Modelling A.I. in Economics

IFL INSIGNIA FINANCIAL LTD (Forecast)

Outlook: INSIGNIA FINANCIAL LTD is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : HoldBuy
Time series to forecast n: 18 Apr 2023 for (n+1 year)
Methodology : Multi-Task Learning (ML)

Abstract

INSIGNIA FINANCIAL LTD prediction model is evaluated with Multi-Task Learning (ML) and ElasticNet Regression1,2,3,4 and it is concluded that the IFL stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: HoldBuy

Key Points

  1. How do you know when a stock will go up or down?
  2. Dominated Move
  3. How do you decide buy or sell a stock?

IFL Target Price Prediction Modeling Methodology

We consider INSIGNIA FINANCIAL LTD Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of IFL 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(ElasticNet 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+1 year) i = 1 n r i

n:Time series to forecast

p:Price signals of IFL 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?

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

Sample Set: Neural Network
Stock/Index: IFL INSIGNIA FINANCIAL LTD
Time series to forecast n: 18 Apr 2023 for (n+1 year)

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

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 INSIGNIA FINANCIAL LTD

  1. In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.
  2. When determining whether the recognition of lifetime expected credit losses is required, an entity shall consider reasonable and supportable information that is available without undue cost or effort and that may affect the credit risk on a financial instrument in accordance with paragraph 5.5.17(c). An entity need not undertake an exhaustive search for information when determining whether credit risk has increased significantly since initial recognition.
  3. When an entity first applies this Standard, it may choose as its accounting policy to continue to apply the hedge accounting requirements of IAS 39 instead of the requirements in Chapter 6 of this Standard. An entity shall apply that policy to all of its hedging relationships. An entity that chooses that policy shall also apply IFRIC 16 Hedges of a Net Investment in a Foreign Operation without the amendments that conform that Interpretation to the requirements in Chapter 6 of this Standard.
  4. If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items

*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

INSIGNIA FINANCIAL LTD is assigned short-term Ba1 & long-term Ba1 estimated rating. INSIGNIA FINANCIAL LTD prediction model is evaluated with Multi-Task Learning (ML) and ElasticNet Regression1,2,3,4 and it is concluded that the IFL stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: HoldBuy

IFL INSIGNIA FINANCIAL LTD Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementB3B2
Balance SheetBaa2C
Leverage RatiosBaa2Baa2
Cash FlowCaa2B2
Rates of Return and ProfitabilityBa3Ba3

*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: 75 out of 100 with 650 signals.

References

  1. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
  2. ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. The Dow Jones Industrial Average (No. Stock Analysis). AC Investment Research.
  3. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
  4. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
  5. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
  6. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
  7. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
Frequently Asked QuestionsQ: What is the prediction methodology for IFL stock?
A: IFL stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and ElasticNet Regression
Q: Is IFL stock a buy or sell?
A: The dominant strategy among neural network is to HoldBuy IFL Stock.
Q: Is INSIGNIA FINANCIAL LTD stock a good investment?
A: The consensus rating for INSIGNIA FINANCIAL LTD is HoldBuy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of IFL stock?
A: The consensus rating for IFL is HoldBuy.
Q: What is the prediction period for IFL stock?
A: The prediction period for IFL is (n+1 year)

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.