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 ESKEN LIMITED prediction models with Ensemble Learning (ML) and Independent T-Test ^{1,2,3,4} and conclude that the LON:ESKN 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:ESKN stock.**

**LON:ESKN, ESKEN LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Which neural network is best for prediction?
- Market Risk
- How can neural networks improve predictions?

## LON:ESKN Target Price Prediction Modeling Methodology

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We consider ESKEN LIMITED Stock Decision Process with Independent T-Test where A is the set of discrete actions of LON:ESKN 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(Independent T-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(Ensemble Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:ESKN 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:ESKN Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:ESKN ESKEN LIMITED

**Time series to forecast n: 16 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:ESKN 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

ESKEN LIMITED assigned short-term Ba1 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Ensemble Learning (ML) with Independent T-Test ^{1,2,3,4} and conclude that the LON:ESKN 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:ESKN stock.**

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

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

Outlook* | Ba1 | Ba3 |

Operational Risk | 83 | 68 |

Market Risk | 86 | 56 |

Technical Analysis | 69 | 40 |

Fundamental Analysis | 30 | 75 |

Risk Unsystematic | 84 | 74 |

### Prediction Confidence Score

## References

- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
- N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
- 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

## Frequently Asked Questions

Q: What is the prediction methodology for LON:ESKN stock?A: LON:ESKN stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Independent T-Test

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

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

Q: Is ESKEN LIMITED stock a good investment?

A: The consensus rating for ESKEN LIMITED is Sell and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.

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

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

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

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