Modelling A.I. in Economics

BZ:TSXV Benz Mining Corp. (Forecast)

Outlook: Benz Mining Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 21 May 2023 for (n+8 weeks)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

Benz Mining Corp. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Sign Test1,2,3,4 and it is concluded that the BZ:TSXV stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

Key Points

  1. Should I buy stocks now or wait amid such uncertainty?
  2. What is the use of Markov decision process?
  3. Stock Rating

BZ:TSXV Target Price Prediction Modeling Methodology

We consider Benz Mining Corp. Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of BZ:TSXV 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(Sign Test)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 (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks) i = 1 n a i

n:Time series to forecast

p:Price signals of BZ:TSXV 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?

BZ:TSXV Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: BZ:TSXV Benz Mining Corp.
Time series to forecast n: 21 May 2023 for (n+8 weeks)

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

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 Benz Mining Corp.

  1. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
  2. The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.
  3. An entity shall amend a hedging relationship as required in paragraph 6.9.1 by the end of the reporting period during which a change required by interest rate benchmark reform is made to the hedged risk, hedged item or hedging instrument. For the avoidance of doubt, such an amendment to the formal designation of a hedging relationship constitutes neither the discontinuation of the hedging relationship nor the designation of a new hedging relationship.
  4. An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.

*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

Benz Mining Corp. is assigned short-term Ba1 & long-term Ba1 estimated rating. Benz Mining Corp. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Sign Test1,2,3,4 and it is concluded that the BZ:TSXV stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Buy

BZ:TSXV Benz Mining Corp. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCBaa2
Balance SheetBaa2B3
Leverage RatiosBaa2Ba3
Cash FlowB3Baa2
Rates of Return and ProfitabilityCC

*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: 93 out of 100 with 460 signals.

References

  1. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
  2. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
  3. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
  4. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
  5. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
  6. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
  7. 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.
Frequently Asked QuestionsQ: What is the prediction methodology for BZ:TSXV stock?
A: BZ:TSXV stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Sign Test
Q: Is BZ:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Buy BZ:TSXV Stock.
Q: Is Benz Mining Corp. stock a good investment?
A: The consensus rating for Benz Mining Corp. is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of BZ:TSXV stock?
A: The consensus rating for BZ:TSXV is Buy.
Q: What is the prediction period for BZ:TSXV stock?
A: The prediction period for BZ:TSXV is (n+8 weeks)

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