LON:WSBN Stock Forecast: A Hold For The Next 6 Month

Jorgenson, Dale W.; Weitzman, Martin L.; ZXhang, Yoshua X; Haxo, Yann M; Mat , Ying X

Outlook: WISHBONE GOLD PLC is assigned short-term B2 & 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 : Inductive Learning (ML)
Hypothesis Testing : Spearman Correlation
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

WISHBONE GOLD PLC prediction model is evaluated with Inductive Learning (ML) and Spearman Correlation^1,2,3,4 and it is concluded that the LON:WSBN stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ According to price forecasts for 6 Month period, the dominant strategy among neural network is: Hold

Key Points

Inductive Learning (ML) for LON:WSBN stock price prediction process.
Spearman Correlation
What are buy sell or hold recommendations?
How can neural networks improve predictions?
Is now good time to invest?

LON:WSBN Stock Price Forecast

We consider WISHBONE GOLD PLC Decision Process with Inductive Learning (ML) where A is the set of discrete actions of LON:WSBN 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

Sample Set: Neural Network
Stock/Index: LON:WSBN WISHBONE GOLD PLC
Time series to forecast: 6 Month

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

F(Spearman Correlation)^6,7=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Inductive Learning (ML)) X S(n):→ 6 Month

\begin{array}{l} \int e^{x} rx \end{array}

n:Time series to forecast

p:Price signals of LON:WSBN stock

j:Nash equilibria (Neural Network)

k:Dominated move of LON:WSBN stock holders

a:Best response for LON:WSBN target price

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ Spearman correlation is a nonparametric measure of the strength and direction of association between two variables. It is a rank-based correlation, which means that it does not assume that the data is normally distributed. Spearman correlation is calculated by first ranking the data for each variable, and then calculating the Pearson correlation between the ranks.^6,7

For further technical information as per how our model work we invite you to visit the article below:

How do Predictive A.I. algorithms actually work?

LON:WSBN Stock Forecast (Buy or Sell) Strategic Interaction Table

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 Inductive Learning (ML) based LON:WSBN Stock Prediction Model

Adjusting the hedge ratio by decreasing the volume of the hedged item does not affect how the changes in the fair value of the hedging instrument are measured. The measurement of the changes in the value of the hedged item related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedged item was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged a volume of 100 tonnes of a commodity at a forward price of CU80 and reduces that volume by 10 tonnes on rebalancing, the hedged item after rebalancing would be 90 tonnes hedged at CU80. The 10 tonnes of the hedged item that are no longer part of the hedging relationship would be accounted for in accordance with the requirements for the discontinuation of hedge accounting (see paragraphs 6.5.6–6.5.7 and B6.5.22–B6.5.28).
Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
In applying the effective interest method, an entity identifies fees that are an integral part of the effective interest rate of a financial instrument. The description of fees for financial services may not be indicative of the nature and substance of the services provided. Fees that are an integral part of the effective interest rate of a financial instrument are treated as an adjustment to the effective interest rate, unless the financial instrument is measured at fair value, with the change in fair value being recognised in profit or loss. In those cases, the fees are recognised as revenue or expense when the instrument is initially recognised.
For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.

*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:WSBN WISHBONE GOLD PLC Financial Analysis*

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

References

M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675

Frequently Asked Questions

Q: What is the prediction methodology for LON:WSBN stock?
A: LON:WSBN stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Spearman Correlation
Q: Is LON:WSBN stock a buy or sell?
A: The dominant strategy among neural network is to Hold LON:WSBN Stock.
Q: Is WISHBONE GOLD PLC stock a good investment?
A: The consensus rating for WISHBONE GOLD PLC is Hold and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:WSBN stock?
A: The consensus rating for LON:WSBN is Hold.
Q: What is the prediction period for LON:WSBN stock?
A: The prediction period for LON:WSBN is 6 Month

What did you think about the prediction? (Insufficient-Outstanding)

Tell us how we can improve PredictiveAI