One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. We evaluate Granules India Limited prediction models with Modular Neural Network (Market Volatility Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NSE GRANULES stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE GRANULES stock.

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

## Key Points

1. Can statistics predict the future?
2. Stock Forecast Based On a Predictive Algorithm
3. What is Markov decision process in reinforcement learning?

## NSE GRANULES Target Price Prediction Modeling Methodology

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We consider Granules India Limited Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of NSE GRANULES 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(Wilcoxon Rank-Sum Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+3 month) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of NSE GRANULES 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 GRANULES Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: NSE GRANULES Granules India Limited
Time series to forecast n: 27 Sep 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE GRANULES 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

Granules India Limited assigned short-term Caa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NSE GRANULES stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE GRANULES stock.

### Financial State Forecast for NSE GRANULES Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Caa2B1
Operational Risk 3053
Market Risk4047
Technical Analysis4138
Fundamental Analysis4573
Risk Unsystematic4483

### Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 802 signals.

## References

1. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
2. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
3. Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
4. K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012
5. uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
6. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
7. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
Frequently Asked QuestionsQ: What is the prediction methodology for NSE GRANULES stock?
A: NSE GRANULES stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Wilcoxon Rank-Sum Test
Q: Is NSE GRANULES stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE GRANULES Stock.
Q: Is Granules India Limited stock a good investment?
A: The consensus rating for Granules India Limited is Hold and assigned short-term Caa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE GRANULES stock?
A: The consensus rating for NSE GRANULES is Hold.
Q: What is the prediction period for NSE GRANULES stock?
A: The prediction period for NSE GRANULES is (n+3 month)