KKO KINETIKO ENERGY LTD

Outlook: KINETIKO ENERGY LTD assigned short-term B3 & long-term Baa2 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 08 Dec 2022 for (n+6 month)
Methodology : Transfer Learning (ML)

Abstract

Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods(Kanade, P.A., Singh, S., Rajoria, S., Veer, P. and Wandile, N., 2020. Machine learning model for stock market prediction. International Journal for Research in Applied Science and Engineering Technology, 8(6), pp.209-216.) We evaluate KINETIKO ENERGY LTD prediction models with Transfer Learning (ML) and Pearson Correlation^1,2,3,4 and conclude that the KKO stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Key Points

1. Is now good time to invest?
2. Can we predict stock market using machine learning?
3. What is Markov decision process in reinforcement learning?

KKO Target Price Prediction Modeling Methodology

We consider KINETIKO ENERGY LTD Decision Process with Transfer Learning (ML) where A is the set of discrete actions of KKO 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of KKO 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?

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

Sample Set: Neural Network
Stock/Index: KKO KINETIKO ENERGY LTD
Time series to forecast n: 08 Dec 2022 for (n+6 month)

According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

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%

Adjusted IFRS* Prediction Methods for KINETIKO ENERGY LTD

1. For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
2. For a financial guarantee contract, the entity is required to make payments only in the event of a default by the debtor in accordance with the terms of the instrument that is guaranteed. Accordingly, cash shortfalls are the expected payments to reimburse the holder for a credit loss that it incurs less any amounts that the entity expects to receive from the holder, the debtor or any other party. If the asset is fully guaranteed, the estimation of cash shortfalls for a financial guarantee contract would be consistent with the estimations of cash shortfalls for the asset subject to the guarantee
3. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
4. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

KINETIKO ENERGY LTD assigned short-term B3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Pearson Correlation^1,2,3,4 and conclude that the KKO stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Hold

Financial State Forecast for KKO KINETIKO ENERGY LTD Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Baa2
Operational Risk	31	80
Market Risk	43	61
Technical Analysis	79	89
Fundamental Analysis	31	90
Risk Unsystematic	58	59

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 679 signals.

References

1. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
2. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can stock prices be predicted?(SMI Index Stock Forecast). AC Investment Research Journal, 101(3).
4. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
5. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
6. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
7. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for KKO stock?
A: KKO stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Pearson Correlation
Q: Is KKO stock a buy or sell?
A: The dominant strategy among neural network is to Hold KKO Stock.
Q: Is KINETIKO ENERGY LTD stock a good investment?
A: The consensus rating for KINETIKO ENERGY LTD is Hold and assigned short-term B3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of KKO stock?
A: The consensus rating for KKO is Hold.
Q: What is the prediction period for KKO stock?
A: The prediction period for KKO is (n+6 month)

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