**Outlook:**ProPetro Holding Corp. Common Stock is assigned short-term B1 & long-term Ba3 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**Speculative Trend

**Time series to forecast n:** for

^{2}

**Methodology :**Statistical Inference (ML)

**Hypothesis Testing :**Independent T-Test

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Summary

ProPetro Holding Corp. Common Stock prediction model is evaluated with Statistical Inference (ML) and Independent T-Test^{1,2,3,4}and it is concluded that the PUMP stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

**According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Speculative Trend**

## Key Points

- How do predictive algorithms actually work?
- Trading Signals
- Operational Risk

## PUMP Target Price Prediction Modeling Methodology

We consider ProPetro Holding Corp. Common Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of PUMP 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(Independent T-Test)

^{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(Statistical Inference (ML)) X S(n):→ 8 Weeks $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of PUMP stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Statistical Inference (ML)

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.### Independent T-Test

An independent t-test is a statistical test that compares the means of two independent samples. In an independent t-test, the data points in each sample are not related to each other. The independent t-test is a parametric test, which means that it assumes that the data is normally distributed. The independent t-test is also a two-sample test, which means that it compares the means of two independent samples.

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?

## PUMP Stock Forecast (Buy or Sell)

**Sample Set:**Neural Network

**Stock/Index:**PUMP ProPetro Holding Corp. Common Stock

**Time series to forecast:**8 Weeks

**According to price forecasts, the dominant strategy among neural network is: Speculative Trend**

Strategic Interaction Table Legend:

**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 (Grey to Black): *Technical Analysis%**

### Financial Data Adjustments for Statistical Inference (ML) based PUMP Stock Prediction Model

- Credit risk analysis is a multifactor and holistic analysis; whether a specific factor is relevant, and its weight compared to other factors, will depend on the type of product, characteristics of the financial instruments and the borrower as well as the geographical region. An entity shall consider reasonable and supportable information that is available without undue cost or effort and that is relevant for the particular financial instrument being assessed. However, some factors or indicators may not be identifiable on an individual financial instrument level. In such a case, the factors or indicators should be assessed for appropriate portfolios, groups of portfolios or portions of a portfolio of financial instruments to determine whether the requirement in paragraph 5.5.3 for the recognition of lifetime expected credit losses has been met.
- An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.
- For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
- An entity shall assess separately whether each subgroup meets the requirements in paragraph 6.6.1 to be an eligible hedged item. If any subgroup fails to meet the requirements in paragraph 6.6.1, the entity shall discontinue hedge accounting prospectively for the hedging relationship in its entirety. An entity also shall apply the requirements in paragraphs 6.5.8 and 6.5.11 to account for ineffectiveness related to the hedging relationship in its entirety.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

### PUMP ProPetro Holding Corp. Common Stock Financial Analysis*

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

Outlook* | B1 | Ba3 |

Income Statement | Baa2 | Baa2 |

Balance Sheet | C | B2 |

Leverage Ratios | Ba3 | Ba3 |

Cash Flow | C | Caa2 |

Rates of Return and Profitability | Baa2 | B3 |

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.

How does neural network examine financial reports and understand financial state of the company?

## Conclusions

ProPetro Holding Corp. Common Stock is assigned short-term B1 & long-term Ba3 estimated rating. ProPetro Holding Corp. Common Stock prediction model is evaluated with Statistical Inference (ML) and Independent T-Test^{1,2,3,4} and it is concluded that the PUMP stock is predictable in the short/long term. ** According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Speculative Trend**

### Prediction Confidence Score

## References

- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- Hill JL. 2011. Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Stat. 20:217–40
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer

## Frequently Asked Questions

Q: What is the prediction methodology for PUMP stock?A: PUMP stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Independent T-Test

Q: Is PUMP stock a buy or sell?

A: The dominant strategy among neural network is to Speculative Trend PUMP Stock.

Q: Is ProPetro Holding Corp. Common Stock stock a good investment?

A: The consensus rating for ProPetro Holding Corp. Common Stock is Speculative Trend and is assigned short-term B1 & long-term Ba3 estimated rating.

Q: What is the consensus rating of PUMP stock?

A: The consensus rating for PUMP is Speculative Trend.

Q: What is the prediction period for PUMP stock?

A: The prediction period for PUMP is 8 Weeks

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