Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction.** We evaluate ConocoPhillips prediction models with Transfer Learning (ML) and Factor ^{1,2,3,4} and conclude that the COP 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 SellBuy COP stock.**

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

*Keywords:*## Key Points

- How can neural networks improve predictions?
- Which neural network is best for prediction?
- What are main components of Markov decision process?

## COP Target Price Prediction Modeling Methodology

Recently, a lot of interesting work has been done in the area of applying Machine Learning Algorithms for analyzing price patterns and predicting stock prices and index changes. Most stock traders nowadays depend on Intelligent Trading Systems which help them in predicting prices based on various situations and conditions, thereby helping them in making instantaneous investment decisions. We consider ConocoPhillips Stock Decision Process with Factor where A is the set of discrete actions of COP 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(Factor)

^{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(Transfer Learning (ML)) X S(n):→ (n+1 year) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of COP 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?

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

**Sample Set:**Neural Network

**Stock/Index:**COP ConocoPhillips

**Time series to forecast n: 20 Sep 2022**for (n+1 year)

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

ConocoPhillips assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Transfer Learning (ML) with Factor ^{1,2,3,4} and conclude that the COP 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 SellBuy COP stock.**

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

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

Outlook* | B2 | B2 |

Operational Risk | 80 | 54 |

Market Risk | 70 | 34 |

Technical Analysis | 44 | 69 |

Fundamental Analysis | 34 | 54 |

Risk Unsystematic | 34 | 56 |

### Prediction Confidence Score

## References

- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- 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.
- 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.
- Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
- P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
- Barkan O. 2016. Bayesian neural word embedding. arXiv:1603.06571 [math.ST]
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006

## Frequently Asked Questions

Q: What is the prediction methodology for COP stock?A: COP stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Factor

Q: Is COP stock a buy or sell?

A: The dominant strategy among neural network is to SellBuy COP Stock.

Q: Is ConocoPhillips stock a good investment?

A: The consensus rating for ConocoPhillips is SellBuy and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of COP stock?

A: The consensus rating for COP is SellBuy.

Q: What is the prediction period for COP stock?

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