Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. ** We evaluate Page Industries Limited prediction models with Inductive Learning (ML) 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

- What are main components of Markov decision process?
- Is now good time to invest?
- How can neural networks improve predictions?

## NSE PAGEIND Target Price Prediction Modeling Methodology

Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. 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(Inductive Learning (ML)) 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: 27 Sep 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 B2 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models Inductive Learning (ML) 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* | B2 | Ba2 |

Operational Risk | 71 | 74 |

Market Risk | 55 | 65 |

Technical Analysis | 59 | 55 |

Fundamental Analysis | 45 | 58 |

Risk Unsystematic | 59 | 88 |

### Prediction Confidence Score

## References

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- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Bai J, Ng S. 2017. Principal components and regularized estimation of factor models. arXiv:1708.08137 [stat.ME]
- M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
- Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
- C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510

## Frequently Asked Questions

Q: What is the prediction methodology for NSE PAGEIND stock?A: NSE PAGEIND stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) 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 B2 & long-term Ba2 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)