OSX OSTEOPORE LIMITED

Outlook: OSTEOPORE LIMITED assigned short-term B3 & long-term Ba3 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 07 Dec 2022 for (n+16 weeks)
Methodology : Modular Neural Network (Market News Sentiment Analysis)

Abstract

Prediction of stock market movement is extremely difficult due to its high mutable nature. The rapid ups and downs occur in stock market because of impact from foreign commodities like emotional behavior of investors, political, psychological and economical factors. Continuous unsettlement in the stock market is major reason why investors sell out at the wrong time and often fail to gain the benefit. While investing in stock market investors must not forget the risk of reward rule and expose their holdings to greater risks. Although it is not possible predict stock market movement with full accuracy, losses from selling stocks at wrong time and its impacts can be reduce to greater extent using prediction of stock market movement based on analysis of historical data. (Usmani, M., Adil, S.H., Raza, K. and Ali, S.S.A., 2016, August. Stock market prediction using machine learning techniques. In 2016 3rd international conference on computer and information sciences (ICCOINS) (pp. 322-327). IEEE.) We evaluate OSTEOPORE LIMITED prediction models with Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation^1,2,3,4 and conclude that the OSX stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell OSX stock.

Key Points

1. How useful are statistical predictions?
2. Buy, Sell and Hold Signals
3. Trust metric by Neural Network

OSX Target Price Prediction Modeling Methodology

We consider OSTEOPORE LIMITED Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of OSX 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(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of OSX 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?

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

Sample Set: Neural Network
Stock/Index: OSX OSTEOPORE LIMITED
Time series to forecast n: 07 Dec 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell OSX 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 OSTEOPORE LIMITED

1. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
2. Despite the requirement in paragraph 7.2.1, an entity that adopts the classification and measurement requirements of this Standard (which include the requirements related to amortised cost measurement for financial assets and impairment in Sections 5.4 and 5.5) shall provide the disclosures set out in paragraphs 42L–42O of IFRS 7 but need not restate prior periods. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application. However, if an entity restates prior periods, the restated financial statements must reflect all of the requirements in this Standard. If an entity's chosen approach to applying IFRS 9 results in more than one date of initial application for different requirements, this paragraph applies at each date of initial application (see paragraph 7.2.2). This would be the case, for example, if an entity elects to early apply only the requirements for the presentation of gains and losses on financial liabilities designated as at fair value through profit or loss in accordance with paragraph 7.1.2 before applying the other requirements in this Standard.
3. The definition of a derivative in this Standard includes contracts that are settled gross by delivery of the underlying item (eg a forward contract to purchase a fixed rate debt instrument). An entity may have a contract to buy or sell a non-financial item that can be settled net in cash or another financial instrument or by exchanging financial instruments (eg a contract to buy or sell a commodity at a fixed price at a future date). Such a contract is within the scope of this Standard unless it was entered into and continues to be held for the purpose of delivery of a non-financial item in accordance with the entity's expected purchase, sale or usage requirements. However, this Standard applies to such contracts for an entity's expected purchase, sale or usage requirements if the entity makes a designation in accordance with paragraph 2.5 (see paragraphs 2.4–2.7).
4. When an entity first applies this Standard, it may choose as its accounting policy to continue to apply the hedge accounting requirements of IAS 39 instead of the requirements in Chapter 6 of this Standard. An entity shall apply that policy to all of its hedging relationships. An entity that chooses that policy shall also apply IFRIC 16 Hedges of a Net Investment in a Foreign Operation without the amendments that conform that Interpretation to the requirements in Chapter 6 of this Standard.

*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

OSTEOPORE LIMITED assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Pearson Correlation^1,2,3,4 and conclude that the OSX stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Sell OSX stock.

Financial State Forecast for OSX OSTEOPORE LIMITED Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba3
Operational Risk	32	41
Market Risk	39	32
Technical Analysis	76	72
Fundamental Analysis	37	88
Risk Unsystematic	56	82

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 728 signals.

References

1. Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
2. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Short/Long Term Stocks: FOX Stock Forecast. AC Investment Research Journal, 101(3).
4. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
5. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
6. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
7. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999

Frequently Asked Questions

Q: What is the prediction methodology for OSX stock?
A: OSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Pearson Correlation
Q: Is OSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell OSX Stock.
Q: Is OSTEOPORE LIMITED stock a good investment?
A: The consensus rating for OSTEOPORE LIMITED is Sell and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of OSX stock?
A: The consensus rating for OSX is Sell.
Q: What is the prediction period for OSX stock?
A: The prediction period for OSX is (n+16 weeks)

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