Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS).** We evaluate ENQUEST PLC prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression ^{1,2,3,4} and conclude that the LON:ENQ 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:ENQ stock.**

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

*Keywords:*## Key Points

- How do predictive algorithms actually work?
- Can stock prices be predicted?
- What are the most successful trading algorithms?

## LON:ENQ Target Price Prediction Modeling Methodology

The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. We consider ENQUEST PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:ENQ 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(Multiple 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 (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:ENQ ENQUEST PLC

**Time series to forecast n: 13 Sep 2022**for (n+4 weeks)

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

ENQUEST PLC assigned short-term Baa2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Multiple Regression ^{1,2,3,4} and conclude that the LON:ENQ 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:ENQ stock.**

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

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

Outlook* | Baa2 | Ba3 |

Operational Risk | 73 | 86 |

Market Risk | 82 | 34 |

Technical Analysis | 76 | 74 |

Fundamental Analysis | 85 | 78 |

Risk Unsystematic | 48 | 60 |

### Prediction Confidence Score

## References

- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
- E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013

## Frequently Asked Questions

Q: What is the prediction methodology for LON:ENQ stock?A: LON:ENQ stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression

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

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

Q: Is ENQUEST PLC stock a good investment?

A: The consensus rating for ENQUEST PLC is Hold and assigned short-term Baa2 & long-term Ba3 forecasted stock rating.

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

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

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

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