LON:ACPE Stock: A Cautionary Tale

Outlook: ACP ENERGY PLC is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Ensemble Learning (ML)
Hypothesis Testing : Chi-Square
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

Abstract

ACP ENERGY PLC prediction model is evaluated with Ensemble Learning (ML) and Chi-Square^1,2,3,4 and it is concluded that the LON:ACPE stock is predictable in the short/long term. Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Key Points

1. Is now good time to invest?
2. Should I buy stocks now or wait amid such uncertainty?
3. How can neural networks improve predictions?

LON:ACPE Target Price Prediction Modeling Methodology

We consider ACP ENERGY PLC Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LON:ACPE 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(Chi-Square)^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(Ensemble Learning (ML)) X S(n):→ 6 Month $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of LON:ACPE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Ensemble Learning (ML)

Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average.

Chi-Square

A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.

 

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:ACPE Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:ACPE ACP ENERGY PLC
Time series to forecast: 6 Month

According to price forecasts, the dominant strategy among neural network is: Hold

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Ensemble Learning (ML) based LON:ACPE Stock Prediction Model

1. 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.
2. A layer component that includes a prepayment option is not eligible to be designated as a hedged item in a fair value hedge if the prepayment option's fair value is affected by changes in the hedged risk, unless the designated layer includes the effect of the related prepayment option when determining the change in the fair value of the hedged item.
3. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.
4. For the avoidance of doubt, the effects of replacing the original counterparty with a clearing counterparty and making the associated changes as described in paragraph 6.5.6 shall be reflected in the measurement of the hedging instrument and therefore in the assessment of hedge effectiveness and the measurement of hedge effectiveness

*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.

LON:ACPE ACP ENERGY PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Income Statement	Ba3	C
Balance Sheet	Baa2	Baa2
Leverage Ratios	Baa2	C
Cash Flow	B3	Baa2
Rates of Return and Profitability	B3	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?

Conclusions

ACP ENERGY PLC is assigned short-term Ba3 & long-term B1 estimated rating. ACP ENERGY PLC prediction model is evaluated with Ensemble Learning (ML) and Chi-Square^1,2,3,4 and it is concluded that the LON:ACPE stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Prediction Confidence Score

Trust metric by Neural Network: 85 out of 100 with 860 signals.

References

1. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
2. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
3. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
4. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
5. Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
6. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
7. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.

Frequently Asked Questions

Q: What is the prediction methodology for LON:ACPE stock?
A: LON:ACPE stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Chi-Square
Q: Is LON:ACPE stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:ACPE Stock.
Q: Is ACP ENERGY PLC stock a good investment?
A: The consensus rating for ACP ENERGY PLC is Hold and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:ACPE stock?
A: The consensus rating for LON:ACPE is Hold.
Q: What is the prediction period for LON:ACPE stock?
A: The prediction period for LON:ACPE is 6 Month

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