Machine Learning refers to a concept in which a machine has been programmed to learn specific patterns from historical data using powerful algorithms and make predictions in future based on the patterns it learnt. Machine learning is a branch of Artificial Intelligence (AI), the term proposed in 1959 by Arthur Samuel who defined it as the ability of computers or machines to learn new rules and concepts from data without being explicitly programmed.** We evaluate ESKEN LIMITED prediction models with Ensemble Learning (ML) and Chi-Square ^{1,2,3,4} and conclude that the LON:ESKN stock is predictable in the short/long term. **

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

- Probability Distribution
- Probability Distribution
- Technical Analysis with Algorithmic Trading

## LON:ESKN Target Price Prediction Modeling Methodology

This paper surveys machine learning techniques for stock market prediction. The prediction of stock markets is regarded as a challenging task of financial time series prediction. We consider ESKEN LIMITED Stock Decision Process with Chi-Square 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(Chi-Square)

^{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+6 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

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+6 month)

**Sample Set:**Neural Network

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

**Time series to forecast n: 23 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) 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 Ba2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Ensemble Learning (ML) with Chi-Square ^{1,2,3,4} and conclude that the LON:ESKN stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) 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* | Ba2 | Ba3 |

Operational Risk | 89 | 70 |

Market Risk | 65 | 55 |

Technical Analysis | 67 | 81 |

Fundamental Analysis | 79 | 49 |

Risk Unsystematic | 41 | 57 |

### Prediction Confidence Score

## References

- Athey S, Mobius MM, Pál J. 2017c. The impact of aggregators on internet news consumption. Unpublished manuscript, Grad. School Bus., Stanford Univ., Stanford, CA
- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
- Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov

## 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 Chi-Square

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 Ba2 & 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+6 month)