Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. We evaluate Organon & Co. prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the OGN stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell OGN stock.

Keywords: OGN, Organon & Co., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. Nash Equilibria
2. Probability Distribution
3. Probability Distribution ## OGN 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 Organon & Co. Stock Decision Process with Stepwise Regression where A is the set of discrete actions of OGN 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(Stepwise Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of OGN 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?

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

Sample Set: Neural Network
Stock/Index: OGN Organon & Co.
Time series to forecast n: 13 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell OGN 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

Organon & Co. assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the OGN stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell OGN stock.

### Financial State Forecast for OGN Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 4359
Market Risk7336
Technical Analysis6566
Fundamental Analysis3861
Risk Unsystematic5160

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 463 signals.

## References

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2. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
3. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
4. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
5. 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
6. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
7. Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
Frequently Asked QuestionsQ: What is the prediction methodology for OGN stock?
A: OGN stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Stepwise Regression
Q: Is OGN stock a buy or sell?
A: The dominant strategy among neural network is to Sell OGN Stock.
Q: Is Organon & Co. stock a good investment?
A: The consensus rating for Organon & Co. is Sell and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of OGN stock?
A: The consensus rating for OGN is Sell.
Q: What is the prediction period for OGN stock?
A: The prediction period for OGN is (n+8 weeks)