TEMPUS RESOURCES LTD Research Report

Summary

Stock market is a promising financial investment that can generate great wealth. However, the volatile nature of the stock market makes it a very high risk investment. Thus, a lot of researchers have contributed their efforts to forecast the stock market pricing and average movement. Researchers have used various methods in computer science and economics in their quests to gain a piece of this volatile information and make great fortune out of the stock market investment. This paper investigates various techniques for the stock market prediction using artificial neural network (ANN). We evaluate TEMPUS RESOURCES LTD prediction models with Modular Neural Network (Market News Sentiment Analysis) and Linear Regression1,2,3,4 and conclude that the TMR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold TMR stock.

Key Points

1. How accurate is machine learning in stock market?
2. Fundemental Analysis with Algorithmic Trading
3. How do you know when a stock will go up or down?

TMR Target Price Prediction Modeling Methodology

We consider TEMPUS RESOURCES LTD Stock Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of TMR 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(Linear Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+3 month) $∑ i = 1 n a i$

n:Time series to forecast

p:Price signals of TMR 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?

TMR Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: TMR TEMPUS RESOURCES LTD
Time series to forecast n: 28 Nov 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold TMR stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for TEMPUS RESOURCES LTD

1. However, an entity is not required to separately recognise interest revenue or impairment gains or losses for a financial asset measured at fair value through profit or loss. Consequently, when an entity reclassifies a financial asset out of the fair value through profit or loss measurement category, the effective interest rate is determined on the basis of the fair value of the asset at the reclassification date. In addition, for the purposes of applying Section 5.5 to the financial asset from the reclassification date, the date of the reclassification is treated as the date of initial recognition.
2. If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.
3. Leverage is a contractual cash flow characteristic of some financial assets. Leverage increases the variability of the contractual cash flows with the result that they do not have the economic characteristics of interest. Stand-alone option, forward and swap contracts are examples of financial assets that include such leverage. Thus, such contracts do not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and cannot be subsequently measured at amortised cost or fair value through other comprehensive income.
4. An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

TEMPUS RESOURCES LTD assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Linear Regression1,2,3,4 and conclude that the TMR stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold TMR stock.

Financial State Forecast for TMR TEMPUS RESOURCES LTD Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 4858
Market Risk3144
Technical Analysis7069
Fundamental Analysis8157
Risk Unsystematic3265

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 494 signals.

References

1. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
2. Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
3. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
4. Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
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. 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
7. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
Frequently Asked QuestionsQ: What is the prediction methodology for TMR stock?
A: TMR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Linear Regression
Q: Is TMR stock a buy or sell?
A: The dominant strategy among neural network is to Hold TMR Stock.
Q: Is TEMPUS RESOURCES LTD stock a good investment?
A: The consensus rating for TEMPUS RESOURCES LTD is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of TMR stock?
A: The consensus rating for TMR is Hold.
Q: What is the prediction period for TMR stock?
A: The prediction period for TMR is (n+3 month)
What did you think about the prediction? (Insufficient-Outstanding)
Tell us how we can improve PredictiveAI