Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. **We evaluate MACFARLANE GROUP PLC prediction models with Modular Neural Network (Financial Sentiment Analysis) and Multiple Regression ^{1,2,3,4} and conclude that the LON:MACF 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 Hold LON:MACF stock.**

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

*Keywords:*## Key Points

- Short/Long Term Stocks
- How useful are statistical predictions?
- What statistical methods are used to analyze data?

## LON:MACF Target Price Prediction Modeling Methodology

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock's price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns.We consider MACFARLANE GROUP PLC Stock Decision Process with Multiple Regression where A is the set of discrete actions of LON:MACF 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 (Financial Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**LON:MACF MACFARLANE GROUP PLC

**Time series to forecast n: 09 Sep 2022**for (n+1 year)

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

MACFARLANE GROUP PLC assigned short-term Ba2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Multiple Regression ^{1,2,3,4} and conclude that the LON:MACF 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 Hold LON:MACF stock.**

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

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

Outlook* | Ba2 | B1 |

Operational Risk | 70 | 90 |

Market Risk | 76 | 30 |

Technical Analysis | 90 | 72 |

Fundamental Analysis | 40 | 64 |

Risk Unsystematic | 66 | 46 |

### Prediction Confidence Score

## References

- Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
- T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.

## Frequently Asked Questions

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

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

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

Q: Is MACFARLANE GROUP PLC stock a good investment?

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

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

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

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

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