**Outlook:**CORA GOLD LIMITED assigned short-term B1 & long-term Ba2 forecasted stock rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 06 Dec 2022**for (n+1 year)

**Methodology :**Modular Neural Network (CNN Layer)

## Abstract

This study aims to predict the direction of stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks.(Kadole, A., 2020. A Machine Learning Model for Stock Price Prediction using Neural Network.)** We evaluate CORA GOLD LIMITED prediction models with Modular Neural Network (CNN Layer) and Chi-Square ^{1,2,3,4} and conclude that the LON:CORA stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy LON:CORA stock.**

## Key Points

- How do you know when a stock will go up or down?
- How useful are statistical predictions?
- Probability Distribution

## LON:CORA Target Price Prediction Modeling Methodology

We consider CORA GOLD LIMITED Decision Process with Modular Neural Network (CNN Layer) where A is the set of discrete actions of LON:CORA 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(Modular Neural Network (CNN Layer)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:CORA stock

j:Nash equilibria (Neural Network)

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:CORA Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:CORA CORA GOLD LIMITED

**Time series to forecast n: 06 Dec 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy LON:CORA 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%**

## Adjusted IFRS* Prediction Methods for CORA GOLD LIMITED

- The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
- For the purpose of recognising foreign exchange gains and losses under IAS 21, a financial asset measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A is treated as a monetary item. Accordingly, such a financial asset is treated as an asset measured at amortised cost in the foreign currency. Exchange differences on the amortised cost are recognised in profit or loss and other changes in the carrying amount are recognised in accordance with paragraph 5.7.10.
- An entity shall assess whether contractual cash flows are solely payments of principal and interest on the principal amount outstanding for the currency in which the financial asset is denominated.
- Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

CORA GOLD LIMITED assigned short-term B1 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Chi-Square ^{1,2,3,4} and conclude that the LON:CORA stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy LON:CORA stock.**

### Financial State Forecast for LON:CORA CORA GOLD LIMITED Options & Futures

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

Outlook* | B1 | Ba2 |

Operational Risk | 37 | 49 |

Market Risk | 55 | 77 |

Technical Analysis | 70 | 83 |

Fundamental Analysis | 67 | 46 |

Risk Unsystematic | 83 | 78 |

### Prediction Confidence Score

## References

- Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
- Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
- Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
- Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:CORA stock?A: LON:CORA stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Chi-Square

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

A: The dominant strategy among neural network is to Buy LON:CORA Stock.

Q: Is CORA GOLD LIMITED stock a good investment?

A: The consensus rating for CORA GOLD LIMITED is Buy and assigned short-term B1 & long-term Ba2 forecasted stock rating.

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

A: The consensus rating for LON:CORA is Buy.

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

A: The prediction period for LON:CORA is (n+1 year)