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 evaluate OG&E prediction models with Modular Neural Network (Market Volatility Analysis) and Beta1,2,3,4 and conclude that the OGE 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 OGE stock.

Keywords: OGE, OG&E, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do you decide buy or sell a stock?
3. Trust metric by Neural Network ## OGE Target Price Prediction Modeling Methodology

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. We consider OG&E Stock Decision Process with Beta where A is the set of discrete actions of OGE 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(Beta)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 (Market Volatility Analysis)) X S(n):→ (n+1 year) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of OGE 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?

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

Sample Set: Neural Network
Stock/Index: OGE OG&E
Time series to forecast n: 18 Sep 2022 for (n+1 year)

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

OG&E assigned short-term B1 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Beta1,2,3,4 and conclude that the OGE 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 OGE stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1Baa2
Operational Risk 7285
Market Risk7184
Technical Analysis6947
Fundamental Analysis4187
Risk Unsystematic5986

### Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 746 signals.

## References

1. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
2. Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
3. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
4. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
5. Bottomley, P. R. Fildes (1998), "The role of prices in models of innovation diffusion," Journal of Forecasting, 17, 539–555.
6. Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
7. Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
Frequently Asked QuestionsQ: What is the prediction methodology for OGE stock?
A: OGE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Beta
Q: Is OGE stock a buy or sell?
A: The dominant strategy among neural network is to Hold OGE Stock.
Q: Is OG&E stock a good investment?
A: The consensus rating for OG&E is Hold and assigned short-term B1 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of OGE stock?
A: The consensus rating for OGE is Hold.
Q: What is the prediction period for OGE stock?
A: The prediction period for OGE is (n+1 year)