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 BUNZL PLC prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression ^{1,2,3,4} and conclude that the LON:BNZL 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:BNZL stock.**

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

*Keywords:*## Key Points

- Market Risk
- Can neural networks predict stock market?
- Can stock prices be predicted?

## LON:BNZL Target Price Prediction Modeling Methodology

A speculator on a Stock Market, aside from having money to spare, needs at least one other thing — a means of producing accurate and understandable predictions ahead of others in the Market, so that a tactical and price advantage can be gained. This work demonstrates that it is possible to predict one such Market to a high degree of accuracy. We consider BUNZL PLC Stock Decision Process with Stepwise Regression where A is the set of discrete actions of LON:BNZL 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(Stepwise 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 (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:BNZL BUNZL PLC

**Time series to forecast n: 25 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:BNZL 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

BUNZL PLC assigned short-term B2 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Stepwise Regression ^{1,2,3,4} and conclude that the LON:BNZL 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:BNZL stock.**

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

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

Outlook* | B2 | Ba3 |

Operational Risk | 61 | 46 |

Market Risk | 75 | 48 |

Technical Analysis | 35 | 78 |

Fundamental Analysis | 55 | 84 |

Risk Unsystematic | 43 | 54 |

### Prediction Confidence Score

## References

- Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
- J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.

## Frequently Asked Questions

Q: What is the prediction methodology for LON:BNZL stock?A: LON:BNZL stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression

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

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

Q: Is BUNZL PLC stock a good investment?

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

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

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

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

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