PTM:TSX Stock: Set to Take Off

Outlook: Platinum Group Metals Ltd. is assigned short-term B1 & long-term B1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 21 Jun 2023 for 1 Year
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

Platinum Group Metals Ltd. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression^1,2,3,4 and it is concluded that the PTM:TSX stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy

Key Points

1. Why do we need predictive models?
2. What are the most successful trading algorithms?
3. Fundemental Analysis with Algorithmic Trading

PTM:TSX Target Price Prediction Modeling Methodology

We consider Platinum Group Metals Ltd. Decision Process with Modular Neural Network (News Feed Sentiment Analysis) 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(Multiple Regression)^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 (News Feed Sentiment Analysis)) X S(n):→ 1 Year $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

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

Modular Neural Network (News Feed Sentiment Analysis)

A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.

 

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 1 Year

Sample Set: Neural Network
Stock/Index: PTM:TSX Platinum Group Metals Ltd.
Time series to forecast n: 21 Jun 2023 for 1 Year

According to price forecasts for 1 Year 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 Platinum Group Metals Ltd.

1. For the purpose of applying the requirement in paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
2. A similar example of a non-financial item is a specific type of crude oil from a particular oil field that is priced off the relevant benchmark crude oil. If an entity sells that crude oil under a contract using a contractual pricing formula that sets the price per barrel at the benchmark crude oil price minus CU10 with a floor of CU15, the entity can designate as the hedged item the entire cash flow variability under the sales contract that is attributable to the change in the benchmark crude oil price. However, the entity cannot designate a component that is equal to the full change in the benchmark crude oil price. Hence, as long as the forward price (for each delivery) does not fall below CU25, the hedged item has the same cash flow variability as a crude oil sale at the benchmark crude oil price (or with a positive spread). However, if the forward price for any delivery falls below CU25, the hedged item has a lower cash flow variability than a crude oil sale at the benchmark crude oil price (or with a positive spread).
3. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
4. An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.

*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

Platinum Group Metals Ltd. is assigned short-term B1 & long-term B1 estimated rating. Platinum Group Metals Ltd. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression^1,2,3,4 and it is concluded that the PTM:TSX stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy

PTM:TSX Platinum Group Metals Ltd. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Income Statement	Baa2	Baa2
Balance Sheet	C	Caa2
Leverage Ratios	Caa2	B1
Cash Flow	Baa2	B1
Rates of Return and Profitability	Ba3	B2

*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: 91 out of 100 with 500 signals.

References

1. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
2. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
3. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
4. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
5. 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.
6. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
7. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37

Frequently Asked Questions

Q: What is the prediction methodology for PTM:TSX stock?
A: PTM:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression
Q: Is PTM:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy PTM:TSX Stock.
Q: Is Platinum Group Metals Ltd. stock a good investment?
A: The consensus rating for Platinum Group Metals Ltd. is Buy and is assigned short-term B1 & long-term B1 estimated rating.
Q: What is the consensus rating of PTM:TSX stock?
A: The consensus rating for PTM:TSX is Buy.
Q: What is the prediction period for PTM:TSX stock?
A: The prediction period for PTM:TSX is 1 Year

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