One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. ** We evaluate FIREANGEL SAFETY TECHNOLOGY GROUP PLC prediction models with Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the LON:FA. stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:FA. stock.**

**LON:FA., FIREANGEL SAFETY TECHNOLOGY GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trust metric by Neural Network
- Which neural network is best for prediction?
- Nash Equilibria

## LON:FA. Target Price Prediction Modeling Methodology

Stock market prediction is a crucial and challenging task due to its nonlinear, evolutionary, complex, and dynamic nature. Research on the stock market has been an important issue for researchers in recent years. Companies invest in trading the stock market. Predicting the stock market trend accurately will minimize the risk and bring a maximum amount of profit for all the stakeholders. During the last several years, a lot of studies have been done to predict stock market trends using Traditional, Machine learning and deep learning techniques. We consider FIREANGEL SAFETY TECHNOLOGY GROUP PLC Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of LON:FA. 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of LON:FA. stock

j:Nash equilibria

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:FA. Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:FA. FIREANGEL SAFETY TECHNOLOGY GROUP PLC

**Time series to forecast n: 21 Sep 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:FA. stock.**

**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 (Yellow to Green): *Technical Analysis%**

## Conclusions

FIREANGEL SAFETY TECHNOLOGY GROUP PLC assigned short-term Ba3 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the LON:FA. stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:FA. stock.**

### Financial State Forecast for LON:FA. Stock Options & Futures

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

Outlook* | Ba3 | Ba1 |

Operational Risk | 36 | 81 |

Market Risk | 89 | 82 |

Technical Analysis | 76 | 82 |

Fundamental Analysis | 57 | 64 |

Risk Unsystematic | 74 | 43 |

### Prediction Confidence Score

## References

- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:FA. stock?A: LON:FA. stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Sign-Rank Test

Q: Is LON:FA. stock a buy or sell?

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

Q: Is FIREANGEL SAFETY TECHNOLOGY GROUP PLC stock a good investment?

A: The consensus rating for FIREANGEL SAFETY TECHNOLOGY GROUP PLC is Sell and assigned short-term Ba3 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of LON:FA. stock?

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

Q: What is the prediction period for LON:FA. stock?

A: The prediction period for LON:FA. is (n+8 weeks)

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