This study presents financial network indicators that can be applied to global stock market investment strategies. We propose to design both undirected and directed volatility networks of global stock market based on simple pair-wise correlation and system-wide connectedness of stock date using a vector auto-regressive model.** We evaluate CESC Limited prediction models with Modular Neural Network (Financial Sentiment Analysis) and Stepwise Regression ^{1,2,3,4} and conclude that the NSE CESC stock is predictable in the short/long term. **

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE CESC stock.**

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

*Keywords:*## Key Points

- Understanding Buy, Sell, and Hold Ratings
- Which neural network is best for prediction?
- How do predictive algorithms actually work?

## NSE CESC Target Price Prediction Modeling Methodology

Nowadays, the stock market's prediction is a topic that attracted researchers in the world. Stock market prediction is a process that requires a comprehensive understanding of the data stock movement and analysis it accurately. Therefore, it needs intelligent methods to deal with this task to ensure that the prediction is as correct as possible, which will return profitable benefits to investors. The main goal of this article is the employment of effective machine learning techniques to build a strong model for stock market prediction. We consider CESC Limited Stock Decision Process with Stepwise Regression where A is the set of discrete actions of NSE CESC 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(Stepwise Regression)

^{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 (Financial Sentiment Analysis)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of NSE CESC 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 CESC Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**NSE CESC CESC Limited

**Time series to forecast n: 29 Sep 2022**for (n+1 year)

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE CESC 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

CESC Limited assigned short-term Ba3 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Stepwise Regression ^{1,2,3,4} and conclude that the NSE CESC stock is predictable in the short/long term.**

**According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE CESC stock.**

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

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

Outlook* | Ba3 | B1 |

Operational Risk | 88 | 47 |

Market Risk | 69 | 57 |

Technical Analysis | 42 | 72 |

Fundamental Analysis | 57 | 75 |

Risk Unsystematic | 61 | 45 |

### Prediction Confidence Score

## References

- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
- Doudchenko N, Imbens GW. 2016. Balancing, regression, difference-in-differences and synthetic control methods: a synthesis. NBER Work. Pap. 22791
- S. Bhatnagar and K. Lakshmanan. An online actor-critic algorithm with function approximation for con- strained Markov decision processes. Journal of Optimization Theory and Applications, 153(3):688–708, 2012.
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- R. Sutton and A. Barto. Reinforcement Learning. The MIT Press, 1998
- Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press

## Frequently Asked Questions

Q: What is the prediction methodology for NSE CESC stock?A: NSE CESC stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Stepwise Regression

Q: Is NSE CESC stock a buy or sell?

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

Q: Is CESC Limited stock a good investment?

A: The consensus rating for CESC Limited is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.

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

A: The consensus rating for NSE CESC is Hold.

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

A: The prediction period for NSE CESC is (n+1 year)

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