Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. We evaluate Quick Heal Technologies Limited prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the NSE QUICKHEAL stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE QUICKHEAL stock.

Keywords: NSE QUICKHEAL, Quick Heal Technologies 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 is Markov decision process in reinforcement learning?
3. What is the use of Markov decision process?

## NSE QUICKHEAL Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider Quick Heal Technologies Limited Stock Decision Process with Spearman Correlation where A is the set of discrete actions of NSE QUICKHEAL 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ (n+8 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of NSE QUICKHEAL 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 QUICKHEAL Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: NSE QUICKHEAL Quick Heal Technologies Limited
Time series to forecast n: 03 Oct 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE QUICKHEAL 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

Quick Heal Technologies Limited assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Spearman Correlation1,2,3,4 and conclude that the NSE QUICKHEAL stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE QUICKHEAL stock.

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

Rating Short-Term Long-Term Senior
Outlook*Baa2B1
Operational Risk 8377
Market Risk8850
Technical Analysis3457
Fundamental Analysis7666
Risk Unsystematic8635

### Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 515 signals.

## References

1. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
2. Abadie A, Cattaneo MD. 2018. Econometric methods for program evaluation. Annu. Rev. Econ. 10:465–503
3. T. Morimura, M. Sugiyama, M. Kashima, H. Hachiya, and T. Tanaka. Nonparametric return distribution ap- proximation for reinforcement learning. In Proceedings of the 27th International Conference on Machine Learning, pages 799–806, 2010
4. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
5. Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
6. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
7. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
Frequently Asked QuestionsQ: What is the prediction methodology for NSE QUICKHEAL stock?
A: NSE QUICKHEAL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Spearman Correlation
Q: Is NSE QUICKHEAL stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE QUICKHEAL Stock.
Q: Is Quick Heal Technologies Limited stock a good investment?
A: The consensus rating for Quick Heal Technologies Limited is Hold and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE QUICKHEAL stock?
A: The consensus rating for NSE QUICKHEAL is Hold.
Q: What is the prediction period for NSE QUICKHEAL stock?
A: The prediction period for NSE QUICKHEAL is (n+8 weeks)