KTA KRAKATOA RESOURCES LIMITED

Outlook: KRAKATOA RESOURCES LIMITED assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 16 Dec 2022 for (n+16 weeks)
Methodology : Active Learning (ML)

Abstract

In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour.(Parray, I.R., Khurana, S.S., Kumar, M. and Altalbe, A.A., 2020. Time series data analysis of stock price movement using machine learning techniques. Soft Computing, 24(21), pp.16509-16517.) We evaluate KRAKATOA RESOURCES LIMITED prediction models with Active Learning (ML) and Lasso Regression^1,2,3,4 and conclude that the KTA stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. What is Markov decision process in reinforcement learning?
2. Can we predict stock market using machine learning?
3. Market Risk

KTA Target Price Prediction Modeling Methodology

We consider KRAKATOA RESOURCES LIMITED Decision Process with Active Learning (ML) where A is the set of discrete actions of KTA 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(Lasso 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(Active Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of KTA 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?

KTA Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: KTA KRAKATOA RESOURCES LIMITED
Time series to forecast n: 16 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) 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 (Grey to Black): *Technical Analysis%

Adjusted IFRS* Prediction Methods for KRAKATOA RESOURCES LIMITED

1. An entity may retain the right to a part of the interest payments on transferred assets as compensation for servicing those assets. The part of the interest payments that the entity would give up upon termination or transfer of the servicing contract is allocated to the servicing asset or servicing liability. The part of the interest payments that the entity would not give up is an interest-only strip receivable. For example, if the entity would not give up any interest upon termination or transfer of the servicing contract, the entire interest spread is an interest-only strip receivable. For the purposes of applying paragraph 3.2.13, the fair values of the servicing asset and interest-only strip receivable are used to allocate the carrying amount of the receivable between the part of the asset that is derecognised and the part that continues to be recognised. If there is no servicing fee specified or the fee to be received is not expected to compensate the entity adequately for performing the servicing, a liability for the servicing obligation is recognised at fair value.
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. In some jurisdictions, the government or a regulatory authority sets interest rates. For example, such government regulation of interest rates may be part of a broad macroeconomic policy or it may be introduced to encourage entities to invest in a particular sector of the economy. In some of these cases, the objective of the time value of money element is not to provide consideration for only the passage of time. However, despite paragraphs B4.1.9A–B4.1.9D, a regulated interest rate shall be considered a proxy for the time value of money element for the purpose of applying the condition in paragraphs 4.1.2(b) and 4.1.2A(b) if that regulated interest rate provides consideration that is broadly consistent with the passage of time and does not provide exposure to risks or volatility in the contractual cash flows that are inconsistent with a basic lending arrangement.
4. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.

*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

KRAKATOA RESOURCES LIMITED assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Lasso Regression^1,2,3,4 and conclude that the KTA stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

Financial State Forecast for KTA KRAKATOA RESOURCES LIMITED Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	38	45
Market Risk	48	47
Technical Analysis	66	72
Fundamental Analysis	52	86
Risk Unsystematic	74	51

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 803 signals.

References

1. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
2. 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
3. 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
4. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
5. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
6. Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
7. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell

Frequently Asked Questions

Q: What is the prediction methodology for KTA stock?
A: KTA stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Lasso Regression
Q: Is KTA stock a buy or sell?
A: The dominant strategy among neural network is to Hold KTA Stock.
Q: Is KRAKATOA RESOURCES LIMITED stock a good investment?
A: The consensus rating for KRAKATOA RESOURCES LIMITED is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of KTA stock?
A: The consensus rating for KTA is Hold.
Q: What is the prediction period for KTA stock?
A: The prediction period for KTA is (n+16 weeks)

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