EOG:TSXV Stock: Is It a Bubble?

Outlook: Eco (Atlantic) Oil & Gas Ltd. is assigned short-term B3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised :
Dominant Strategy : Sell
Time series to forecast n: for 1 Year
Methodology : Reinforcement Machine Learning (ML)
Hypothesis Testing : Stepwise Regression
Surveillance : Major exchange and OTC

Summary

Eco (Atlantic) Oil & Gas Ltd. prediction model is evaluated with Reinforcement Machine Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the EOG:TSXV stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

Key Points

1. Can we predict stock market using machine learning?
2. Market Risk
3. Dominated Move

EOG:TSXV Target Price Prediction Modeling Methodology

We consider Eco (Atlantic) Oil & Gas Ltd. Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of EOG:TSXV 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(Stepwise 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(Reinforcement Machine Learning (ML)) X S(n):→ 1 Year $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of EOG:TSXV stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Reinforcement Machine Learning (ML)

Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

Stepwise Regression

Stepwise regression is a method of variable selection in which variables are added or removed from a model one at a time, based on their statistical significance. There are two main types of stepwise regression: forward selection and backward elimination. In forward selection, variables are added to the model one at a time, starting with the variable with the highest F-statistic. The F-statistic is a measure of how much improvement in the model is gained by adding the variable. Variables are added to the model until no variable adds a statistically significant improvement to the model.

 

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?

EOG:TSXV Stock Forecast (Buy or Sell) for 1 Year

Sample Set: Neural Network
Stock/Index: EOG:TSXV Eco (Atlantic) Oil & Gas Ltd.
Time series to forecast: 1 Year

According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

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 Eco (Atlantic) Oil & Gas Ltd.

1. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
2. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
3. Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.
4. Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.

*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

Eco (Atlantic) Oil & Gas Ltd. is assigned short-term B3 & long-term Ba3 estimated rating. Eco (Atlantic) Oil & Gas Ltd. prediction model is evaluated with Reinforcement Machine Learning (ML) and Stepwise Regression^1,2,3,4 and it is concluded that the EOG:TSXV stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell

EOG:TSXV Eco (Atlantic) Oil & Gas Ltd. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Ba3
Income Statement	B2	Baa2
Balance Sheet	Ba3	C
Leverage Ratios	Caa2	B1
Cash Flow	C	Baa2
Rates of Return and Profitability	Caa2	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?

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 481 signals.

References

1. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
2. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
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. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
7. Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press

Frequently Asked Questions

Q: What is the prediction methodology for EOG:TSXV stock?
A: EOG:TSXV stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Stepwise Regression
Q: Is EOG:TSXV stock a buy or sell?
A: The dominant strategy among neural network is to Sell EOG:TSXV Stock.
Q: Is Eco (Atlantic) Oil & Gas Ltd. stock a good investment?
A: The consensus rating for Eco (Atlantic) Oil & Gas Ltd. is Sell and is assigned short-term B3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of EOG:TSXV stock?
A: The consensus rating for EOG:TSXV is Sell.
Q: What is the prediction period for EOG:TSXV stock?
A: The prediction period for EOG:TSXV is 1 Year

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