**Outlook:**SSP GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 20 Jan 2023**for (n+6 month)

**Methodology :**Modular Neural Network (Market Volatility Analysis)

## Abstract

SSP GROUP PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Lasso Regression^{1,2,3,4}and it is concluded that the LON:SSPG stock is predictable in the short/long term.

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

## Key Points

- Is now good time to invest?
- Reaction Function
- What statistical methods are used to analyze data?

## LON:SSPG Target Price Prediction Modeling Methodology

We consider SSP GROUP PLC Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of LON:SSPG 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(Lasso 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 (Market Volatility Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:SSPG 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:SSPG Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:SSPG SSP GROUP PLC

**Time series to forecast n: 20 Jan 2023**for (n+6 month)

**According to price forecasts for (n+6 month) 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 SSP GROUP PLC

- For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
- Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income subsequent changes in the fair value of particular investments in equity instruments. Such an investment is not a monetary item. Accordingly, the gain or loss that is presented in other comprehensive income in accordance with paragraph 5.7.5 includes any related foreign exchange component.
- An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
- When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.

*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

SSP GROUP PLC is assigned short-term Ba1 & long-term Ba1 estimated rating. SSP GROUP PLC prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Lasso Regression^{1,2,3,4} and it is concluded that the LON:SSPG stock is predictable in the short/long term. ** According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

### LON:SSPG SSP GROUP PLC Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | B3 | C |

Balance Sheet | B3 | Baa2 |

Leverage Ratios | Ba1 | B2 |

Cash Flow | B2 | B3 |

Rates of Return and Profitability | Caa2 | B2 |

*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

## References

- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
- Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]
- Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
- M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000

## Frequently Asked Questions

Q: What is the prediction methodology for LON:SSPG stock?A: LON:SSPG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Lasso Regression

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

A: The dominant strategy among neural network is to Sell LON:SSPG Stock.

Q: Is SSP GROUP PLC stock a good investment?

A: The consensus rating for SSP GROUP PLC is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

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

A: The consensus rating for LON:SSPG is Sell.

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

A: The prediction period for LON:SSPG is (n+6 month)

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