We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. ** We evaluate FRENKEL TOPPING GROUP PLC prediction models with Statistical Inference (ML) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LON:FEN stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:FEN stock.**

**LON:FEN, FRENKEL TOPPING GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Technical Analysis with Algorithmic Trading
- How can neural networks improve predictions?
- Stock Forecast Based On a Predictive Algorithm

## LON:FEN Target Price Prediction Modeling Methodology

Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. We consider FRENKEL TOPPING GROUP PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:FEN 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 Rank-Sum 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(Statistical Inference (ML)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:FEN 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:FEN Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:FEN FRENKEL TOPPING GROUP PLC

**Time series to forecast n: 11 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:FEN 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

FRENKEL TOPPING GROUP PLC assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LON:FEN stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:FEN stock.**

### Financial State Forecast for LON:FEN Stock Options & Futures

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

Outlook* | B2 | B1 |

Operational Risk | 59 | 65 |

Market Risk | 59 | 45 |

Technical Analysis | 36 | 70 |

Fundamental Analysis | 86 | 33 |

Risk Unsystematic | 35 | 73 |

### Prediction Confidence Score

## References

- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- 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
- Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
- Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
- O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:FEN stock?A: LON:FEN stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Wilcoxon Rank-Sum Test

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

A: The dominant strategy among neural network is to Hold LON:FEN Stock.

Q: Is FRENKEL TOPPING GROUP PLC stock a good investment?

A: The consensus rating for FRENKEL TOPPING GROUP PLC is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.

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

A: The consensus rating for LON:FEN is Hold.

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

A: The prediction period for LON:FEN is (n+4 weeks)

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