Modelling A.I. in Economics

SCZ:TSXV Santacruz Silver Mining Ltd. (Forecast)

Outlook: Santacruz Silver Mining Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 18 May 2023 for (n+1 year)
Methodology : Statistical Inference (ML)

Abstract

Santacruz Silver Mining Ltd. prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the SCZ:TSXV stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

Key Points

  1. What is neural prediction?
  2. What are the most successful trading algorithms?
  3. Decision Making

SCZ:TSXV Target Price Prediction Modeling Methodology

We consider Santacruz Silver Mining Ltd. Decision Process with Statistical Inference (ML) where A is the set of discrete actions of SCZ: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(Wilcoxon Sign-Rank 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(Statistical Inference (ML)) X S(n):→ (n+1 year) i = 1 n s i

n:Time series to forecast

p:Price signals of SCZ: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?

SCZ:TSXV Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: SCZ:TSXV Santacruz Silver Mining Ltd.
Time series to forecast n: 18 May 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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%

IFRS Reconciliation Adjustments for Santacruz Silver Mining Ltd.

  1. If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.
  2. Measurement of a financial asset or financial liability and classification of recognised changes in its value are determined by the item's classification and whether the item is part of a designated hedging relationship. Those requirements can create a measurement or recognition inconsistency (sometimes referred to as an 'accounting mismatch') when, for example, in the absence of designation as at fair value through profit or loss, a financial asset would be classified as subsequently measured at fair value through profit or loss and a liability the entity considers related would be subsequently measured at amortised cost (with changes in fair value not recognised). In such circumstances, an entity may conclude that its financial statements would provide more relevant information if both the asset and the liability were measured as at fair value through profit or loss.
  3. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
  4. If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)

*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

Santacruz Silver Mining Ltd. is assigned short-term Ba1 & long-term Ba1 estimated rating. Santacruz Silver Mining Ltd. prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the SCZ:TSXV stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

SCZ:TSXV Santacruz Silver Mining Ltd. Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3Baa2
Balance SheetCBaa2
Leverage RatiosCaa2Caa2
Cash FlowB2Baa2
Rates of Return and ProfitabilityCBaa2

*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: 76 out of 100 with 675 signals.

References

  1. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
  2. Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
  3. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
  4. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
  5. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
  6. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
  7. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
Frequently Asked QuestionsQ: What is the prediction methodology for SCZ:TSXV stock?
A: SCZ:TSXV stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Sign-Rank Test
Q: Is SCZ:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Hold SCZ:TSXV Stock.
Q: Is Santacruz Silver Mining Ltd. stock a good investment?
A: The consensus rating for Santacruz Silver Mining Ltd. is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of SCZ:TSXV stock?
A: The consensus rating for SCZ:TSXV is Hold.
Q: What is the prediction period for SCZ:TSXV stock?
A: The prediction period for SCZ:TSXV is (n+1 year)

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