Asian Granito India Limited assigned short-term B2 & long-term Ba3 forecasted stock rating.

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We evaluate Asian Granito India Limited prediction models with Modular Neural Network (Market Volatility Analysis) and Spearman Correlation^1,2,3,4 and conclude that the NSE ASIANTILES 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 ASIANTILES stock.

Keywords: NSE ASIANTILES, Asian Granito India Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Operational Risk
2. Market Risk
3. Trading Signals

NSE ASIANTILES Target Price Prediction Modeling Methodology

In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour. We consider Asian Granito India Limited Stock Decision Process with Spearman Correlation where A is the set of discrete actions of NSE ASIANTILES 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(Spearman Correlation)^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 (Market Volatility Analysis)) X S(n):→ (n+4 weeks) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE ASIANTILES Asian Granito India 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 ASIANTILES 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

Asian Granito India Limited assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Spearman Correlation^1,2,3,4 and conclude that the NSE ASIANTILES 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 ASIANTILES stock.

Financial State Forecast for NSE ASIANTILES Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Operational Risk	61	38
Market Risk	39	88
Technical Analysis	67	81
Fundamental Analysis	90	53
Risk Unsystematic	32	56

Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 680 signals.

References

1. Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
2. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
3. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
4. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
5. Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
6. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
7. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press

Frequently Asked Questions

Q: What is the prediction methodology for NSE ASIANTILES stock?
A: NSE ASIANTILES stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Spearman Correlation
Q: Is NSE ASIANTILES stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE ASIANTILES Stock.
Q: Is Asian Granito India Limited stock a good investment?
A: The consensus rating for Asian Granito India Limited is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE ASIANTILES stock?
A: The consensus rating for NSE ASIANTILES is Hold.
Q: What is the prediction period for NSE ASIANTILES stock?
A: The prediction period for NSE ASIANTILES is (n+4 weeks)

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