Is VLE:TSX Stock Buy or Sell?

Outlook: Valeura Energy Inc. is assigned short-term B1 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised :
Dominant Strategy : Speculative Trend
Time series to forecast n: for 1 Year
Methodology : Transductive Learning (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
Surveillance : Major exchange and OTC

Abstract

Valeura Energy Inc. prediction model is evaluated with Transductive Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the VLE:TSX stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend

Key Points

1. Can neural networks predict stock market?
2. Reaction Function
3. Decision Making

VLE:TSX Target Price Prediction Modeling Methodology

We consider Valeura Energy Inc. Decision Process with Transductive Learning (ML) where A is the set of discrete actions of VLE: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(Wilcoxon Sign-Rank Test)^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(Transductive 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 VLE:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transductive Learning (ML)

Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.

Wilcoxon Sign-Rank Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

 

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?

VLE:TSX Stock Forecast (Buy or Sell) for 1 Year

Sample Set: Neural Network
Stock/Index: VLE:TSX Valeura Energy Inc.
Time series to forecast: 1 Year

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

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 Valeura Energy Inc.

1. An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
2. The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
3. Paragraph 5.7.5 permits an entity to make an irrevocable election to present in other comprehensive income changes in the fair value of an investment in an equity instrument that is not held for trading. This election is made on an instrument-by-instrument (ie share-by-share) basis. Amounts presented in other comprehensive income shall not be subsequently transferred to profit or loss. However, the entity may transfer the cumulative gain or loss within equity. Dividends on such investments are recognised in profit or loss in accordance with paragraph 5.7.6 unless the dividend clearly represents a recovery of part of the cost of the investment.
4. A layer component that includes a prepayment option is not eligible to be designated as a hedged item in a fair value hedge if the prepayment option's fair value is affected by changes in the hedged risk, unless the designated layer includes the effect of the related prepayment option when determining the change in the fair value of the hedged item.

*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

Valeura Energy Inc. is assigned short-term B1 & long-term Ba3 estimated rating. Valeura Energy Inc. prediction model is evaluated with Transductive Learning (ML) and Wilcoxon Sign-Rank Test^1,2,3,4 and it is concluded that the VLE:TSX stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Speculative Trend

VLE:TSX Valeura Energy Inc. Financial Analysis*

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

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 790 signals.

References

1. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
2. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
3. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
4. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
5. Athey S, Bayati M, Doudchenko N, Imbens G, Khosravi K. 2017a. Matrix completion methods for causal panel data models. arXiv:1710.10251 [math.ST]
6. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
7. S. Devlin, L. Yliniemi, D. Kudenko, and K. Tumer. Potential-based difference rewards for multiagent reinforcement learning. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014

Frequently Asked Questions

Q: What is the prediction methodology for VLE:TSX stock?
A: VLE:TSX stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Wilcoxon Sign-Rank Test
Q: Is VLE:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend VLE:TSX Stock.
Q: Is Valeura Energy Inc. stock a good investment?
A: The consensus rating for Valeura Energy Inc. is Speculative Trend and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of VLE:TSX stock?
A: The consensus rating for VLE:TSX is Speculative Trend.
Q: What is the prediction period for VLE:TSX stock?
A: The prediction period for VLE:TSX is 1 Year

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