LON:AEET AQUILA ENERGY EFFICIENCY TRUST PLC

Outlook: AQUILA ENERGY EFFICIENCY TRUST PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 17 Mar 2023 for (n+1 year)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

AQUILA ENERGY EFFICIENCY TRUST PLC prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression^1,2,3,4 and it is concluded that the LON:AEET 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. Can stock prices be predicted?
2. Nash Equilibria
3. What is statistical models in machine learning?

LON:AEET Target Price Prediction Modeling Methodology

We consider AQUILA ENERGY EFFICIENCY TRUST PLC Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of LON:AEET 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(Lasso 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 (Financial Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:AEET 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?

LON:AEET Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LON:AEET AQUILA ENERGY EFFICIENCY TRUST PLC
Time series to forecast n: 17 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 AQUILA ENERGY EFFICIENCY TRUST PLC

1. When measuring a loss allowance for a lease receivable, the cash flows used for determining the expected credit losses should be consistent with the cash flows used in measuring the lease receivable in accordance with IFRS 16 Leases.
2. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
3. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
4. 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.

*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

AQUILA ENERGY EFFICIENCY TRUST PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. AQUILA ENERGY EFFICIENCY TRUST PLC prediction model is evaluated with Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression^1,2,3,4 and it is concluded that the LON:AEET 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

LON:AEET AQUILA ENERGY EFFICIENCY TRUST PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B2	Baa2
Balance Sheet	Baa2	B1
Leverage Ratios	B1	Caa2
Cash Flow	Baa2	C
Rates of Return and Profitability	B2	Caa2

*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: 87 out of 100 with 825 signals.

References

1. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
2. S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
3. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
4. 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.
5. S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
6. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
7. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503

Frequently Asked Questions

Q: What is the prediction methodology for LON:AEET stock?
A: LON:AEET stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Lasso Regression
Q: Is LON:AEET stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:AEET Stock.
Q: Is AQUILA ENERGY EFFICIENCY TRUST PLC stock a good investment?
A: The consensus rating for AQUILA ENERGY EFFICIENCY TRUST PLC is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:AEET stock?
A: The consensus rating for LON:AEET is Buy.
Q: What is the prediction period for LON:AEET stock?
A: The prediction period for LON:AEET is (n+1 year)

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