Should You Buy EGLX:TSX Right Now?

Outlook: Enthusiast Gaming Holdings Inc. is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)
Hypothesis Testing : Logistic Regression
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

Enthusiast Gaming Holdings Inc. prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Logistic Regression^1,2,3,4 and it is concluded that the EGLX: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 emotional trigger/responses 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 emotional trigger/responses analysis, MNNs can be used to identify the emotional triggers that cause people to experience certain emotions, and to identify the responses that people typically exhibit when they experience those emotions. This information can then be used to develop more effective emotional support systems, to improve the accuracy of artificial intelligence systems, and to create more engaging and immersive entertainment experiences. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. Decision Making
2. What is statistical models in machine learning?
3. Market Signals

EGLX:TSX Target Price Prediction Modeling Methodology

We consider Enthusiast Gaming Holdings Inc. Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of EGLX: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(Logistic 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ 16 Weeks $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of EGLX:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Emotional Trigger/Responses Analysis)

A modular neural network (MNN) is a type of artificial neural network that can be used for emotional trigger/responses 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 emotional trigger/responses analysis, MNNs can be used to identify the emotional triggers that cause people to experience certain emotions, and to identify the responses that people typically exhibit when they experience those emotions. This information can then be used to develop more effective emotional support systems, to improve the accuracy of artificial intelligence systems, and to create more engaging and immersive entertainment experiences.

Logistic Regression

In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.

 

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?

EGLX:TSX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: EGLX:TSX Enthusiast Gaming Holdings Inc.
Time series to forecast: 16 Weeks

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

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 (Emotional Trigger/Responses Analysis) based EGLX:TSX Stock Prediction Model

1. The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
2. For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
3. If the contractual cash flows on a financial asset have been renegotiated or otherwise modified, but the financial asset is not derecognised, that financial asset is not automatically considered to have lower credit risk. An entity shall assess whether there has been a significant increase in credit risk since initial recognition on the basis of all reasonable and supportable information that is available without undue cost or effort. This includes historical and forwardlooking information and an assessment of the credit risk over the expected life of the financial asset, which includes information about the circumstances that led to the modification. Evidence that the criteria for the recognition of lifetime expected credit losses are no longer met may include a history of up-to-date and timely payment performance against the modified contractual terms. Typically a customer would need to demonstrate consistently good payment behaviour over a period of time before the credit risk is considered to have decreased.
4. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.

*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.

EGLX:TSX Enthusiast Gaming Holdings Inc. Financial Analysis*

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

*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

Enthusiast Gaming Holdings Inc. is assigned short-term Ba3 & long-term Ba3 estimated rating. Enthusiast Gaming Holdings Inc. prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and Logistic Regression^1,2,3,4 and it is concluded that the EGLX:TSX stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 806 signals.

References

1. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
2. M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
3. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
4. V. Borkar. A sensitivity formula for the risk-sensitive cost and the actor-critic algorithm. Systems & Control Letters, 44:339–346, 2001
5. Schapire RE, Freund Y. 2012. Boosting: Foundations and Algorithms. Cambridge, MA: MIT Press
6. M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
7. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44

Frequently Asked Questions

Q: What is the prediction methodology for EGLX:TSX stock?
A: EGLX:TSX stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Logistic Regression
Q: Is EGLX:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy EGLX:TSX Stock.
Q: Is Enthusiast Gaming Holdings Inc. stock a good investment?
A: The consensus rating for Enthusiast Gaming Holdings Inc. is Buy and is assigned short-term Ba3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of EGLX:TSX stock?
A: The consensus rating for EGLX:TSX is Buy.
Q: What is the prediction period for EGLX:TSX stock?
A: The prediction period for EGLX:TSX is 16 Weeks

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