Should You Buy Now or Wait? CNE:TSX Stock Forecast

Outlook: Canacol Energy Ltd. is assigned short-term B2 & long-term Ba1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (News Feed Sentiment Analysis)
Hypothesis Testing : Pearson Correlation
Surveillance : Major exchange and OTC

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

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

Summary

Canacol Energy Ltd. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation^1,2,3,4 and it is concluded that the CNE:TSX stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

Key Points

1. What is statistical models in machine learning?
2. What are buy sell or hold recommendations?
3. Market Outlook

CNE:TSX Target Price Prediction Modeling Methodology

We consider Canacol Energy Ltd. Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of CNE:TSX 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(Pearson Correlation)^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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ 8 Weeks $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of CNE:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (News Feed Sentiment Analysis)

A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.

Pearson Correlation

Pearson correlation, also known as Pearson's product-moment correlation, is a measure of the linear relationship between two variables. It is a statistical measure that assesses the strength and direction of a linear relationship between two variables. The sign of the correlation coefficient indicates the direction of the relationship, while the magnitude of the correlation coefficient indicates the strength of the relationship. A correlation coefficient of 0.9 indicates a strong positive correlation, while a correlation coefficient of 0.2 indicates a weak positive correlation.

 

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?

CNE:TSX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: CNE:TSX Canacol Energy Ltd.
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: Sell

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 Modular Neural Network (News Feed Sentiment Analysis) based CNE:TSX Stock Prediction Model

1. In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.
2. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
3. If subsequently an entity reasonably expects that the alternative benchmark rate will not be separately identifiable within 24 months from the date the entity designated it as a non-contractually specified risk component for the first time, the entity shall cease applying the requirement in paragraph 6.9.11 to that alternative benchmark rate and discontinue hedge accounting prospectively from the date of that reassessment for all hedging relationships in which the alternative benchmark rate was designated as a noncontractually specified risk component.
4. 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).

*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.

CNE:TSX Canacol Energy Ltd. Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba1
Income Statement	C	Baa2
Balance Sheet	Baa2	Baa2
Leverage Ratios	B1	C
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	Ba2	Baa2

*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

Canacol Energy Ltd. is assigned short-term B2 & long-term Ba1 estimated rating. Canacol Energy Ltd. prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation^1,2,3,4 and it is concluded that the CNE:TSX stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

Prediction Confidence Score

Trust metric by Neural Network: 79 out of 100 with 615 signals.

References

1. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
2. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
3. Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
4. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Apple's Stock Price: How News Affects Volatility. AC Investment Research Journal, 220(44).
5. C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
6. J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Re- search, 15:319–350, 2001.
7. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94

Frequently Asked Questions

Q: What is the prediction methodology for CNE:TSX stock?
A: CNE:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Pearson Correlation
Q: Is CNE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell CNE:TSX Stock.
Q: Is Canacol Energy Ltd. stock a good investment?
A: The consensus rating for Canacol Energy Ltd. is Sell and is assigned short-term B2 & long-term Ba1 estimated rating.
Q: What is the consensus rating of CNE:TSX stock?
A: The consensus rating for CNE:TSX is Sell.
Q: What is the prediction period for CNE:TSX stock?
A: The prediction period for CNE:TSX is 8 Weeks

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