LON:GRID GRESHAM HOUSE ENERGY STORAGE FUND PLC

Outlook: GRESHAM HOUSE ENERGY STORAGE FUND PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 12 Mar 2023 for (n+6 month)
Methodology : Reinforcement Machine Learning (ML)

Abstract

GRESHAM HOUSE ENERGY STORAGE FUND PLC prediction model is evaluated with Reinforcement Machine Learning (ML) and Linear Regression^1,2,3,4 and it is concluded that the LON:GRID stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

Key Points

1. Market Signals
2. How useful are statistical predictions?
3. What is neural prediction?

LON:GRID Target Price Prediction Modeling Methodology

We consider GRESHAM HOUSE ENERGY STORAGE FUND PLC Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of LON:GRID 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(Linear 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(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:GRID 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:GRID Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: LON:GRID GRESHAM HOUSE ENERGY STORAGE FUND PLC
Time series to forecast n: 12 Mar 2023 for (n+6 month)

According to price forecasts for (n+6 month) 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 GRESHAM HOUSE ENERGY STORAGE FUND PLC

1. Expected credit losses are a probability-weighted estimate of credit losses (ie the present value of all cash shortfalls) over the expected life of the financial instrument. A cash shortfall is the difference between the cash flows that are due to an entity in accordance with the contract and the cash flows that the entity expects to receive. Because expected credit losses consider the amount and timing of payments, a credit loss arises even if the entity expects to be paid in full but later than when contractually due.
2. For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
3. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
4. An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.

*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

GRESHAM HOUSE ENERGY STORAGE FUND PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. GRESHAM HOUSE ENERGY STORAGE FUND PLC prediction model is evaluated with Reinforcement Machine Learning (ML) and Linear Regression^1,2,3,4 and it is concluded that the LON:GRID stock is predictable in the short/long term. According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell

LON:GRID GRESHAM HOUSE ENERGY STORAGE FUND PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	B3
Balance Sheet	Ba3	Baa2
Leverage Ratios	Caa2	Baa2
Cash Flow	C	Caa2
Rates of Return and Profitability	Baa2	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

Trust metric by Neural Network: 84 out of 100 with 785 signals.

References

1. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
2. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can neural networks predict stock market?(ATVI Stock Forecast). AC Investment Research Journal, 101(3).
4. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can neural networks predict stock market?(ATVI Stock Forecast). AC Investment Research Journal, 101(3).
5. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
6. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
7. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.

Frequently Asked Questions

Q: What is the prediction methodology for LON:GRID stock?
A: LON:GRID stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Linear Regression
Q: Is LON:GRID stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:GRID Stock.
Q: Is GRESHAM HOUSE ENERGY STORAGE FUND PLC stock a good investment?
A: The consensus rating for GRESHAM HOUSE ENERGY STORAGE FUND PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:GRID stock?
A: The consensus rating for LON:GRID is Sell.
Q: What is the prediction period for LON:GRID stock?
A: The prediction period for LON:GRID is (n+6 month)

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