Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend.** We evaluate FERGUSON PLC prediction models with Modular Neural Network (Market Volatility Analysis) and Multiple Regression ^{1,2,3,4} and conclude that the LON:FERG 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 Sell LON:FERG stock.**

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

*Keywords:*## Key Points

- What are the most successful trading algorithms?
- Why do we need predictive models?
- Can machine learning predict?

## LON:FERG Target Price Prediction Modeling Methodology

As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system. We consider FERGUSON PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:FERG 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(Multiple 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+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of LON:FERG 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:FERG Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**LON:FERG FERGUSON PLC

**Time series to forecast n: 15 Sep 2022**for (n+1 year)

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

FERGUSON PLC assigned short-term Ba2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Multiple Regression ^{1,2,3,4} and conclude that the LON:FERG 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 Sell LON:FERG stock.**

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

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

Outlook* | Ba2 | B1 |

Operational Risk | 57 | 84 |

Market Risk | 60 | 49 |

Technical Analysis | 76 | 65 |

Fundamental Analysis | 84 | 39 |

Risk Unsystematic | 61 | 55 |

### Prediction Confidence Score

## References

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- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- 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
- 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
- CandÃ¨s E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015

## Frequently Asked Questions

Q: What is the prediction methodology for LON:FERG stock?A: LON:FERG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Multiple Regression

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

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

Q: Is FERGUSON PLC stock a good investment?

A: The consensus rating for FERGUSON PLC is Sell and assigned short-term Ba2 & long-term B1 forecasted stock rating.

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

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

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

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