DWSN Dawson Geophysical Company Common Stock

Outlook: Dawson Geophysical Company Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 16 Feb 2023 for (n+8 weeks)
Methodology : Deductive Inference (ML)

Abstract

Dawson Geophysical Company Common Stock prediction model is evaluated with Deductive Inference (ML) and Linear Regression^1,2,3,4 and it is concluded that the DWSN stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. How do predictive algorithms actually work?
2. Dominated Move
3. Nash Equilibria

DWSN Target Price Prediction Modeling Methodology

We consider Dawson Geophysical Company Common Stock Decision Process with Deductive Inference (ML) where A is the set of discrete actions of DWSN 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(Linear Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of DWSN stock

j:Nash equilibria (Neural Network)

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?

DWSN Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: DWSN Dawson Geophysical Company Common Stock
Time series to forecast n: 16 Feb 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

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%

IFRS Reconciliation Adjustments for Dawson Geophysical Company Common Stock

1. If a put option obligation written by an entity or call option right held by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at amortised cost, the associated liability is measured at its cost (ie the consideration received) adjusted for the amortisation of any difference between that cost and the gross carrying amount of the transferred asset at the expiration date of the option. For example, assume that the gross carrying amount of the asset on the date of the transfer is CU98 and that the consideration received is CU95. The gross carrying amount of the asset on the option exercise date will be CU100. The initial carrying amount of the associated liability is CU95 and the difference between CU95 and CU100 is recognised in profit or loss using the effective interest method. If the option is exercised, any difference between the carrying amount of the associated liability and the exercise price is recognised in profit or loss.
2. 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.
3. Paragraph 5.5.4 requires that lifetime expected credit losses are recognised on all financial instruments for which there has been significant increases in credit risk since initial recognition. In order to meet this objective, if an entity is not able to group financial instruments for which the credit risk is considered to have increased significantly since initial recognition based on shared credit risk characteristics, the entity should recognise lifetime expected credit losses on a portion of the financial assets for which credit risk is deemed to have increased significantly. The aggregation of financial instruments to assess whether there are changes in credit risk on a collective basis may change over time as new information becomes available on groups of, or individual, financial instruments.
4. If a collar, in the form of a purchased call and written put, prevents a transferred asset from being derecognised and the entity measures the asset at fair value, it continues to measure the asset at fair value. The associated liability is measured at (i) the sum of the call exercise price and fair value of the put option less the time value of the call option, if the call option is in or at the money, or (ii) the sum of the fair value of the asset and the fair value of the put option less the time value of the call option if the call option is out of the money. The adjustment to the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the options held and written by the entity. For example, assume an entity transfers a financial asset that is measured at fair value while simultaneously purchasing a call with an exercise price of CU120 and writing a put with an exercise price of CU80. Assume also that the fair value of the asset is CU100 at the date of the transfer. The time value of the put and call are CU1 and CU5 respectively. In this case, the entity recognises an asset of CU100 (the fair value of the asset) and a liability of CU96 [(CU100 + CU1) – CU5]. This gives a net asset value of CU4, which is the fair value of the options held and written by the entity.

*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.

Conclusions

Dawson Geophysical Company Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Dawson Geophysical Company Common Stock prediction model is evaluated with Deductive Inference (ML) and Linear Regression^1,2,3,4 and it is concluded that the DWSN stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

DWSN Dawson Geophysical Company Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B3	Baa2
Balance Sheet	Baa2	Baa2
Leverage Ratios	Baa2	Ba2
Cash Flow	Baa2	Caa2
Rates of Return and Profitability	Ba3	B1

*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?

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 833 signals.

References

1. Breiman L. 2001a. Random forests. Mach. Learn. 45:5–32
2. Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
3. 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.
4. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
5. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
6. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
7. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.

Frequently Asked Questions

Q: What is the prediction methodology for DWSN stock?
A: DWSN stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Linear Regression
Q: Is DWSN stock a buy or sell?
A: The dominant strategy among neural network is to Hold DWSN Stock.
Q: Is Dawson Geophysical Company Common Stock stock a good investment?
A: The consensus rating for Dawson Geophysical Company Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of DWSN stock?
A: The consensus rating for DWSN is Hold.
Q: What is the prediction period for DWSN stock?
A: The prediction period for DWSN is (n+8 weeks)

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