Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. ** We evaluate Chesapeake Energy prediction models with Modular Neural Network (Market Direction Analysis) and Polynomial Regression ^{1,2,3,4} and conclude that the CHK 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 Hold CHK stock.**

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

*Keywords:*## Key Points

- What is a prediction confidence?
- Is it better to buy and sell or hold?
- Decision Making

## CHK Target Price Prediction Modeling Methodology

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 consider Chesapeake Energy Stock Decision Process with Polynomial Regression where A is the set of discrete actions of CHK 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(Polynomial 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(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of CHK 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?

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

**Sample Set:**Neural Network

**Stock/Index:**CHK Chesapeake Energy

**Time series to forecast n: 16 Oct 2022**for (n+3 month)

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

Chesapeake Energy assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Polynomial Regression ^{1,2,3,4} and conclude that the CHK 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 Hold CHK stock.**

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

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 69 | 90 |

Market Risk | 75 | 39 |

Technical Analysis | 70 | 52 |

Fundamental Analysis | 59 | 65 |

Risk Unsystematic | 41 | 78 |

### Prediction Confidence Score

## References

- Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
- Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
- Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- Akgiray, V. (1989), "Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts," Journal of Business, 62, 55–80.
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.

## Frequently Asked Questions

Q: What is the prediction methodology for CHK stock?A: CHK stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Polynomial Regression

Q: Is CHK stock a buy or sell?

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

Q: Is Chesapeake Energy stock a good investment?

A: The consensus rating for Chesapeake Energy is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of CHK stock?

A: The consensus rating for CHK is Hold.

Q: What is the prediction period for CHK stock?

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