Modelling A.I. in Economics

PTM:TSX Platinum Group Metals Ltd. Stock Forecast

Platinum Group Metals Ltd. Research Report

Summary

Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed. We evaluate Platinum Group Metals Ltd. prediction models with Reinforcement Machine Learning (ML) and Linear Regression1,2,3,4 and conclude that the PTM:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell PTM:TSX stock.

Key Points

  1. Stock Rating
  2. What is neural prediction?
  3. Is now good time to invest?

PTM:TSX Target Price Prediction Modeling Methodology

We consider Platinum Group Metals Ltd. Stock Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of PTM: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(Linear Regression)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(Reinforcement Machine Learning (ML)) X S(n):→ (n+3 month) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of PTM: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?

PTM:TSX Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: PTM:TSX Platinum Group Metals Ltd.
Time series to forecast n: 26 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell PTM:TSX stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Platinum Group Metals Ltd.

  1. If a financial instrument is designated in accordance with paragraph 6.7.1 as measured at fair value through profit or loss after its initial recognition, or was previously not recognised, the difference at the time of designation between the carrying amount, if any, and the fair value shall immediately be recognised in profit or loss. For financial assets measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A, the cumulative gain or loss previously recognised in other comprehensive income shall immediately be reclassified from equity to profit or loss as a reclassification adjustment.
  2. A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
  3. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
  4. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.

*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

Platinum Group Metals Ltd. assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Linear Regression1,2,3,4 and conclude that the PTM:TSX stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell PTM:TSX stock.

Financial State Forecast for PTM:TSX Platinum Group Metals Ltd. Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2Ba3
Operational Risk 8674
Market Risk5257
Technical Analysis6836
Fundamental Analysis5580
Risk Unsystematic7784

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 842 signals.

References

  1. Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
  2. 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
  3. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
  4. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
  5. Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
  6. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
  7. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
Frequently Asked QuestionsQ: What is the prediction methodology for PTM:TSX stock?
A: PTM:TSX stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Linear Regression
Q: Is PTM:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell PTM:TSX Stock.
Q: Is Platinum Group Metals Ltd. stock a good investment?
A: The consensus rating for Platinum Group Metals Ltd. is Sell and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of PTM:TSX stock?
A: The consensus rating for PTM:TSX is Sell.
Q: What is the prediction period for PTM:TSX stock?
A: The prediction period for PTM:TSX is (n+3 month)

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