In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators.** We evaluate QUIZ PLC prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test ^{1,2,3,4} and conclude that the LON:QUIZ 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:QUIZ stock.**

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

*Keywords:*## Key Points

- Dominated Move
- What are the most successful trading algorithms?
- Reaction Function

## LON:QUIZ Target Price Prediction Modeling Methodology

Different machine learning algorithms are discussed in this literature review. These algorithms can be used for predicting the stock market. The prediction of the stock market is one of the challenging tasks that must have to be handled. In this paper, it is discussed how the machine learning algorithms can be used for predicting the stock value. We consider QUIZ PLC Stock Decision Process with Sign Test where A is the set of discrete actions of LON:QUIZ 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(Sign 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+6 month) $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:QUIZ QUIZ PLC

**Time series to forecast n: 18 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:QUIZ 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

QUIZ PLC assigned short-term B2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Sign Test ^{1,2,3,4} and conclude that the LON:QUIZ 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:QUIZ stock.**

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

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

Outlook* | B2 | Ba3 |

Operational Risk | 57 | 79 |

Market Risk | 82 | 45 |

Technical Analysis | 47 | 46 |

Fundamental Analysis | 55 | 75 |

Risk Unsystematic | 31 | 78 |

### Prediction Confidence Score

## References

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- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
- G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
- Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier

## Frequently Asked Questions

Q: What is the prediction methodology for LON:QUIZ stock?A: LON:QUIZ stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Sign Test

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

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

Q: Is QUIZ PLC stock a good investment?

A: The consensus rating for QUIZ PLC is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.

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

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

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

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