Stock price prediction has always been a challenging task for the researchers in financial domain. While the Efficient Market Hypothesis claims that it is impossible to predict stock prices accurately, there are work in the literature that have demonstrated that stock price movements can be forecasted with a reasonable degree of accuracy, if appropriate variables are chosen and suitable predictive models are built using those variables. In this work, we present a robust and accurate framework of stock price prediction using statistical, machine learning and deep learning methods** We evaluate MULBERRY GROUP PLC prediction models with Modular Neural Network (Market Direction Analysis) and Multiple Regression ^{1,2,3,4} and conclude that the LON:MUL stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:MUL stock.**

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

*Keywords:*## Key Points

- How do you pick a stock?
- Trust metric by Neural Network
- How can neural networks improve predictions?

## LON:MUL Target Price Prediction Modeling Methodology

With the up-gradation of technology and exploration of new machine learning models, the stock market data analysis has gained attention as these models provide a platform for businessman and traders to choose more profitable stocks. As these data are in large volumes and highly complex so a need of more efficient machine learning model for daily predictions is always looked upon. We consider MULBERRY GROUP PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:MUL 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(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 Direction Analysis)) X S(n):→ (n+4 weeks) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of LON:MUL 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:MUL Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**LON:MUL MULBERRY GROUP PLC

**Time series to forecast n: 14 Sep 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:MUL 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

MULBERRY GROUP PLC assigned short-term Ba1 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Multiple Regression ^{1,2,3,4} and conclude that the LON:MUL stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold LON:MUL stock.**

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

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

Outlook* | Ba1 | B2 |

Operational Risk | 53 | 70 |

Market Risk | 59 | 52 |

Technical Analysis | 87 | 41 |

Fundamental Analysis | 74 | 38 |

Risk Unsystematic | 86 | 54 |

### Prediction Confidence Score

## References

- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
- S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.

## Frequently Asked Questions

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

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

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

Q: Is MULBERRY GROUP PLC stock a good investment?

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

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

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

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

A: The prediction period for LON:MUL is (n+4 weeks)

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