Modelling A.I. in Economics

Where Will ELVA Stock Be in 1 Year? (Forecast)

Outlook: Electrovaya Inc. Common Shares is assigned short-term Baa2 & long-term B2 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 (News Feed Sentiment Analysis)
Hypothesis Testing : Factor
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

ELVA stock (ticker: ELVA) is a stock that trades on the Nasdaq stock exchange. The company is a medical device company that develops and manufactures minimally invasive surgical instruments and systems. ELVA was founded in 1996 and is headquartered in Irvine, California. The company went public in 2014 and has a market capitalization of approximately $1.5 billion. ELVA's products are used in a variety of surgical procedures, including laparoscopic surgery, robotic surgery, and minimally invasive spine surgery. The company has a strong track record of innovation and has developed several new products that have been well-received by the medical community. ELVA is also a leader in the development of new technologies, such as its proprietary CO2 laser technology. ELVA is a small-cap stock that has been volatile in recent years. However, the company has a strong financial position and a solid growth strategy. ELVA is a potential investment for investors who are looking for a company with a strong track record of innovation and growth. Electrovaya Inc. Common Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Factor1,2,3,4 and it is concluded that the ELVA 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.5 According to price forecasts for 1 Year period, the dominant strategy among neural network is: Buy

Graph 27

Key Points

  1. Modular Neural Network (News Feed Sentiment Analysis) for ELVA stock price prediction process.
  2. Factor
  3. Stock Rating
  4. What is neural prediction?
  5. What statistical methods are used to analyze data?

ELVA Stock Price Forecast

We consider Electrovaya Inc. Common Shares Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of ELVA 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


Sample Set: Neural Network
Stock/Index: ELVA Electrovaya Inc. Common Shares
Time series to forecast: 1 Year

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


F(Factor)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ 1 Year i = 1 n r i

n:Time series to forecast

p:Price signals of ELVA stock

j:Nash equilibria (Neural Network)

k:Dominated move of ELVA stock holders

a:Best response for ELVA target price


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.5 In statistics, a factor is a variable that can influence the value of another variable. Factors can be categorical or continuous. Categorical factors have a limited number of possible values, such as gender (male or female) or blood type (A, B, AB, or O). Continuous factors can have an infinite number of possible values, such as height or weight. Factors can be used to explain the variation in a dependent variable. For example, a study might find that there is a relationship between gender and height. In this case, gender would be the independent variable, height would be the dependent variable, and the factor would be gender.6,7

 

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How do PredictiveAI algorithms actually work?

ELVA Stock Forecast (Buy or Sell) Strategic Interaction Table

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 ELVA Stock Prediction Model

  1. The requirements in paragraphs 6.8.4–6.8.8 may cease to apply at different times. Therefore, in applying paragraph 6.9.1, an entity may be required to amend the formal designation of its hedging relationships at different times, or may be required to amend the formal designation of a hedging relationship more than once. When, and only when, such a change is made to the hedge designation, an entity shall apply paragraphs 6.9.7–6.9.12 as applicable. An entity also shall apply paragraph 6.5.8 (for a fair value hedge) or paragraph 6.5.11 (for a cash flow hedge) to account for any changes in the fair value of the hedged item or the hedging instrument.
  2. This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.
  3. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
  4. If a financial asset contains a contractual term that could change the timing or amount of contractual cash flows (for example, if the asset can be prepaid before maturity or its term can be extended), the entity must determine whether the contractual cash flows that could arise over the life of the instrument due to that contractual term are solely payments of principal and interest on the principal amount outstanding. To make this determination, the entity must assess the contractual cash flows that could arise both before, and after, the change in contractual cash flows. The entity may also need to assess the nature of any contingent event (ie the trigger) that would change the timing or amount of the contractual cash flows. While the nature of the contingent event in itself is not a determinative factor in assessing whether the contractual cash flows are solely payments of principal and interest, it may be an indicator. For example, compare a financial instrument with an interest rate that is reset to a higher rate if the debtor misses a particular number of payments to a financial instrument with an interest rate that is reset to a higher rate if a specified equity index reaches a particular level. It is more likely in the former case that the contractual cash flows over the life of the instrument will be solely payments of principal and interest on the principal amount outstanding because of the relationship between missed payments and an increase in credit risk. (See also paragraph B4.1.18.)

*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.

ELVA Electrovaya Inc. Common Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Baa2B2
Income StatementBaa2B1
Balance SheetBaa2Caa2
Leverage RatiosBaa2C
Cash FlowB1Caa2
Rates of Return and ProfitabilityBaa2Baa2

*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?

References

  1. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
  2. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
  3. Künzel S, Sekhon J, Bickel P, Yu B. 2017. Meta-learners for estimating heterogeneous treatment effects using machine learning. arXiv:1706.03461 [math.ST]
  4. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
  5. P. Milgrom and I. Segal. Envelope theorems for arbitrary choice sets. Econometrica, 70(2):583–601, 2002
  6. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
  7. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
Frequently Asked QuestionsQ: Is ELVA stock expected to rise?
A: ELVA stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Factor and it is concluded that dominant strategy for ELVA stock is Buy
Q: Is ELVA stock a buy or sell?
A: The dominant strategy among neural network is to Buy ELVA Stock.
Q: Is Electrovaya Inc. Common Shares stock a good investment?
A: The consensus rating for Electrovaya Inc. Common Shares is Buy and is assigned short-term Baa2 & long-term B2 estimated rating.
Q: What is the consensus rating of ELVA stock?
A: The consensus rating for ELVA is Buy.
Q: What is the forecast for ELVA stock?
A: ELVA target price forecast: Buy

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