PRU:TSX Stock: A Spark That Could Fizzle

Outlook: Perseus Mining Limited is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Multi-Instance Learning (ML)
Hypothesis Testing : Paired T-Test
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Abstract

Perseus Mining Limited prediction model is evaluated with Multi-Instance Learning (ML) and Paired T-Test^1,2,3,4 and it is concluded that the PRU:TSX stock is predictable in the short/long term. Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Key Points

1. Buy, Sell and Hold Signals
2. Why do we need predictive models?
3. Can statistics predict the future?

PRU:TSX Target Price Prediction Modeling Methodology

We consider Perseus Mining Limited Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of PRU: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(Paired T-Test)^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(Multi-Instance Learning (ML)) X S(n):→ 6 Month $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of PRU:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Instance Learning (ML)

Multi-instance learning (MIL) is a machine learning (ML) problem where a dataset consists of multiple instances, and each instance is associated with a single label. The goal of MIL is to learn a model that can predict the label of a new instance based on the labels of the instances that it is similar to. MIL is a challenging problem because the instances in a dataset are not labeled individually. This means that the model cannot simply learn a mapping from the features of an instance to its label. Instead, the model must learn a way to combine the features of multiple instances to predict the label of a new instance.

Paired T-Test

A paired t-test is a statistical test that compares the means of two paired samples. In a paired t-test, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The paired t-test is a parametric test, which means that it assumes that the data is normally distributed. The paired t-test is also a dependent samples test, which means that the data points in each pair are correlated.

 

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?

PRU:TSX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: PRU:TSX Perseus Mining Limited
Time series to forecast: 6 Month

According to price forecasts, the dominant strategy among neural network is: Hold

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Multi-Instance Learning (ML) based PRU:TSX Stock Prediction Model

1. For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
2. An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
3. An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.
4. Lifetime expected credit losses are not recognised on a financial instrument simply because it was considered to have low credit risk in the previous reporting period and is not considered to have low credit risk at the reporting date. In such a case, an entity shall determine whether there has been a significant increase in credit risk since initial recognition and thus whether lifetime expected credit losses are required to be recognised in accordance with paragraph 5.5.3.

*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.

PRU:TSX Perseus Mining Limited Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Income Statement	Baa2	Baa2
Balance Sheet	B3	Baa2
Leverage Ratios	C	Baa2
Cash Flow	Baa2	Caa2
Rates of Return and Profitability	Caa2	B3

*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?

Conclusions

Perseus Mining Limited is assigned short-term B2 & long-term Ba3 estimated rating. Perseus Mining Limited prediction model is evaluated with Multi-Instance Learning (ML) and Paired T-Test^1,2,3,4 and it is concluded that the PRU:TSX stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 500 signals.

References

1. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
2. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
3. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
4. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
5. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
6. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
7. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004

Frequently Asked Questions

Q: What is the prediction methodology for PRU:TSX stock?
A: PRU:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Paired T-Test
Q: Is PRU:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold PRU:TSX Stock.
Q: Is Perseus Mining Limited stock a good investment?
A: The consensus rating for Perseus Mining Limited is Hold and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of PRU:TSX stock?
A: The consensus rating for PRU:TSX is Hold.
Q: What is the prediction period for PRU:TSX stock?
A: The prediction period for PRU:TSX is 6 Month

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