## Summary

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators.** We evaluate ROCKFIRE RESOURCES PLC prediction models with Inductive Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the LON:ROCK stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:ROCK stock.**

## Key Points

- Technical Analysis with Algorithmic Trading
- Can stock prices be predicted?
- What is a prediction confidence?

## LON:ROCK Target Price Prediction Modeling Methodology

We consider ROCKFIRE RESOURCES PLC Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of LON:ROCK 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(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(Inductive Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of LON:ROCK 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:ROCK Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:ROCK ROCKFIRE RESOURCES PLC

**Time series to forecast n: 20 Nov 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:ROCK 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 ROCKFIRE RESOURCES PLC

- An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.
- Adjusting the hedge ratio allows an entity to respond to changes in the relationship between the hedging instrument and the hedged item that arise from their underlyings or risk variables. For example, a hedging relationship in which the hedging instrument and the hedged item have different but related underlyings changes in response to a change in the relationship between those two underlyings (for example, different but related reference indices, rates or prices). Hence, rebalancing allows the continuation of a hedging relationship in situations in which the relationship between the hedging instrument and the hedged item chang
- When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
- If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.

*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

ROCKFIRE RESOURCES PLC assigned short-term Ba3 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Inductive Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the LON:ROCK stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell LON:ROCK stock.**

### Financial State Forecast for LON:ROCK ROCKFIRE RESOURCES PLC Stock Options & Futures

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

Outlook* | Ba3 | Ba2 |

Operational Risk | 88 | 88 |

Market Risk | 84 | 65 |

Technical Analysis | 78 | 70 |

Fundamental Analysis | 34 | 44 |

Risk Unsystematic | 40 | 69 |

### Prediction Confidence Score

## References

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## Frequently Asked Questions

Q: What is the prediction methodology for LON:ROCK stock?A: LON:ROCK stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Logistic Regression

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

A: The dominant strategy among neural network is to Sell LON:ROCK Stock.

Q: Is ROCKFIRE RESOURCES PLC stock a good investment?

A: The consensus rating for ROCKFIRE RESOURCES PLC is Sell and assigned short-term Ba3 & long-term Ba2 forecasted stock rating.

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

A: The consensus rating for LON:ROCK is Sell.

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

A: The prediction period for LON:ROCK is (n+8 weeks)