Modelling A.I. in Economics

KTA Stock: A High-Risk, High-Reward Investment

Outlook: KRAKATOA RESOURCES LIMITED is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Market Direction Analysis)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.

Summary

KRAKATOA RESOURCES LIMITED prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Chi-Square1,2,3,4 and it is concluded that the KTA stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

Graph 42

Key Points

  1. Market Signals
  2. How can neural networks improve predictions?
  3. Is now good time to invest?

KTA Target Price Prediction Modeling Methodology

We consider KRAKATOA RESOURCES LIMITED Decision Process with Modular Neural Network (Market Direction Analysis) 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(Chi-Square)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ 4 Weeks S = s 1 s 2 s 3

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

Modular Neural Network (Market Direction Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements.

Chi-Square

A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.

 

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)

Sample Set: Neural Network
Stock/Index: KTA KRAKATOA RESOURCES LIMITED
Time series to forecast: 4 Weeks

According to price forecasts, the dominant strategy among neural network is: Buy

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Modular Neural Network (Market Direction Analysis) based KTA Stock Prediction Model

  1. Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
  2. If subsequently an entity reasonably expects that the alternative benchmark rate will not be separately identifiable within 24 months from the date the entity designated it as a non-contractually specified risk component for the first time, the entity shall cease applying the requirement in paragraph 6.9.11 to that alternative benchmark rate and discontinue hedge accounting prospectively from the date of that reassessment for all hedging relationships in which the alternative benchmark rate was designated as a noncontractually specified risk component.
  3. For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
  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) 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.

KTA KRAKATOA RESOURCES LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Income StatementBaa2Caa2
Balance SheetCaa2Baa2
Leverage RatiosBaa2Ba1
Cash FlowBaa2Ba3
Rates of Return and ProfitabilityCB3

*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?

Conclusions

KRAKATOA RESOURCES LIMITED is assigned short-term Ba3 & long-term Ba3 estimated rating. KRAKATOA RESOURCES LIMITED prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Chi-Square1,2,3,4 and it is concluded that the KTA stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 721 signals.

References

  1. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
  2. 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
  3. Breiman L. 1996. Bagging predictors. Mach. Learn. 24:123–40
  4. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
  5. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
  6. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
  7. Harris ZS. 1954. Distributional structure. Word 10:146–62
Frequently Asked QuestionsQ: What is the prediction methodology for KTA stock?
A: KTA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Chi-Square
Q: Is KTA stock a buy or sell?
A: The dominant strategy among neural network is to Buy KTA Stock.
Q: Is KRAKATOA RESOURCES LIMITED stock a good investment?
A: The consensus rating for KRAKATOA RESOURCES LIMITED is Buy and is assigned short-term Ba3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of KTA stock?
A: The consensus rating for KTA is Buy.
Q: What is the prediction period for KTA stock?
A: The prediction period for KTA is 4 Weeks

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