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 ON Semiconductor prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Chi-Square1,2,3,4 and conclude that the ON 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 ON stock.

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

## Key Points

1. Is now good time to invest?
2. Can neural networks predict stock market?
3. Technical Analysis with Algorithmic Trading

## ON Target Price Prediction Modeling Methodology

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We consider ON Semiconductor Stock Decision Process with Chi-Square where A is the set of discrete actions of ON 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(Chi-Square)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 (Social Media Sentiment Analysis)) X S(n):→ (n+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ON 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?

## ON Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: ON ON Semiconductor
Time series to forecast n: 12 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold ON 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

ON Semiconductor assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Chi-Square1,2,3,4 and conclude that the ON 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 ON stock.

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

Rating Short-Term Long-Term Senior
Outlook*B1B1
Operational Risk 5750
Market Risk5435
Technical Analysis3371
Fundamental Analysis8056
Risk Unsystematic8981

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 602 signals.

## References

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2. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
3. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
4. Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
5. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
6. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
7. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
Frequently Asked QuestionsQ: What is the prediction methodology for ON stock?
A: ON stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Chi-Square
Q: Is ON stock a buy or sell?
A: The dominant strategy among neural network is to Hold ON Stock.
Q: Is ON Semiconductor stock a good investment?
A: The consensus rating for ON Semiconductor is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of ON stock?
A: The consensus rating for ON is Hold.
Q: What is the prediction period for ON stock?
A: The prediction period for ON is (n+6 month)