KE Kimball Electronics Inc. Common Stock (Forecast)

Outlook: Kimball Electronics Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 26 Jan 2023 for (n+8 weeks)
Methodology : Modular Neural Network (CNN Layer)

Abstract

Kimball Electronics Inc. Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Logistic Regression^1,2,3,4 and it is concluded that the KE stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. What is Markov decision process in reinforcement learning?
2. Fundemental Analysis with Algorithmic Trading
3. Game Theory

KE Target Price Prediction Modeling Methodology

We consider Kimball Electronics Inc. Common Stock Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of KE 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(Modular Neural Network (CNN Layer)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of KE stock

j:Nash equilibria (Neural Network)

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?

KE Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: KE Kimball Electronics Inc. Common Stock
Time series to forecast n: 26 Jan 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

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 (Grey to Black): *Technical Analysis%

IFRS Reconciliation Adjustments for Kimball Electronics Inc. Common Stock

1. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
2. An entity's business model refers to how an entity manages its financial assets in order to generate cash flows. That is, the entity's business model determines whether cash flows will result from collecting contractual cash flows, selling financial assets or both. Consequently, this assessment is not performed on the basis of scenarios that the entity does not reasonably expect to occur, such as so-called 'worst case' or 'stress case' scenarios. For example, if an entity expects that it will sell a particular portfolio of financial assets only in a stress case scenario, that scenario would not affect the entity's assessment of the business model for those assets if the entity reasonably expects that such a scenario will not occur. If cash flows are realised in a way that is different from the entity's expectations at the date that the entity assessed the business model (for example, if the entity sells more or fewer financial assets than it expected when it classified the assets), that does not give rise to a prior period error in the entity's financial statements (see IAS 8 Accounting Policies, Changes in Accounting Estimates and Errors) nor does it change the classification of the remaining financial assets held in that business model (ie those assets that the entity recognised in prior periods and still holds) as long as the entity considered all relevant information that was available at the time that it made the business model assessment.
3. For the purposes of the transition provisions in paragraphs 7.2.1, 7.2.3–7.2.28 and 7.3.2, the date of initial application is the date when an entity first applies those requirements of this Standard and must be the beginning of a reporting period after the issue of this Standard. Depending on the entity's chosen approach to applying IFRS 9, the transition can involve one or more than one date of initial application for different requirements.
4. Adjusting the hedge ratio allows an entity to respond to changes in the relationship between the hedging instrument and the hedged item that arise from their underlyings or risk variables. For example, a hedging relationship in which the hedging instrument and the hedged item have different but related underlyings changes in response to a change in the relationship between those two underlyings (for example, different but related reference indices, rates or prices). Hence, rebalancing allows the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item chang

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

Kimball Electronics Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Kimball Electronics Inc. Common Stock prediction model is evaluated with Modular Neural Network (CNN Layer) and Logistic Regression^1,2,3,4 and it is concluded that the KE stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

KE Kimball Electronics Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Baa2
Balance Sheet	B1	Ba3
Leverage Ratios	Caa2	Baa2
Cash Flow	Ba3	C
Rates of Return and Profitability	C	Ba2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 821 signals.

References

1. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
2. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
3. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
4. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
5. Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
6. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
7. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for KE stock?
A: KE stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Logistic Regression
Q: Is KE stock a buy or sell?
A: The dominant strategy among neural network is to Sell KE Stock.
Q: Is Kimball Electronics Inc. Common Stock stock a good investment?
A: The consensus rating for Kimball Electronics Inc. Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of KE stock?
A: The consensus rating for KE is Sell.
Q: What is the prediction period for KE stock?
A: The prediction period for KE is (n+8 weeks)

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