Modelling A.I. in Economics

PTON Peloton Interactive Inc. Class A Common Stock (Forecast)

Outlook: Peloton Interactive Inc. Class A Common Stock assigned short-term Ba3 & long-term Ba2 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 13 Dec 2022 for (n+1 year)
Methodology : Modular Neural Network (Financial Sentiment Analysis)

Abstract

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN.(Lee, J.W., 2001, June. Stock price prediction using reinforcement learning. In ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570) (Vol. 1, pp. 690-695). IEEE.) We evaluate Peloton Interactive Inc. Class A Common Stock prediction models with Modular Neural Network (Financial Sentiment Analysis) and ElasticNet Regression1,2,3,4 and conclude that the PTON 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

Key Points

  1. Trading Interaction
  2. What is Markov decision process in reinforcement learning?
  3. What are the most successful trading algorithms?

PTON Target Price Prediction Modeling Methodology

We consider Peloton Interactive Inc. Class A Common Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of PTON 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+1 year) i = 1 n a i

n:Time series to forecast

p:Price signals of PTON 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?

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

Sample Set: Neural Network
Stock/Index: PTON Peloton Interactive Inc. Class A Common Stock
Time series to forecast n: 13 Dec 2022 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%

Adjusted IFRS* Prediction Methods for Peloton Interactive Inc. Class A Common Stock

  1. If an entity previously accounted at cost (in accordance with IAS 39), for an investment in an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) (or for a derivative asset that is linked to and must be settled by delivery of such an equity instrument) it shall measure that instrument at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.
  2. Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.
  3. This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.
  4. Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure

*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

Peloton Interactive Inc. Class A Common Stock assigned short-term Ba3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with ElasticNet Regression1,2,3,4 and conclude that the PTON 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

Financial State Forecast for PTON Peloton Interactive Inc. Class A Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba2
Operational Risk 3990
Market Risk6758
Technical Analysis6669
Fundamental Analysis6351
Risk Unsystematic8073

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 652 signals.

References

  1. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
  2. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
  3. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
  4. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
  5. S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
  6. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
  7. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
Frequently Asked QuestionsQ: What is the prediction methodology for PTON stock?
A: PTON stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and ElasticNet Regression
Q: Is PTON stock a buy or sell?
A: The dominant strategy among neural network is to Buy PTON Stock.
Q: Is Peloton Interactive Inc. Class A Common Stock stock a good investment?
A: The consensus rating for Peloton Interactive Inc. Class A Common Stock is Buy and assigned short-term Ba3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of PTON stock?
A: The consensus rating for PTON is Buy.
Q: What is the prediction period for PTON stock?
A: The prediction period for PTON is (n+1 year)

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