**Outlook:**PENGANA INTERNATIONAL EQUITIES LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 12 Jan 2023**for (n+16 weeks)

**Methodology :**Modular Neural Network (Market Volatility Analysis)

## Abstract

PENGANA INTERNATIONAL EQUITIES LIMITED prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and ElasticNet Regression^{1,2,3,4}and it is concluded that the PIA stock is predictable in the short/long term.

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell**

## Key Points

- What is the use of Markov decision process?
- Reaction Function
- What is Markov decision process in reinforcement learning?

## PIA Target Price Prediction Modeling Methodology

We consider PENGANA INTERNATIONAL EQUITIES LIMITED Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of PIA 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(ElasticNet Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of PIA 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?

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

**Sample Set:**Neural Network

**Stock/Index:**PIA PENGANA INTERNATIONAL EQUITIES LIMITED

**Time series to forecast n: 12 Jan 2023**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell**

**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 PENGANA INTERNATIONAL EQUITIES LIMITED

- In the reporting period that includes the date of initial application of these amendments, an entity is not required to present the quantitative information required by paragraph 28(f) of IAS 8.
- Compared to a business model whose objective is to hold financial assets to collect contractual cash flows, this business model will typically involve greater frequency and value of sales. This is because selling financial assets is integral to achieving the business model's objective instead of being only incidental to it. However, there is no threshold for the frequency or value of sales that must occur in this business model because both collecting contractual cash flows and selling financial assets are integral to achieving its objective.
- Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income subsequent changes in the fair value of particular investments in equity instruments. Such an investment is not a monetary item. Accordingly, the gain or loss that is presented in other comprehensive income in accordance with paragraph 5.7.5 includes any related foreign exchange component.
- An entity can rebut this presumption. However, it can do so only when it has reasonable and supportable information available that demonstrates that even if contractual payments become more than 30 days past due, this does not represent a significant increase in the credit risk of a financial instrument. For example when non-payment was an administrative oversight, instead of resulting from financial difficulty of the borrower, or the entity has access to historical evidence that demonstrates that there is no correlation between significant increases in the risk of a default occurring and financial assets on which payments are more than 30 days past due, but that evidence does identify such a correlation when payments are more than 60 days past due.

*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

PENGANA INTERNATIONAL EQUITIES LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. PENGANA INTERNATIONAL EQUITIES LIMITED prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and ElasticNet Regression^{1,2,3,4} and it is concluded that the PIA stock is predictable in the short/long term. ** According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Sell**

### PIA PENGANA INTERNATIONAL EQUITIES LIMITED Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba1 | Ba1 |

Income Statement | Baa2 | C |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Caa2 | B3 |

Cash Flow | B2 | C |

Rates of Return and Profitability | Ba3 | 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

## References

- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]

## Frequently Asked Questions

Q: What is the prediction methodology for PIA stock?A: PIA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and ElasticNet Regression

Q: Is PIA stock a buy or sell?

A: The dominant strategy among neural network is to Sell PIA Stock.

Q: Is PENGANA INTERNATIONAL EQUITIES LIMITED stock a good investment?

A: The consensus rating for PENGANA INTERNATIONAL EQUITIES LIMITED is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of PIA stock?

A: The consensus rating for PIA is Sell.

Q: What is the prediction period for PIA stock?

A: The prediction period for PIA is (n+16 weeks)