Modelling A.I. in Economics

OSCR Oscar Health Inc. Class A Common Stock (Forecast)

Outlook: Oscar Health Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : SellBuy
Time series to forecast n: 01 May 2023 for (n+8 weeks)
Methodology : Ensemble Learning (ML)

Abstract

Oscar Health Inc. Class A Common Stock prediction model is evaluated with Ensemble Learning (ML) and Spearman Correlation1,2,3,4 and it is concluded that the OSCR stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: SellBuy

Key Points

  1. What are main components of Markov decision process?
  2. What statistical methods are used to analyze data?
  3. Stock Forecast Based On a Predictive Algorithm

OSCR Target Price Prediction Modeling Methodology

We consider Oscar Health Inc. Class A Common Stock Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of OSCR 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(Spearman Correlation)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) e x rx

n:Time series to forecast

p:Price signals of OSCR 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?

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

Sample Set: Neural Network
Stock/Index: OSCR Oscar Health Inc. Class A Common Stock
Time series to forecast n: 01 May 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: SellBuy

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 Oscar Health Inc. Class A Common Stock

  1. 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.
  2. 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.
  3. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
  4. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.

*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

Oscar Health Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Oscar Health Inc. Class A Common Stock prediction model is evaluated with Ensemble Learning (ML) and Spearman Correlation1,2,3,4 and it is concluded that the OSCR stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: SellBuy

OSCR Oscar Health Inc. Class A Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2Baa2
Balance SheetBaa2Baa2
Leverage RatiosBa2B3
Cash FlowCBaa2
Rates of Return and ProfitabilityBaa2Ba3

*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 572 signals.

References

  1. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
  2. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
  3. M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
  4. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
  5. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
  6. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
  7. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
Frequently Asked QuestionsQ: What is the prediction methodology for OSCR stock?
A: OSCR stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Spearman Correlation
Q: Is OSCR stock a buy or sell?
A: The dominant strategy among neural network is to SellBuy OSCR Stock.
Q: Is Oscar Health Inc. Class A Common Stock stock a good investment?
A: The consensus rating for Oscar Health Inc. Class A Common Stock is SellBuy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OSCR stock?
A: The consensus rating for OSCR is SellBuy.
Q: What is the prediction period for OSCR stock?
A: The prediction period for OSCR is (n+8 weeks)

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