Modelling A.I. in Economics

PTON Target Price Prediction (Forecast)

The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We evaluate Peloton prediction models with Modular Neural Network (CNN Layer) and Paired T-Test1,2,3,4 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 to Hold PTON stock.


Keywords: PTON, Peloton, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. What is a prediction confidence?
  2. Is now good time to invest?
  3. How do predictive algorithms actually work?

PTON Target Price Prediction Modeling Methodology

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We consider Peloton Stock Decision Process with Paired T-Test 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(Paired T-Test)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 (CNN Layer)) 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

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
Time series to forecast n: 08 Oct 2022 for (n+1 year)

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


Conclusions

Peloton assigned short-term Caa2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Paired T-Test1,2,3,4 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 to Hold PTON stock.

Financial State Forecast for PTON Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2Ba2
Operational Risk 4544
Market Risk4483
Technical Analysis3362
Fundamental Analysis5862
Risk Unsystematic4184

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 566 signals.

References

  1. M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
  2. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
  3. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
  4. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
  5. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
  6. Dimakopoulou M, Athey S, Imbens G. 2017. Estimation considerations in contextual bandits. arXiv:1711.07077 [stat.ML]
  7. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
Frequently Asked QuestionsQ: What is the prediction methodology for PTON stock?
A: PTON stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Paired T-Test
Q: Is PTON stock a buy or sell?
A: The dominant strategy among neural network is to Hold PTON Stock.
Q: Is Peloton stock a good investment?
A: The consensus rating for Peloton is Hold and assigned short-term Caa2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of PTON stock?
A: The consensus rating for PTON is Hold.
Q: What is the prediction period for PTON stock?
A: The prediction period for PTON is (n+1 year)

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