Short-term trading is a difficult task due to fluctuating demand and supply in the stock market. These demands and supply are reflected in stock prices. The stock prices may be predicted using technical indicators. Most of the existing literature considered the limited technical indicators to measure short-term prices. We have considered 82 different combinations of technical indicators to predict the stock prices. ** We evaluate Page Industries Limited prediction models with Modular Neural Network (DNN Layer) and Sign Test ^{1,2,3,4} and conclude that the NSE PAGEIND stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE PAGEIND stock.**

**NSE PAGEIND, Page Industries Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Why do we need predictive models?
- Can neural networks predict stock market?
- Market Outlook

## NSE PAGEIND Target Price Prediction Modeling Methodology

Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We consider Page Industries Limited Stock Decision Process with Sign Test where A is the set of discrete actions of NSE PAGEIND 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(Sign Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+6 month) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of NSE PAGEIND 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 PAGEIND Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**NSE PAGEIND Page Industries Limited

**Time series to forecast n: 02 Oct 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE PAGEIND 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

Page Industries Limited assigned short-term B1 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Sign Test ^{1,2,3,4} and conclude that the NSE PAGEIND stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE PAGEIND stock.**

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

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B1 | B2 |

Operational Risk | 44 | 37 |

Market Risk | 84 | 33 |

Technical Analysis | 84 | 54 |

Fundamental Analysis | 53 | 62 |

Risk Unsystematic | 30 | 64 |

### Prediction Confidence Score

## References

- Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
- Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
- Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
- Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press
- 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 PAGEIND stock?A: NSE PAGEIND stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Sign Test

Q: Is NSE PAGEIND stock a buy or sell?

A: The dominant strategy among neural network is to Hold NSE PAGEIND Stock.

Q: Is Page Industries Limited stock a good investment?

A: The consensus rating for Page Industries Limited is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of NSE PAGEIND stock?

A: The consensus rating for NSE PAGEIND is Hold.

Q: What is the prediction period for NSE PAGEIND stock?

A: The prediction period for NSE PAGEIND is (n+6 month)

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