LON:PHE Stock: A Cautionary Tale

Outlook: POWERHOUSE ENERGY GROUP PLC is assigned short-term Ba3 & long-term Ba2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Transfer Learning (ML)
Hypothesis Testing : Beta
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.

Summary

POWERHOUSE ENERGY GROUP PLC prediction model is evaluated with Transfer Learning (ML) and Beta^1,2,3,4 and it is concluded that the LON:PHE stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

Key Points

1. Reaction Function
2. Stock Forecast Based On a Predictive Algorithm
3. How do you decide buy or sell a stock?

LON:PHE Target Price Prediction Modeling Methodology

We consider POWERHOUSE ENERGY GROUP PLC Decision Process with Transfer Learning (ML) where A is the set of discrete actions of LON:PHE 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(Beta)^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(Transfer Learning (ML)) X S(n):→ 6 Month $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of LON:PHE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transfer Learning (ML)

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.

Beta

In statistics, beta (β) is a measure of the strength of the relationship between two variables. It is calculated as the slope of the line of best fit in a regression analysis. Beta can range from -1 to 1, with a value of 0 indicating no relationship between the two variables. A positive beta indicates that as one variable increases, the other variable also increases. A negative beta indicates that as one variable increases, the other variable decreases. For example, a study might find that there is a positive relationship between height and weight. This means that taller people tend to weigh more. The beta coefficient for this relationship would be positive.

 

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:PHE Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:PHE POWERHOUSE ENERGY GROUP PLC
Time series to forecast: 6 Month

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

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 Transfer Learning (ML) based LON:PHE Stock Prediction Model

1. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
2. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
3. A firm commitment to acquire a business in a business combination cannot be a hedged item, except for foreign currency risk, because the other risks being hedged cannot be specifically identified and measured. Those other risks are general business risks.
4. Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.

*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:PHE POWERHOUSE ENERGY GROUP PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	Ba2
Income Statement	Caa2	B2
Balance Sheet	B3	Ba1
Leverage Ratios	Ba3	Baa2
Cash Flow	Baa2	B3
Rates of Return and Profitability	Ba2	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

POWERHOUSE ENERGY GROUP PLC is assigned short-term Ba3 & long-term Ba2 estimated rating. POWERHOUSE ENERGY GROUP PLC prediction model is evaluated with Transfer Learning (ML) and Beta^1,2,3,4 and it is concluded that the LON:PHE stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 842 signals.

References

1. Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
2. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
3. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
4. Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
5. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
6. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
7. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.

Frequently Asked Questions

Q: What is the prediction methodology for LON:PHE stock?
A: LON:PHE stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Beta
Q: Is LON:PHE stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:PHE Stock.
Q: Is POWERHOUSE ENERGY GROUP PLC stock a good investment?
A: The consensus rating for POWERHOUSE ENERGY GROUP PLC is Sell and is assigned short-term Ba3 & long-term Ba2 estimated rating.
Q: What is the consensus rating of LON:PHE stock?
A: The consensus rating for LON:PHE is Sell.
Q: What is the prediction period for LON:PHE stock?
A: The prediction period for LON:PHE is 6 Month