Modelling A.I. in Economics

How Do You Pick a Stock? (NSE JSWENERGY Stock Forecast)

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We evaluate JSW Energy Limited prediction models with Transfer Learning (ML) and Pearson Correlation1,2,3,4 and conclude that the NSE JSWENERGY 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 Buy NSE JSWENERGY stock.


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

Key Points

  1. How useful are statistical predictions?
  2. What are the most successful trading algorithms?
  3. Probability Distribution

NSE JSWENERGY Target Price Prediction Modeling Methodology

The research reported in the paper focuses on the stock market prediction problem, the main aim being the development of a methodology to forecast the stock closing price. The methodology is based on some novel variable selection methods and an analysis of neural network and support vector machines based prediction models. Also, a hybrid approach which combines the use of the variables derived from technical and fundamental analysis of stock market indicators in order to improve prediction results of the proposed approaches is reported in this paper. We consider JSW Energy Limited Stock Decision Process with Pearson Correlation where A is the set of discrete actions of NSE JSWENERGY 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(Pearson Correlation)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Transfer Learning (ML)) X S(n):→ (n+1 year) i = 1 n s i

n:Time series to forecast

p:Price signals of NSE JSWENERGY 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 JSWENERGY Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NSE JSWENERGY JSW Energy Limited
Time series to forecast n: 27 Sep 2022 for (n+1 year)

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

JSW Energy Limited assigned short-term Ba2 & long-term B1 forecasted stock rating. We evaluate the prediction models Transfer Learning (ML) with Pearson Correlation1,2,3,4 and conclude that the NSE JSWENERGY 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 Buy NSE JSWENERGY stock.

Financial State Forecast for NSE JSWENERGY Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba2B1
Operational Risk 7762
Market Risk7086
Technical Analysis6457
Fundamental Analysis3831
Risk Unsystematic9058

Prediction Confidence Score

Trust metric by Neural Network: 80 out of 100 with 586 signals.

References

  1. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
  2. Li L, Chu W, Langford J, Moon T, Wang X. 2012. An unbiased offline evaluation of contextual bandit algo- rithms with generalized linear models. In Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 297–306. New York: ACM
  3. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
  4. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
  5. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
  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. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
Frequently Asked QuestionsQ: What is the prediction methodology for NSE JSWENERGY stock?
A: NSE JSWENERGY stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Pearson Correlation
Q: Is NSE JSWENERGY stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSE JSWENERGY Stock.
Q: Is JSW Energy Limited stock a good investment?
A: The consensus rating for JSW Energy Limited is Buy and assigned short-term Ba2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE JSWENERGY stock?
A: The consensus rating for NSE JSWENERGY is Buy.
Q: What is the prediction period for NSE JSWENERGY stock?
A: The prediction period for NSE JSWENERGY is (n+1 year)

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.