As part of this research, different techniques have been studied for data extraction and analysis. After having reviewed the work related to the initial idea of the research, it is shown the development carried out, together with the data extraction and the machine learning algorithms for prediction used. The calculation of technical analysis metrics is also included. The development of a visualization platform has been proposed for high-level interaction between the user and the recommendation system.** We evaluate ConocoPhillips prediction models with Transductive Learning (ML) and Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the COP 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 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

- What are the most successful trading algorithms?
- Stock Rating
- Can neural networks predict stock market?

## COP Target Price Prediction Modeling Methodology

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. We consider ConocoPhillips Stock Decision Process with Statistical Hypothesis Testing 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 γ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Statistical Hypothesis Testing)

^{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(Transductive Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({s}_{i}\right)$

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+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**COP ConocoPhillips

**Time series to forecast n: 11 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold 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 B3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transductive Learning (ML) with Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the COP 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 COP stock.**

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

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

Outlook* | B3 | B1 |

Operational Risk | 52 | 54 |

Market Risk | 63 | 63 |

Technical Analysis | 45 | 87 |

Fundamental Analysis | 30 | 58 |

Risk Unsystematic | 48 | 37 |

### Prediction Confidence Score

## References

- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Candès E, Tao T. 2007. The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35:2313–51
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- Bertsimas D, King A, Mazumder R. 2016. Best subset selection via a modern optimization lens. Ann. Stat. 44:813–52
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]

## Frequently Asked Questions

Q: What is the prediction methodology for COP stock?A: COP stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Statistical Hypothesis Testing

Q: Is COP stock a buy or sell?

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

Q: Is ConocoPhillips stock a good investment?

A: The consensus rating for ConocoPhillips is Hold and assigned short-term B3 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of COP stock?

A: The consensus rating for COP is Hold.

Q: What is the prediction period for COP stock?

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