Modelling A.I. in Economics

How accurate is machine learning in stock market? (NSE APOLLOTYRE Stock Forecast) (Forecast)

Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate Apollo Tyres Limited prediction models with Modular Neural Network (Financial Sentiment Analysis) and ElasticNet Regression1,2,3,4 and conclude that the NSE APOLLOTYRE 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 NSE APOLLOTYRE stock.


Keywords: NSE APOLLOTYRE, Apollo Tyres Limited, 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 know when a stock will go up or down?
  2. Market Outlook
  3. How useful are statistical predictions?

NSE APOLLOTYRE Target Price Prediction Modeling Methodology

This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model. We consider Apollo Tyres Limited Stock Decision Process with ElasticNet Regression where A is the set of discrete actions of NSE APOLLOTYRE 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(ElasticNet 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(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+1 year) i = 1 n s i

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE APOLLOTYRE Apollo Tyres Limited
Time series to forecast n: 28 Sep 2022 for (n+1 year)

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

Apollo Tyres Limited assigned short-term Ba3 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with ElasticNet Regression1,2,3,4 and conclude that the NSE APOLLOTYRE 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 NSE APOLLOTYRE stock.

Financial State Forecast for NSE APOLLOTYRE Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba2
Operational Risk 3982
Market Risk8080
Technical Analysis6484
Fundamental Analysis7648
Risk Unsystematic7247

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 673 signals.

References

  1. A. Tamar and S. Mannor. Variance adjusted actor critic algorithms. arXiv preprint arXiv:1310.3697, 2013.
  2. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
  3. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
  4. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
  5. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
  6. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
  7. 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
Frequently Asked QuestionsQ: What is the prediction methodology for NSE APOLLOTYRE stock?
A: NSE APOLLOTYRE stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and ElasticNet Regression
Q: Is NSE APOLLOTYRE stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE APOLLOTYRE Stock.
Q: Is Apollo Tyres Limited stock a good investment?
A: The consensus rating for Apollo Tyres Limited is Hold and assigned short-term Ba3 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of NSE APOLLOTYRE stock?
A: The consensus rating for NSE APOLLOTYRE is Hold.
Q: What is the prediction period for NSE APOLLOTYRE stock?
A: The prediction period for NSE APOLLOTYRE 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.