Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction.** We evaluate ALBA MINERAL RESOURCES PLC prediction models with Transfer Learning (ML) and Pearson Correlation ^{1,2,3,4} and conclude that the LON:ALBA stock is predictable in the short/long term. **

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:ALBA stock.**

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

*Keywords:*## Key Points

- Reaction Function
- How do predictive algorithms actually work?
- Trust metric by Neural Network

## LON:ALBA 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 ALBA MINERAL RESOURCES PLC Stock Decision Process with Pearson Correlation where A is the set of discrete actions of LON:ALBA 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(Pearson Correlation)

^{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(Transfer Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:ALBA 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:ALBA Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:ALBA ALBA MINERAL RESOURCES PLC

**Time series to forecast n: 21 Sep 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:ALBA 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

ALBA MINERAL RESOURCES PLC assigned short-term B2 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Pearson Correlation ^{1,2,3,4} and conclude that the LON:ALBA stock is predictable in the short/long term.**

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold LON:ALBA stock.**

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

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

Outlook* | B2 | Ba1 |

Operational Risk | 58 | 79 |

Market Risk | 79 | 61 |

Technical Analysis | 36 | 81 |

Fundamental Analysis | 61 | 65 |

Risk Unsystematic | 47 | 63 |

### Prediction Confidence Score

## References

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- Byron, R. P. O. Ashenfelter (1995), "Predicting the quality of an unborn grange," Economic Record, 71, 40–53.
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
- Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
- Batchelor, R. P. Dua (1993), "Survey vs ARCH measures of inflation uncertainty," Oxford Bulletin of Economics Statistics, 55, 341–353.
- E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004

## Frequently Asked Questions

Q: What is the prediction methodology for LON:ALBA stock?A: LON:ALBA stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Pearson Correlation

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

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

Q: Is ALBA MINERAL RESOURCES PLC stock a good investment?

A: The consensus rating for ALBA MINERAL RESOURCES PLC is Hold and assigned short-term B2 & long-term Ba1 forecasted stock rating.

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

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

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

A: The prediction period for LON:ALBA is (n+3 month)