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 evaluate M&G prediction models with Supervised Machine Learning (ML) and ElasticNet Regression ^{1,2,3,4} and conclude that the MNG 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 MNG stock.**

**MNG, M&G, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can stock prices be predicted?
- Is now good time to invest?
- What is a prediction confidence?

## MNG Target Price Prediction Modeling Methodology

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. We consider M&G Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of MNG 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(ElasticNet 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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of MNG 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?

## MNG Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**MNG M&G

**Time series to forecast n: 17 Sep 2022**for (n+1 year)

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

M&G assigned short-term Ba2 & long-term B3 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with ElasticNet Regression ^{1,2,3,4} and conclude that the MNG 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 MNG stock.**

### Financial State Forecast for MNG Stock Options & Futures

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

Outlook* | Ba2 | B3 |

Operational Risk | 33 | 69 |

Market Risk | 85 | 35 |

Technical Analysis | 89 | 49 |

Fundamental Analysis | 58 | 31 |

Risk Unsystematic | 81 | 45 |

### Prediction Confidence Score

## References

- E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
- M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
- Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
- L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016

## Frequently Asked Questions

Q: What is the prediction methodology for MNG stock?A: MNG stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and ElasticNet Regression

Q: Is MNG stock a buy or sell?

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

Q: Is M&G stock a good investment?

A: The consensus rating for M&G is Hold and assigned short-term Ba2 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of MNG stock?

A: The consensus rating for MNG is Hold.

Q: What is the prediction period for MNG stock?

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