Modelling A.I. in Economics

LEV:TSX Lion Electric Company (The) (Forecast)

Outlook: Lion Electric Company (The) assigned short-term B3 & long-term Ba2 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 13 Dec 2022 for (n+4 weeks)
Methodology : Multi-Task Learning (ML)

Abstract

In this paper a Bayesian regularized artificial neural network is proposed as a novel method to forecast financial market behavior. Daily market prices and financial technical indicators are utilized as inputs to predict the one day future closing price of individual stocks. The prediction of stock price movement is generally considered to be a challenging and important task for financial time series analysis. (Anand, C., 2021. Comparison of stock price prediction models using pre-trained neural networks. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 3(02), pp.122-134.) We evaluate Lion Electric Company (The) prediction models with Multi-Task Learning (ML) and Stepwise Regression1,2,3,4 and conclude that the LEV:TSX stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Key Points

  1. How do predictive algorithms actually work?
  2. How do you decide buy or sell a stock?
  3. Prediction Modeling

LEV:TSX Target Price Prediction Modeling Methodology

We consider Lion Electric Company (The) Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of LEV: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(Stepwise Regression)5,6,7= 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(Multi-Task Learning (ML)) X S(n):→ (n+4 weeks) i = 1 n s i

n:Time series to forecast

p:Price signals of LEV:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

LEV:TSX Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: LEV:TSX Lion Electric Company (The)
Time series to forecast n: 13 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) 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%

Adjusted IFRS* Prediction Methods for Lion Electric Company (The)

  1. The decision of an entity to designate a financial asset or financial liability as at fair value through profit or loss is similar to an accounting policy choice (although, unlike an accounting policy choice, it is not required to be applied consistently to all similar transactions). When an entity has such a choice, paragraph 14(b) of IAS 8 requires the chosen policy to result in the financial statements providing reliable and more relevant information about the effects of transactions, other events and conditions on the entity's financial position, financial performance or cash flows. For example, in the case of designation of a financial liability as at fair value through profit or loss, paragraph 4.2.2 sets out the two circumstances when the requirement for more relevant information will be met. Accordingly, to choose such designation in accordance with paragraph 4.2.2, the entity needs to demonstrate that it falls within one (or both) of these two circumstances.
  2. If the group of items does not have any offsetting risk positions (for example, a group of foreign currency expenses that affect different line items in the statement of profit or loss and other comprehensive income that are hedged for foreign currency risk) then the reclassified hedging instrument gains or losses shall be apportioned to the line items affected by the hedged items. This apportionment shall be done on a systematic and rational basis and shall not result in the grossing up of the net gains or losses arising from a single hedging instrument.
  3. For the purpose of applying paragraphs B4.1.11(b) and B4.1.12(b), irrespective of the event or circumstance that causes the early termination of the contract, a party may pay or receive reasonable compensation for that early termination. For example, a party may pay or receive reasonable compensation when it chooses to terminate the contract early (or otherwise causes the early termination to occur).
  4. An entity shall assess at the inception of the hedging relationship, and on an ongoing basis, whether a hedging relationship meets the hedge effectiveness requirements. At a minimum, an entity shall perform the ongoing assessment at each reporting date or upon a significant change in the circumstances affecting the hedge effectiveness requirements, whichever comes first. The assessment relates to expectations about hedge effectiveness and is therefore only forward-looking.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Lion Electric Company (The) assigned short-term B3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Stepwise Regression1,2,3,4 and conclude that the LEV:TSX stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Financial State Forecast for LEV:TSX Lion Electric Company (The) Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba2
Operational Risk 3475
Market Risk3466
Technical Analysis7284
Fundamental Analysis7032
Risk Unsystematic4578

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 677 signals.

References

  1. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
  2. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
  3. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
  4. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
  5. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
  6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
  7. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
Frequently Asked QuestionsQ: What is the prediction methodology for LEV:TSX stock?
A: LEV:TSX stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Stepwise Regression
Q: Is LEV:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell LEV:TSX Stock.
Q: Is Lion Electric Company (The) stock a good investment?
A: The consensus rating for Lion Electric Company (The) is Sell and assigned short-term B3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of LEV:TSX stock?
A: The consensus rating for LEV:TSX is Sell.
Q: What is the prediction period for LEV:TSX stock?
A: The prediction period for LEV:TSX is (n+4 weeks)

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.