**Outlook:**WARPAINT LONDON PLC assigned short-term B2 & long-term Ba3 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 14 Dec 2022**for (n+1 year)

**Methodology :**Modular Neural Network (Social Media Sentiment Analysis)

## Abstract

The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications.(Vui, C.S., Soon, G.K., On, C.K., Alfred, R. and Anthony, P., 2013, November. A review of stock market prediction with Artificial neural network (ANN). In 2013 IEEE international conference on control system, computing and engineering (pp. 477-482). IEEE.)** We evaluate WARPAINT LONDON PLC prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression ^{1,2,3,4} and conclude that the LON:W7L stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

## Key Points

- What is the best way to predict stock prices?
- Dominated Move
- Can stock prices be predicted?

## LON:W7L Target Price Prediction Modeling Methodology

We consider WARPAINT LONDON PLC Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of LON:W7L 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(Ridge Regression)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:W7L 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:W7L Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:W7L WARPAINT LONDON PLC

**Time series to forecast n: 14 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

**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%**

## Adjusted IFRS* Prediction Methods for WARPAINT LONDON PLC

- The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
- Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
- An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
- If a call option right retained by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the asset continues to be measured at its fair value. The associated liability is measured at (i) the option exercise price less the time value of the option if the option is in or at the money, or (ii) the fair value of the transferred asset less the time value of the option if the option is out of the money. The adjustment to the measurement of the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the call option right. For example, if the fair value of the underlying asset is CU80, the option exercise price is CU95 and the time value of the option is CU5, the carrying amount of the associated liability is CU75 (CU80 – CU5) and the carrying amount of the transferred asset is CU80 (ie its fair value)

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

WARPAINT LONDON PLC assigned short-term B2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Ridge Regression ^{1,2,3,4} and conclude that the LON:W7L stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy**

### Financial State Forecast for LON:W7L WARPAINT LONDON PLC Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | Ba3 |

Operational Risk | 58 | 73 |

Market Risk | 74 | 60 |

Technical Analysis | 84 | 45 |

Fundamental Analysis | 32 | 67 |

Risk Unsystematic | 39 | 73 |

### Prediction Confidence Score

## References

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- L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
- D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:W7L stock?A: LON:W7L stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression

Q: Is LON:W7L stock a buy or sell?

A: The dominant strategy among neural network is to Buy LON:W7L Stock.

Q: Is WARPAINT LONDON PLC stock a good investment?

A: The consensus rating for WARPAINT LONDON PLC is Buy and assigned short-term B2 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of LON:W7L stock?

A: The consensus rating for LON:W7L is Buy.

Q: What is the prediction period for LON:W7L stock?

A: The prediction period for LON:W7L is (n+1 year)