Modelling A.I. in Economics

LXFR Luxfer Holdings PLC Ordinary Shares

Outlook: Luxfer Holdings PLC Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 21 May 2023 for (n+1 year)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

Abstract

Luxfer Holdings PLC Ordinary Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the LXFR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

Key Points

  1. Why do we need predictive models?
  2. How useful are statistical predictions?
  3. What are the most successful trading algorithms?

LXFR Target Price Prediction Modeling Methodology

We consider Luxfer Holdings PLC Ordinary Shares Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of LXFR 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(Polynomial 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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+1 year) i = 1 n a i

n:Time series to forecast

p:Price signals of LXFR 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?

LXFR Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: LXFR Luxfer Holdings PLC Ordinary Shares
Time series to forecast n: 21 May 2023 for (n+1 year)

According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

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 Luxfer Holdings PLC Ordinary Shares

  1. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
  2. Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.
  3. 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.
  4. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.

*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

Luxfer Holdings PLC Ordinary Shares is assigned short-term Ba1 & long-term Ba1 estimated rating. Luxfer Holdings PLC Ordinary Shares prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the LXFR stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Hold

LXFR Luxfer Holdings PLC Ordinary Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3Baa2
Balance SheetBaa2Caa2
Leverage RatiosCC
Cash FlowBaa2Baa2
Rates of Return and ProfitabilityB1Baa2

*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: 79 out of 100 with 485 signals.

References

  1. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
  2. G. Theocharous and A. Hallak. Lifetime value marketing using reinforcement learning. RLDM 2013, page 19, 2013
  3. D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
  4. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
  5. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
  6. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
  7. K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
Frequently Asked QuestionsQ: What is the prediction methodology for LXFR stock?
A: LXFR stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Polynomial Regression
Q: Is LXFR stock a buy or sell?
A: The dominant strategy among neural network is to Hold LXFR Stock.
Q: Is Luxfer Holdings PLC Ordinary Shares stock a good investment?
A: The consensus rating for Luxfer Holdings PLC Ordinary Shares is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of LXFR stock?
A: The consensus rating for LXFR is Hold.
Q: What is the prediction period for LXFR stock?
A: The prediction period for LXFR is (n+1 year)



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