Modelling A.I. in Economics

PGYWW Pagaya Technologies Ltd. Warrants (Forecast)

Pagaya Technologies Ltd. Warrants Research Report

Summary

Predicting stock index with traditional time series analysis has proven to be difficult an Artificial Neural network may be suitable for the task. A Neural Network has the ability to extract useful information from large set of data. This paper presents a review of literature application of Artificial Neural Network for stock market predictions and from this literature found that Artificial Neural Network is very useful for predicting world stock markets. We evaluate Pagaya Technologies Ltd. Warrants prediction models with Ensemble Learning (ML) and Stepwise Regression1,2,3,4 and conclude that the PGYWW stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold PGYWW stock.

Key Points

  1. How do you know when a stock will go up or down?
  2. Why do we need predictive models?
  3. What is Markov decision process in reinforcement learning?

PGYWW Target Price Prediction Modeling Methodology

We consider Pagaya Technologies Ltd. Warrants Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of PGYWW 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(Ensemble Learning (ML)) X S(n):→ (n+8 weeks) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of PGYWW 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?

PGYWW Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: PGYWW Pagaya Technologies Ltd. Warrants
Time series to forecast n: 03 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold PGYWW stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Pagaya Technologies Ltd. Warrants

  1. When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.
  2. An entity shall assess whether contractual cash flows are solely payments of principal and interest on the principal amount outstanding for the currency in which the financial asset is denominated.
  3. However, an entity is not required to separately recognise interest revenue or impairment gains or losses for a financial asset measured at fair value through profit or loss. Consequently, when an entity reclassifies a financial asset out of the fair value through profit or loss measurement category, the effective interest rate is determined on the basis of the fair value of the asset at the reclassification date. In addition, for the purposes of applying Section 5.5 to the financial asset from the reclassification date, the date of the reclassification is treated as the date of initial recognition.
  4. If any instrument in the pool does not meet the conditions in either paragraph B4.1.23 or paragraph B4.1.24, the condition in paragraph B4.1.21(b) is not met. In performing this assessment, a detailed instrument-byinstrument analysis of the pool may not be necessary. However, an entity must use judgement and perform sufficient analysis to determine whether the instruments in the pool meet the conditions in paragraphs B4.1.23–B4.1.24. (See also paragraph B4.1.18 for guidance on contractual cash flow characteristics that have only a de minimis effect.)

*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

Pagaya Technologies Ltd. Warrants assigned short-term Ba1 & long-term B2 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Stepwise Regression1,2,3,4 and conclude that the PGYWW stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold PGYWW stock.

Financial State Forecast for PGYWW Pagaya Technologies Ltd. Warrants Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba1B2
Operational Risk 8145
Market Risk7169
Technical Analysis8359
Fundamental Analysis8055
Risk Unsystematic4435

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 477 signals.

References

  1. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
  2. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
  3. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
  4. T. Shardlow and A. Stuart. A perturbation theory for ergodic Markov chains and application to numerical approximations. SIAM journal on numerical analysis, 37(4):1120–1137, 2000
  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. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
  7. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
Frequently Asked QuestionsQ: What is the prediction methodology for PGYWW stock?
A: PGYWW stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Stepwise Regression
Q: Is PGYWW stock a buy or sell?
A: The dominant strategy among neural network is to Hold PGYWW Stock.
Q: Is Pagaya Technologies Ltd. Warrants stock a good investment?
A: The consensus rating for Pagaya Technologies Ltd. Warrants is Hold and assigned short-term Ba1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of PGYWW stock?
A: The consensus rating for PGYWW is Hold.
Q: What is the prediction period for PGYWW stock?
A: The prediction period for PGYWW is (n+8 weeks)

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