Is LON:BUMP Stock Expected to Go Up?

Outlook: SERAPHINE GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 15 Jun 2023 for 4 Weeks
Methodology : Modular Neural Network (Market Volatility Analysis)

Abstract

SERAPHINE GROUP PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the LON:BUMP stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. Buy, Sell and Hold Signals
2. Technical Analysis with Algorithmic Trading
3. Game Theory

LON:BUMP Target Price Prediction Modeling Methodology

We consider SERAPHINE GROUP PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:BUMP 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(Wilcoxon Sign-Rank Test)^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 (Market Volatility Analysis)) X S(n):→ 4 Weeks $\sum _{i=1}^n\left(r_i\right)$

n:Time series to forecast

p:Price signals of LON:BUMP stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Market Volatility Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.

Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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:BUMP Stock Forecast (Buy or Sell) for 4 Weeks

Sample Set: Neural Network
Stock/Index: LON:BUMP SERAPHINE GROUP PLC
Time series to forecast n: 15 Jun 2023 for 4 Weeks

According to price forecasts for 4 Weeks 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 SERAPHINE GROUP PLC

1. At the date of initial application, an entity shall assess whether a financial asset meets the condition in paragraphs 4.1.2(a) or 4.1.2A(a) on the basis of the facts and circumstances that exist at that date. The resulting classification shall be applied retrospectively irrespective of the entity's business model in prior reporting periods.
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 regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
4. If a guarantee provided by an entity to pay for default losses on a transferred asset prevents the transferred asset from being derecognised to the extent of the continuing involvement, the transferred asset at the date of the transfer is measured at the lower of (i) the carrying amount of the asset and (ii) the maximum amount of the consideration received in the transfer that the entity could be required to repay ('the guarantee amount'). The associated liability is initially measured at the guarantee amount plus the fair value of the guarantee (which is normally the consideration received for the guarantee). Subsequently, the initial fair value of the guarantee is recognised in profit or loss when (or as) the obligation is satisfied (in accordance with the principles of IFRS 15) and the carrying value of the asset is reduced by any loss allowance.

*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

SERAPHINE GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. SERAPHINE GROUP PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the LON:BUMP stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

LON:BUMP SERAPHINE GROUP PLC Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	C
Balance Sheet	Caa2	Baa2
Leverage Ratios	Baa2	C
Cash Flow	C	C
Rates of Return and Profitability	Caa2	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: 90 out of 100 with 657 signals.

References

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2. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
3. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37
4. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
5. Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
6. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is FFBC Stock Buy or Sell?(Stock Forecast). AC Investment Research Journal, 101(3).
7. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell

Frequently Asked Questions

Q: What is the prediction methodology for LON:BUMP stock?
A: LON:BUMP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Wilcoxon Sign-Rank Test
Q: Is LON:BUMP stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:BUMP Stock.
Q: Is SERAPHINE GROUP PLC stock a good investment?
A: The consensus rating for SERAPHINE GROUP PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LON:BUMP stock?
A: The consensus rating for LON:BUMP is Sell.
Q: What is the prediction period for LON:BUMP stock?
A: The prediction period for LON:BUMP is 4 Weeks