LICY Stock: A Sinking Ship?

Outlook: Li-Cycle Holdings Corp. Common Shares is assigned short-term Ba3 & long-term B1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 24 Jun 2023 for 8 Weeks
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

Li-Cycle Holdings Corp (NYSE: LICY) is a leader in lithium-ion battery recycling. The company's proprietary Spoke & Hub Technologies™ enable it to recover critical materials from lithium-ion batteries and reintroduce them back into the supply chain. Li-Cycle has a number of recycling facilities in North America and Europe, and it is planning to expand its operations in the coming years. Li-Cycle's Spoke Technology™ is a decentralized network of collection and processing centers that collect used lithium-ion batteries from a variety of sources. The Hub Technology™ is a centralized processing facility that uses a hydrometallurgical process to recover critical materials from the lithium-ion batteries collected by the Spoke Technology™. Li-Cycle's recycling process is more sustainable than traditional methods of lithium-ion battery recycling. The company's process uses less energy and produces less waste than traditional methods. Li-Cycle's recycling process also recovers a higher percentage of critical materials from lithium-ion batteries. Li-Cycle's customers include battery manufacturers, electric vehicle manufacturers, and battery recycling companies. The company's products include black mass, which is a powder that contains lithium, cobalt, nickel, and other critical materials. Li-Cycle also produces lithium hydroxide, which is a key component in lithium-ion batteries. Li-Cycle is well-positioned to benefit from the growing demand for lithium-ion battery recycling. The global market for lithium-ion battery recycling is expected to grow from $1.5 billion in 2022 to $10 billion in 2030. Li-Cycle is one of the few companies that have the technology and infrastructure to recycle lithium-ion batteries at scale. The company is also well-funded. Li-Cycle has raised over $1 billion in funding from investors such as Glencore, Trafigura, and Koch Strategic Platforms. This funding will allow the company to expand its operations and meet the growing demand for lithium-ion battery recycling. Li-Cycle is a leader in the lithium-ion battery recycling market. The company has a strong technology platform, a well-funded business, and a growing customer base. Li-Cycle is well-positioned to benefit from the growing demand for lithium-ion battery recycling in the coming years.Li-Cycle Holdings Corp. Common Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression^1,2,3,4 and it is concluded that the LICY 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 Graph 29

Key Points

Technical Analysis with Algorithmic Trading
What is statistical models in machine learning?
How can neural networks improve predictions?

LICY Target Price Prediction Modeling Methodology

We consider Li-Cycle Holdings Corp. Common Shares Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of LICY 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(Multiple Regression)^5,6,7=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ 8 Weeks

\sum_{i = 1}^{n} (r_{i})

n:Time series to forecast

p:Price signals of LICY 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.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.

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?

LICY Stock Forecast (Buy or Sell) for 8 Weeks

Sample Set: Neural Network
Stock/Index: LICY Li-Cycle Holdings Corp. Common Shares
Time series to forecast n: 24 Jun 2023 for 8 Weeks

According to price forecasts for 8 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%

IFRS Reconciliation Adjustments for Li-Cycle Holdings Corp. Common Shares

Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.
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.)
An entity that first applies these amendments at the same time it first applies this Standard shall apply paragraphs 7.2.1–7.2.28 instead of paragraphs 7.2.31–7.2.34.
Conversely, if the critical terms of the hedging instrument and the hedged item are not closely aligned, there is an increased level of uncertainty about the extent of offset. Consequently, the hedge effectiveness during the term of the hedging relationship is more difficult to predict. In such a situation it might only be possible for an entity to conclude on the basis of a quantitative assessment that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6). In some situations a quantitative assessment might also be needed to assess whether the hedge ratio used for designating the hedging relationship meets the hedge effectiveness requirements (see paragraphs B6.4.9–B6.4.11). An entity can use the same or different methods for those two different purposes.

*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

Li-Cycle Holdings Corp. Common Shares is assigned short-term Ba3 & long-term B1 estimated rating. Li-Cycle Holdings Corp. Common Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression^1,2,3,4 and it is concluded that the LICY stock is predictable in the short/long term.

According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

LICY Li-Cycle Holdings Corp. Common Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Income Statement	B1	B2
Balance Sheet	Baa2	Ba3
Leverage Ratios	Ba1	Caa2
Cash Flow	C	Ba3
Rates of Return and Profitability	Baa2	Ba2

*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: 80 out of 100 with 839 signals.

References

Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
Athey S, Wager S. 2017. Efficient policy learning. arXiv:1702.02896 [math.ST]

Frequently Asked Questions

Q: What is the prediction methodology for LICY stock?
A: LICY stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Multiple Regression
Q: Is LICY stock a buy or sell?
A: The dominant strategy among neural network is to Sell LICY Stock.
Q: Is Li-Cycle Holdings Corp. Common Shares stock a good investment?
A: The consensus rating for Li-Cycle Holdings Corp. Common Shares is Sell and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of LICY stock?
A: The consensus rating for LICY is Sell.
Q: What is the prediction period for LICY stock?
A: The prediction period for LICY is 8 Weeks