PE1 PENGANA PRIVATE EQUITY TRUST

Outlook: PENGANA PRIVATE EQUITY TRUST is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 19 Mar 2023 for (n+1 year)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

Abstract

PENGANA PRIVATE EQUITY TRUST prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression^1,2,3,4 and it is concluded that the PE1 stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

Key Points

1. Is now good time to invest?
2. What are main components of Markov decision process?
3. What are the most successful trading algorithms?

PE1 Target Price Prediction Modeling Methodology

We consider PENGANA PRIVATE EQUITY TRUST Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of PE1 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(Ridge Regression)^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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of PE1 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?

PE1 Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: PE1 PENGANA PRIVATE EQUITY TRUST
Time series to forecast n: 19 Mar 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

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 PRIVATE EQUITY TRUST

1. An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
2. If, in applying paragraph 7.2.44, an entity reinstates a discontinued hedging relationship, the entity shall read references in paragraphs 6.9.11 and 6.9.12 to the date the alternative benchmark rate is designated as a noncontractually specified risk component for the first time as referring to the date of initial application of these amendments (ie the 24-month period for that alternative benchmark rate designated as a non-contractually specified risk component begins from the date of initial application of these amendments).
3. 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.
4. This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.

*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 PRIVATE EQUITY TRUST is assigned short-term Ba1 & long-term Ba1 estimated rating. PENGANA PRIVATE EQUITY TRUST prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression^1,2,3,4 and it is concluded that the PE1 stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy

PE1 PENGANA PRIVATE EQUITY TRUST Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	Caa2
Balance Sheet	Caa2	C
Leverage Ratios	Baa2	Ba3
Cash Flow	Baa2	Baa2
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: 91 out of 100 with 828 signals.

References

1. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
2. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
3. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
4. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
5. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
6. Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
7. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]

Frequently Asked Questions

Q: What is the prediction methodology for PE1 stock?
A: PE1 stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression
Q: Is PE1 stock a buy or sell?
A: The dominant strategy among neural network is to Buy PE1 Stock.
Q: Is PENGANA PRIVATE EQUITY TRUST stock a good investment?
A: The consensus rating for PENGANA PRIVATE EQUITY TRUST is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of PE1 stock?
A: The consensus rating for PE1 is Buy.
Q: What is the prediction period for PE1 stock?
A: The prediction period for PE1 is (n+1 year)

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