TK:TSXV Stock: Set to Take Off

Outlook: Tinka Resources Limited is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised :
Dominant Strategy : Buy
Time series to forecast n: for 16 Weeks
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Sign Test
Surveillance : Major exchange and OTC

Summary

Tinka Resources Limited prediction model is evaluated with Multi-Task Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the TK:TSXV stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. What is a prediction confidence?
2. Which neural network is best for prediction?
3. What are the most successful trading algorithms?

TK:TSXV Target Price Prediction Modeling Methodology

We consider Tinka Resources Limited Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of TK: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(Sign 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-Task Learning (ML)) X S(n):→ 16 Weeks $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of TK:TSXV stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-Task Learning (ML)

Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

Sign Test

The sign test is a non-parametric hypothesis test that is used to compare two paired samples. In a paired sample, 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 sign test is a non-parametric test, which means that it does not assume that the data is normally distributed. The sign 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?

TK:TSXV Stock Forecast (Buy or Sell) for 16 Weeks

Sample Set: Neural Network
Stock/Index: TK:TSXV Tinka Resources Limited
Time series to forecast: 16 Weeks

According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Strategic Interaction Table

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-Task Learning (ML) based TK:TSXV Prediction Model

1. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.
2. For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
3. 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.
4. The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.

*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

Tinka Resources Limited is assigned short-term Ba3 & long-term B1 estimated rating. Tinka Resources Limited prediction model is evaluated with Multi-Task Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the TK:TSXV stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

TK:TSXV Tinka Resources Limited Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Income Statement	Baa2	Caa2
Balance Sheet	Caa2	Baa2
Leverage Ratios	Caa2	B3
Cash Flow	Baa2	Ba2
Rates of Return and Profitability	Baa2	C

*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: 73 out of 100 with 680 signals.

References

1. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
2. 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.
3. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
4. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
5. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
6. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
7. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for TK:TSXV stock?
A: TK:TSXV stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Sign Test
Q: Is TK:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Buy TK:TSXV Stock.
Q: Is Tinka Resources Limited stock a good investment?
A: The consensus rating for Tinka Resources Limited is Buy and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of TK:TSXV stock?
A: The consensus rating for TK:TSXV is Buy.
Q: What is the prediction period for TK:TSXV stock?
A: The prediction period for TK:TSXV is 16 Weeks

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