OC Target Price Forecast

The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. We evaluate Owens Corning prediction models with Modular Neural Network (Market Direction Analysis) and Lasso Regression^1,2,3,4 and conclude that the OC 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 OC stock.

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

Key Points

1. What are buy sell or hold recommendations?
2. Market Outlook
3. How do you know when a stock will go up or down?

OC Target Price Prediction Modeling Methodology

In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We consider Owens Corning Stock Decision Process with Lasso Regression where A is the set of discrete actions of OC 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(Lasso 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 (Market Direction Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of OC 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?

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

Sample Set: Neural Network
Stock/Index: OC Owens Corning
Time series to forecast n: 21 Oct 2022 for (n+1 year)

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

Owens Corning assigned short-term Ba2 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Lasso Regression^1,2,3,4 and conclude that the OC 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 OC stock.

Financial State Forecast for OC Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba2	B2
Operational Risk	59	44
Market Risk	84	30
Technical Analysis	68	73
Fundamental Analysis	73	65
Risk Unsystematic	58	40

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 861 signals.

References

1. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
2. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
3. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
4. R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
5. Morris CN. 1983. Parametric empirical Bayes inference: theory and applications. J. Am. Stat. Assoc. 78:47–55
6. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
7. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]

Frequently Asked Questions

Q: What is the prediction methodology for OC stock?
A: OC stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Lasso Regression
Q: Is OC stock a buy or sell?
A: The dominant strategy among neural network is to Hold OC Stock.
Q: Is Owens Corning stock a good investment?
A: The consensus rating for Owens Corning is Hold and assigned short-term Ba2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of OC stock?
A: The consensus rating for OC is Hold.
Q: What is the prediction period for OC stock?
A: The prediction period for OC is (n+1 year)

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