TTM TITAN MINERALS LIMITED

Outlook: TITAN MINERALS LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 05 Apr 2023 for (n+16 weeks)
Methodology : Statistical Inference (ML)

Abstract

TITAN MINERALS LIMITED prediction model is evaluated with Statistical Inference (ML) and Pearson Correlation^1,2,3,4 and it is concluded that the TTM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. Market Signals
2. Nash Equilibria
3. Dominated Move

TTM Target Price Prediction Modeling Methodology

We consider TITAN MINERALS LIMITED Decision Process with Statistical Inference (ML) where A is the set of discrete actions of TTM 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(Pearson Correlation)^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(Statistical Inference (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of TTM 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?

TTM Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: TTM TITAN MINERALS LIMITED
Time series to forecast n: 05 Apr 2023 for (n+16 weeks)

According to price forecasts for (n+16 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 TITAN MINERALS LIMITED

1. Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.
2. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
3. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.
4. The definition of a derivative refers to non-financial variables that are not specific to a party to the contract. These include an index of earthquake losses in a particular region and an index of temperatures in a particular city. Non-financial variables specific to a party to the contract include the occurrence or non-occurrence of a fire that damages or destroys an asset of a party to the contract. A change in the fair value of a non-financial asset is specific to the owner if the fair value reflects not only changes in market prices for such assets (a financial variable) but also the condition of the specific non-financial asset held (a non-financial variable). For example, if a guarantee of the residual value of a specific car exposes the guarantor to the risk of changes in the car's physical condition, the change in that residual value is specific to the owner of the car.

*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

TITAN MINERALS LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. TITAN MINERALS LIMITED prediction model is evaluated with Statistical Inference (ML) and Pearson Correlation^1,2,3,4 and it is concluded that the TTM stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Hold

TTM TITAN MINERALS LIMITED Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Ba3
Balance Sheet	B3	C
Leverage Ratios	B2	Caa2
Cash Flow	Ba3	Baa2
Rates of Return and Profitability	Baa2	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?

Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 689 signals.

References

1. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
2. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
3. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
4. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
5. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
6. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
7. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010

Frequently Asked Questions

Q: What is the prediction methodology for TTM stock?
A: TTM stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Pearson Correlation
Q: Is TTM stock a buy or sell?
A: The dominant strategy among neural network is to Hold TTM Stock.
Q: Is TITAN MINERALS LIMITED stock a good investment?
A: The consensus rating for TITAN MINERALS LIMITED is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of TTM stock?
A: The consensus rating for TTM is Hold.
Q: What is the prediction period for TTM stock?
A: The prediction period for TTM is (n+16 weeks)

Premium

• Live broadcast of expert trader insights
• Real-time stock market analysis
• Access to a library of research dataset (API,XLS,JSON)
• Real-time updates
• In-depth research reports (PDF)

Login