Dominant Strategy : Buy
Time series to forecast n: 13 Mar 2023 for (n+1 year)
Methodology : Active Learning (ML)
Abstract
LOYAL LITHIUM LIMITED prediction model is evaluated with Active Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the LLI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: BuyKey Points
- What statistical methods are used to analyze data?
- Stock Rating
- How do you decide buy or sell a stock?
LLI Target Price Prediction Modeling Methodology
We consider LOYAL LITHIUM LIMITED Decision Process with Active Learning (ML) where A is the set of discrete actions of LLI 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= X R(Active Learning (ML)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of LLI 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?
LLI Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: LLI LOYAL LITHIUM LIMITED
Time series to forecast n: 13 Mar 2023 for (n+1 year)
According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy
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 LOYAL LITHIUM LIMITED
- The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
- In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.
- An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
- A firm commitment to acquire a business in a business combination cannot be a hedged item, except for foreign currency risk, because the other risks being hedged cannot be specifically identified and measured. Those other risks are general business risks.
*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
LOYAL LITHIUM LIMITED is assigned short-term Ba1 & long-term Ba1 estimated rating. LOYAL LITHIUM LIMITED prediction model is evaluated with Active Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the LLI stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Buy
LLI LOYAL LITHIUM LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | Baa2 | Caa2 |
Balance Sheet | Ba1 | Ba1 |
Leverage Ratios | C | C |
Cash Flow | B3 | C |
Rates of Return and Profitability | C | C |
*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

References
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., MO Stock Price Prediction. AC Investment Research Journal, 101(3).
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
- Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
- S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
Frequently Asked Questions
Q: What is the prediction methodology for LLI stock?A: LLI stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is LLI stock a buy or sell?
A: The dominant strategy among neural network is to Buy LLI Stock.
Q: Is LOYAL LITHIUM LIMITED stock a good investment?
A: The consensus rating for LOYAL LITHIUM LIMITED is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LLI stock?
A: The consensus rating for LLI is Buy.
Q: What is the prediction period for LLI stock?
A: The prediction period for LLI is (n+1 year)
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