Modelling A.I. in Economics

PCGU Pacific Gas & Electric Co. Equity Unit

Outlook: Pacific Gas & Electric Co. Equity Unit is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 04 Apr 2023 for (n+6 month)
Methodology : Inductive Learning (ML)

Abstract

Pacific Gas & Electric Co. Equity Unit prediction model is evaluated with Inductive Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the PCGU stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

Key Points

  1. Market Signals
  2. What are the most successful trading algorithms?
  3. Can we predict stock market using machine learning?

PCGU Target Price Prediction Modeling Methodology

We consider Pacific Gas & Electric Co. Equity Unit Decision Process with Inductive Learning (ML) where A is the set of discrete actions of PCGU 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(Multiple Regression)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Inductive Learning (ML)) X S(n):→ (n+6 month) i = 1 n s i

n:Time series to forecast

p:Price signals of PCGU 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?

PCGU Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: PCGU Pacific Gas & Electric Co. Equity Unit
Time series to forecast n: 04 Apr 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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 Pacific Gas & Electric Co. Equity Unit

  1. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods only if it is possible to do so without the use of hindsight. If an entity restates prior periods, the restated financial statements must reflect all the requirements in this Standard for the affected financial instruments. 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 of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
  2. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
  3. IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
  4. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.

*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

Pacific Gas & Electric Co. Equity Unit is assigned short-term Ba1 & long-term Ba1 estimated rating. Pacific Gas & Electric Co. Equity Unit prediction model is evaluated with Inductive Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the PCGU stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Buy

PCGU Pacific Gas & Electric Co. Equity Unit Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Ba3
Balance SheetBaa2Baa2
Leverage RatiosCCaa2
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityBaa2C

*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: 90 out of 100 with 687 signals.

References

  1. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
  2. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
  3. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
  4. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
  5. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
  6. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Trading Signals (WTS Stock Forecast). AC Investment Research Journal, 101(3).
  7. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
Frequently Asked QuestionsQ: What is the prediction methodology for PCGU stock?
A: PCGU stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Multiple Regression
Q: Is PCGU stock a buy or sell?
A: The dominant strategy among neural network is to Buy PCGU Stock.
Q: Is Pacific Gas & Electric Co. Equity Unit stock a good investment?
A: The consensus rating for Pacific Gas & Electric Co. Equity Unit is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of PCGU stock?
A: The consensus rating for PCGU is Buy.
Q: What is the prediction period for PCGU stock?
A: The prediction period for PCGU is (n+6 month)

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