The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods.** We evaluate Marathon Oil prediction models with Active Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the MRO stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold MRO stock.**

**MRO, Marathon Oil, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Should I buy stocks now or wait amid such uncertainty?
- Stock Rating
- Buy, Sell and Hold Signals

## MRO Target Price Prediction Modeling Methodology

Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global stock markets and various financial products to predict the next-day stock trend. We consider Marathon Oil Stock Decision Process with Logistic Regression where A is the set of discrete actions of MRO 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(Logistic 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(Active Learning (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of MRO 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?

## MRO Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**MRO Marathon Oil

**Time series to forecast n: 24 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold MRO 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

Marathon Oil assigned short-term Baa2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Active Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the MRO stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold MRO stock.**

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

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

Outlook* | Baa2 | B2 |

Operational Risk | 68 | 54 |

Market Risk | 84 | 35 |

Technical Analysis | 83 | 70 |

Fundamental Analysis | 81 | 31 |

Risk Unsystematic | 87 | 73 |

### Prediction Confidence Score

## References

- Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
- C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
- 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
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.

## Frequently Asked Questions

Q: What is the prediction methodology for MRO stock?A: MRO stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Logistic Regression

Q: Is MRO stock a buy or sell?

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

Q: Is Marathon Oil stock a good investment?

A: The consensus rating for Marathon Oil is Hold and assigned short-term Baa2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of MRO stock?

A: The consensus rating for MRO is Hold.

Q: What is the prediction period for MRO stock?

A: The prediction period for MRO is (n+8 weeks)

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