How do you know when a stock will go up or down? (PTON Stock Forecast)

With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. We evaluate Peloton prediction models with Modular Neural Network (CNN Layer) and Logistic Regression^1,2,3,4 and conclude that the PTON stock is predictable in the short/long term. According to price forecasts for (n+6 month) 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. Can neural networks predict stock market?
2. Can statistics predict the future?
3. What is the best way to predict stock prices?

PTON Target Price Prediction Modeling Methodology

Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. We consider Peloton Stock Decision Process with Logistic Regression 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(Logistic Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

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+6 month)

Sample Set: Neural Network
Stock/Index: PTON Peloton
Time series to forecast n: 03 Oct 2022 for (n+6 month)

According to price forecasts for (n+6 month) 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 B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Logistic Regression^1,2,3,4 and conclude that the PTON stock is predictable in the short/long term. According to price forecasts for (n+6 month) 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*	B1	B1
Operational Risk	78	35
Market Risk	38	58
Technical Analysis	49	77
Fundamental Analysis	89	40
Risk Unsystematic	52	86

Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 866 signals.

References

1. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
2. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
3. M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
4. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
5. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
6. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
7. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65

Frequently Asked Questions

Q: What is the prediction methodology for PTON stock?
A: PTON stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Logistic Regression
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 B1 & long-term B1 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+6 month)

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