PAAS:TSX Pan American Silver Corp.

Outlook: Pan American Silver Corp. assigned short-term B2 & long-term Ba2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 12 Dec 2022 for (n+8 weeks)
Methodology : Modular Neural Network (DNN Layer)

Abstract

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. (Waqar, M., Dawood, H., Guo, P., Shahnawaz, M.B. and Ghazanfar, M.A., 2017, December. Prediction of stock market by principal component analysis. In 2017 13th International Conference on Computational Intelligence and Security (CIS) (pp. 599-602). IEEE.) We evaluate Pan American Silver Corp. prediction models with Modular Neural Network (DNN Layer) and Factor^1,2,3,4 and conclude that the PAAS:TSX 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. Can we predict stock market using machine learning?
2. How do you know when a stock will go up or down?
3. What are the most successful trading algorithms?

PAAS:TSX Target Price Prediction Modeling Methodology

We consider Pan American Silver Corp. Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of PAAS:TSX 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(Factor)^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(Modular Neural Network (DNN Layer)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of PAAS:TSX 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?

PAAS:TSX Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: PAAS:TSX Pan American Silver Corp.
Time series to forecast n: 12 Dec 2022 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%

Adjusted IFRS* Prediction Methods for Pan American Silver Corp.

1. If an entity prepares interim financial reports in accordance with IAS 34 Interim Financial Reporting the entity need not apply the requirements in this Standard to interim periods prior to the date of initial application if it is impracticable (as defined in IAS 8).
2. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
3. If a guarantee provided by an entity to pay for default losses on a transferred asset prevents the transferred asset from being derecognised to the extent of the continuing involvement, the transferred asset at the date of the transfer is measured at the lower of (i) the carrying amount of the asset and (ii) the maximum amount of the consideration received in the transfer that the entity could be required to repay ('the guarantee amount'). The associated liability is initially measured at the guarantee amount plus the fair value of the guarantee (which is normally the consideration received for the guarantee). Subsequently, the initial fair value of the guarantee is recognised in profit or loss when (or as) the obligation is satisfied (in accordance with the principles of IFRS 15) and the carrying value of the asset is reduced by any loss allowance.
4. Paragraph 4.1.1(b) requires an entity to classify a financial asset on the basis of its contractual cash flow characteristics if the financial asset is held within a business model whose objective is to hold assets to collect contractual cash flows or within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets, unless paragraph 4.1.5 applies. To do so, the condition in paragraphs 4.1.2(b) and 4.1.2A(b) requires an entity to determine whether the asset's contractual cash flows are solely payments of principal and interest on the principal amount outstanding.

*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

Pan American Silver Corp. assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (DNN Layer) with Factor^1,2,3,4 and conclude that the PAAS:TSX 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

Financial State Forecast for PAAS:TSX Pan American Silver Corp. Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba2
Operational Risk	31	84
Market Risk	36	82
Technical Analysis	86	50
Fundamental Analysis	37	70
Risk Unsystematic	84	56

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 671 signals.

References

1. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
2. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
3. Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
4. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
5. Bessler, D. A. R. A. Babula, (1987), "Forecasting wheat exports: Do exchange rates matter?" Journal of Business and Economic Statistics, 5, 397–406.
6. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
7. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.

Frequently Asked Questions

Q: What is the prediction methodology for PAAS:TSX stock?
A: PAAS:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Factor
Q: Is PAAS:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy PAAS:TSX Stock.
Q: Is Pan American Silver Corp. stock a good investment?
A: The consensus rating for Pan American Silver Corp. is Buy and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of PAAS:TSX stock?
A: The consensus rating for PAAS:TSX is Buy.
Q: What is the prediction period for PAAS:TSX stock?
A: The prediction period for PAAS:TSX is (n+8 weeks)

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