Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators.** We evaluate ConocoPhillips prediction models with Transfer 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+3 month) period: The dominant strategy among neural network is to Sell 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?
- Can neural networks predict stock market?
- Operational Risk

## COP Target Price Prediction Modeling Methodology

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. 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(Transfer Learning (ML)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of COP stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

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How do AC Investment Research machine learning (predictive) algorithms actually work?

## COP Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**COP ConocoPhillips

**Time series to forecast n: 28 Oct 2022**for (n+3 month)

**According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell 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%**

## Adjusted IFRS* Prediction Methods for ConocoPhillips

- The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.
- Adjusting the hedge ratio by increasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).
- Annual Improvements to IFRS Standards 2018–2020, issued in May 2020, added paragraphs 7.2.35 and B3.3.6A and amended paragraph B3.3.6. An entity shall apply that amendment for annual reporting periods beginning on or after 1 January 2022. Earlier application is permitted. If an entity applies the amendment for an earlier period, it shall disclose that fact.
- If there are changes in circumstances that affect hedge effectiveness, an entity may have to change the method for assessing whether a hedging relationship meets the hedge effectiveness requirements in order to ensure that the relevant characteristics of the hedging relationship, including the sources of hedge ineffectiveness, are still captured.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

ConocoPhillips assigned short-term B2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Transfer 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+3 month) period: The dominant strategy among neural network is to Sell COP stock.**

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

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

Outlook* | B2 | B1 |

Operational Risk | 69 | 42 |

Market Risk | 38 | 53 |

Technical Analysis | 38 | 84 |

Fundamental Analysis | 80 | 48 |

Risk Unsystematic | 60 | 66 |

### Prediction Confidence Score

## References

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- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
- Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer

## 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 Statistical Hypothesis Testing

Q: Is COP stock a buy or sell?

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

Q: Is ConocoPhillips stock a good investment?

A: The consensus rating for ConocoPhillips is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of COP stock?

A: The consensus rating for COP is Sell.

Q: What is the prediction period for COP stock?

A: The prediction period for COP is (n+3 month)

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