With technological advancements, big data can be easily generated and collected in many applications. Embedded in these big data are useful information and knowledge that can be discovered by machine learning and data mining models, techniques or algorithms.** We evaluate DOMINO'S PIZZA GROUP PLC prediction models with Reinforcement Machine Learning (ML) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LON:DOM stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:DOM stock.**

**LON:DOM, DOMINO'S PIZZA GROUP PLC, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Should I buy stocks now or wait amid such uncertainty?
- What is prediction model?
- Can we predict stock market using machine learning?

## LON:DOM Target Price Prediction Modeling Methodology

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We consider DOMINO'S PIZZA GROUP PLC Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of LON:DOM 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(Wilcoxon Rank-Sum Test)

^{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(Reinforcement Machine Learning (ML)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of LON:DOM 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:DOM Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**LON:DOM DOMINO'S PIZZA GROUP PLC

**Time series to forecast n: 07 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:DOM 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

DOMINO'S PIZZA GROUP PLC assigned short-term B1 & long-term B2 forecasted stock rating.** We evaluate the prediction models Reinforcement Machine Learning (ML) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the LON:DOM stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold LON:DOM stock.**

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

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

Outlook* | B1 | B2 |

Operational Risk | 41 | 59 |

Market Risk | 69 | 82 |

Technical Analysis | 53 | 43 |

Fundamental Analysis | 75 | 35 |

Risk Unsystematic | 73 | 38 |

### Prediction Confidence Score

## References

- Dudik M, Langford J, Li L. 2011. Doubly robust policy evaluation and learning. In Proceedings of the 28th International Conference on Machine Learning, pp. 1097–104. La Jolla, CA: Int. Mach. Learn. Soc.
- Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
- Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
- C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
- Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67

## Frequently Asked Questions

Q: What is the prediction methodology for LON:DOM stock?A: LON:DOM stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Wilcoxon Rank-Sum Test

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

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

Q: Is DOMINO'S PIZZA GROUP PLC stock a good investment?

A: The consensus rating for DOMINO'S PIZZA GROUP PLC is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.

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

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

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

A: The prediction period for LON:DOM is (n+6 month)

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