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 CENTAMIN PLC prediction models with Modular Neural Network (DNN Layer) and Sign Test ^{1,2,3,4} and conclude that the LON:CEY 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:CEY stock.**

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

*Keywords:*## Key Points

- What is Markov decision process in reinforcement learning?
- Is Target price a good indicator?
- Operational Risk

## LON:CEY Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We consider CENTAMIN PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:CEY 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Sign 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(Modular Neural Network (DNN Layer)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:CEY CENTAMIN PLC

**Time series to forecast n: 14 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:CEY 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

CENTAMIN PLC assigned short-term Caa2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Sign Test ^{1,2,3,4} and conclude that the LON:CEY 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:CEY stock.**

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

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

Outlook* | Caa2 | B2 |

Operational Risk | 37 | 37 |

Market Risk | 59 | 35 |

Technical Analysis | 41 | 87 |

Fundamental Analysis | 51 | 45 |

Risk Unsystematic | 41 | 46 |

### Prediction Confidence Score

## References

- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
- Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- 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.
- 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
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:CEY stock?A: LON:CEY stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Sign Test

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

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

Q: Is CENTAMIN PLC stock a good investment?

A: The consensus rating for CENTAMIN PLC is Hold and assigned short-term Caa2 & long-term B2 forecasted stock rating.

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

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

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

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