Machine Learning stock prediction: NSE SUPPETRO Stock Prediction

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate Supreme Petrochem Limited prediction models with Modular Neural Network (CNN Layer) and Beta^1,2,3,4 and conclude that the NSE SUPPETRO stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE SUPPETRO stock.

Keywords: NSE SUPPETRO, Supreme Petrochem Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Probability Distribution
2. Prediction Modeling
3. What are main components of Markov decision process?

NSE SUPPETRO Target Price Prediction Modeling Methodology

Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. We consider Supreme Petrochem Limited Stock Decision Process with Beta where A is the set of discrete actions of NSE SUPPETRO 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of NSE SUPPETRO 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?

NSE SUPPETRO Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: NSE SUPPETRO Supreme Petrochem Limited
Time series to forecast n: 28 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE SUPPETRO 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

Supreme Petrochem Limited assigned short-term B3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Beta^1,2,3,4 and conclude that the NSE SUPPETRO stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE SUPPETRO stock.

Financial State Forecast for NSE SUPPETRO Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B3	Baa2
Operational Risk	30	81
Market Risk	66	77
Technical Analysis	33	72
Fundamental Analysis	75	75
Risk Unsystematic	36	80

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 730 signals.

References

1. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
2. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
3. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
4. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
5. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
6. Athey S, Blei D, Donnelly R, Ruiz F. 2017b. Counterfactual inference for consumer choice across many prod- uct categories. AEA Pap. Proc. 108:64–67
7. Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791

Frequently Asked Questions

Q: What is the prediction methodology for NSE SUPPETRO stock?
A: NSE SUPPETRO stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Beta
Q: Is NSE SUPPETRO stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE SUPPETRO Stock.
Q: Is Supreme Petrochem Limited stock a good investment?
A: The consensus rating for Supreme Petrochem Limited is Hold and assigned short-term B3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of NSE SUPPETRO stock?
A: The consensus rating for NSE SUPPETRO is Hold.
Q: What is the prediction period for NSE SUPPETRO stock?
A: The prediction period for NSE SUPPETRO is (n+16 weeks)

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