Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history.** We evaluate CLARKSON PLC prediction models with Supervised Machine Learning (ML) and Multiple Regression ^{1,2,3,4} and conclude that the LON:CKN stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:CKN stock.**

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

*Keywords:*## Key Points

- Why do we need predictive models?
- What is a prediction confidence?
- Trust metric by Neural Network

## LON:CKN Target Price Prediction Modeling Methodology

Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets. We consider CLARKSON PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:CKN 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(Supervised Machine Learning (ML)) X S(n):→ (n+16 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:CKN 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:CKN Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:CKN CLARKSON PLC

**Time series to forecast n: 13 Oct 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:CKN 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

CLARKSON PLC assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Multiple Regression ^{1,2,3,4} and conclude that the LON:CKN stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold LON:CKN stock.**

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

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 82 | 75 |

Market Risk | 55 | 31 |

Technical Analysis | 43 | 71 |

Fundamental Analysis | 69 | 80 |

Risk Unsystematic | 75 | 63 |

### Prediction Confidence Score

## References

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- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
- Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:CKN stock?A: LON:CKN stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Multiple Regression

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

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

Q: Is CLARKSON PLC stock a good investment?

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

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

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

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

A: The prediction period for LON:CKN is (n+16 weeks)

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