In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements.** We evaluate PANTHEON RESOURCES PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Logistic Regression ^{1,2,3,4} and conclude that the LON:PANR 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:PANR stock.**

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

*Keywords:*## Key Points

- Short/Long Term Stocks
- Can neural networks predict stock market?
- What is statistical models in machine learning?

## LON:PANR Target Price Prediction Modeling Methodology

The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. We consider PANTHEON RESOURCES PLC Stock Decision Process with Logistic Regression where A is the set of discrete actions of LON:PANR 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(Logistic 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 (Market Volatility Analysis)) X S(n):→ (n+16 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:PANR PANTHEON RESOURCES PLC

**Time series to forecast n: 14 Sep 2022**for (n+16 weeks)

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

PANTHEON RESOURCES PLC assigned short-term Ba1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Logistic Regression ^{1,2,3,4} and conclude that the LON:PANR 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:PANR stock.**

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

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

Outlook* | Ba1 | B1 |

Operational Risk | 77 | 64 |

Market Risk | 75 | 71 |

Technical Analysis | 55 | 34 |

Fundamental Analysis | 75 | 77 |

Risk Unsystematic | 76 | 35 |

### Prediction Confidence Score

## References

- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
- Miller A. 2002. Subset Selection in Regression. New York: CRC Press
- 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:PANR stock?A: LON:PANR stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Logistic Regression

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

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

Q: Is PANTHEON RESOURCES PLC stock a good investment?

A: The consensus rating for PANTHEON RESOURCES PLC is Hold and assigned short-term Ba1 & long-term B1 forecasted stock rating.

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

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

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

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