Beta^1,2,3,4 and conclude that the KMB 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 Sell KMB stock.

Financial State Forecast for KMB Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Operational Risk	87	83
Market Risk	43	61
Technical Analysis	62	37
Fundamental Analysis	76	60
Risk Unsystematic	35	43

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 729 signals.

References

1. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
2. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
3. 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.
4. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
5. 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
6. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
7. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer

Frequently Asked Questions

Q: What is the prediction methodology for KMB stock?
A: KMB stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Beta
Q: Is KMB stock a buy or sell?
A: The dominant strategy among neural network is to Sell KMB Stock.
Q: Is Kimberly-Clark stock a good investment?
A: The consensus rating for Kimberly-Clark is Sell and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of KMB stock?
A: The consensus rating for KMB is Sell.
Q: What is the prediction period for KMB stock?
A: The prediction period for KMB is (n+6 month)

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