Modelling A.I. in Economics

Is now good time to invest? (NSE ADANIPORTS Stock Forecast)

Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. We evaluate Adani Ports and Special Economic Zone Limited prediction models with Reinforcement Machine Learning (ML) and Logistic Regression1,2,3,4 and conclude that the NSE ADANIPORTS stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE ADANIPORTS stock.


Keywords: NSE ADANIPORTS, Adani Ports and Special Economic Zone Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

  1. Market Outlook
  2. What are main components of Markov decision process?
  3. What statistical methods are used to analyze data?

NSE ADANIPORTS Target Price Prediction Modeling Methodology

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider Adani Ports and Special Economic Zone Limited Stock Decision Process with Logistic Regression where A is the set of discrete actions of NSE ADANIPORTS 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(Logistic Regression)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(Reinforcement Machine Learning (ML)) X S(n):→ (n+4 weeks) S = s 1 s 2 s 3

n:Time series to forecast

p:Price signals of NSE ADANIPORTS 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 ADANIPORTS Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: NSE ADANIPORTS Adani Ports and Special Economic Zone Limited
Time series to forecast n: 30 Sep 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE ADANIPORTS 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

Adani Ports and Special Economic Zone Limited assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Logistic Regression1,2,3,4 and conclude that the NSE ADANIPORTS stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE ADANIPORTS stock.

Financial State Forecast for NSE ADANIPORTS Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba3
Operational Risk 5764
Market Risk6663
Technical Analysis5463
Fundamental Analysis3560
Risk Unsystematic4081

Prediction Confidence Score

Trust metric by Neural Network: 72 out of 100 with 861 signals.

References

  1. K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
  2. P. Artzner, F. Delbaen, J. Eber, and D. Heath. Coherent measures of risk. Journal of Mathematical Finance, 9(3):203–228, 1999
  3. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
  4. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
  5. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
  6. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
  7. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
Frequently Asked QuestionsQ: What is the prediction methodology for NSE ADANIPORTS stock?
A: NSE ADANIPORTS stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Logistic Regression
Q: Is NSE ADANIPORTS stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE ADANIPORTS Stock.
Q: Is Adani Ports and Special Economic Zone Limited stock a good investment?
A: The consensus rating for Adani Ports and Special Economic Zone Limited is Hold and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE ADANIPORTS stock?
A: The consensus rating for NSE ADANIPORTS is Hold.
Q: What is the prediction period for NSE ADANIPORTS stock?
A: The prediction period for NSE ADANIPORTS is (n+4 weeks)



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