TNX:TSX TRX Gold Corporation

Outlook: TRX Gold Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 11 Jan 2023 for (n+8 weeks)
Methodology : Transductive Learning (ML)

Abstract

TRX Gold Corporation prediction model is evaluated with Transductive Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the TNX: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: Hold

Key Points

1. Why do we need predictive models?
2. Market Risk
3. Can we predict stock market using machine learning?

TNX:TSX Target Price Prediction Modeling Methodology

We consider TRX Gold Corporation Decision Process with Transductive Learning (ML) where A is the set of discrete actions of TNX: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(Statistical Hypothesis Testing)^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(Transductive Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of TNX: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?

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

Sample Set: Neural Network
Stock/Index: TNX:TSX TRX Gold Corporation
Time series to forecast n: 11 Jan 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

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 TRX Gold Corporation

1. When assessing a modified time value of money element, an entity must consider factors that could affect future contractual cash flows. For example, if an entity is assessing a bond with a five-year term and the variable interest rate is reset every six months to a five-year rate, the entity cannot conclude that the contractual cash flows are solely payments of principal and interest on the principal amount outstanding simply because the interest rate curve at the time of the assessment is such that the difference between a five-year interest rate and a six-month interest rate is not significant. Instead, the entity must also consider whether the relationship between the five-year interest rate and the six-month interest rate could change over the life of the instrument such that the contractual (undiscounted) cash flows over the life of the instrument could be significantly different from the (undiscounted) benchmark cash flows. However, an entity must consider only reasonably possible scenarios instead of every possible scenario. If an entity concludes that the contractual (undiscounted) cash flows could be significantly different from the (undiscounted) benchmark cash flows, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and therefore cannot be measured at amortised cost or fair value through other comprehensive income.
2. If an entity previously accounted for a derivative liability that is linked to, and must be settled by, delivery of an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) at cost in accordance with IAS 39, it shall measure that derivative liability at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings of the reporting period that includes the date of initial application.
3. 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).
4. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.

*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

TRX Gold Corporation is assigned short-term Ba1 & long-term Ba1 estimated rating. TRX Gold Corporation prediction model is evaluated with Transductive Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the TNX: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: Hold

TNX:TSX TRX Gold Corporation Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Caa2
Balance Sheet	C	Caa2
Leverage Ratios	C	Baa2
Cash Flow	Baa2	B1
Rates of Return and Profitability	Baa2	Baa2

*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: 78 out of 100 with 791 signals.

References

1. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
2. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
3. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
4. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
5. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
6. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
7. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015

Frequently Asked Questions

Q: What is the prediction methodology for TNX:TSX stock?
A: TNX:TSX stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Statistical Hypothesis Testing
Q: Is TNX:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Hold TNX:TSX Stock.
Q: Is TRX Gold Corporation stock a good investment?
A: The consensus rating for TRX Gold Corporation is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of TNX:TSX stock?
A: The consensus rating for TNX:TSX is Hold.
Q: What is the prediction period for TNX:TSX stock?
A: The prediction period for TNX:TSX is (n+8 weeks)

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