Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price.** We evaluate Crompton Greaves Consumer Electricals Limited prediction models with Modular Neural Network (DNN Layer) and Logistic Regression ^{1,2,3,4} and conclude that the NSE CROMPTON 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 CROMPTON stock.**

**NSE CROMPTON, Crompton Greaves Consumer Electricals Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Investment Risk
- Understanding Buy, Sell, and Hold Ratings
- Market Risk

## NSE CROMPTON Target Price Prediction Modeling Methodology

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors' behavior. We consider Crompton Greaves Consumer Electricals Limited Stock Decision Process with Logistic Regression where A is the set of discrete actions of NSE CROMPTON 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(Logistic 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 (DNN Layer)) X S(n):→ (n+3 month) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

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

**Sample Set:**Neural Network

**Stock/Index:**NSE CROMPTON Crompton Greaves Consumer Electricals Limited

**Time series to forecast n: 26 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 CROMPTON 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

Crompton Greaves Consumer Electricals Limited assigned short-term B2 & long-term Baa2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Logistic Regression ^{1,2,3,4} and conclude that the NSE CROMPTON 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 CROMPTON stock.**

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

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

Outlook* | B2 | Baa2 |

Operational Risk | 50 | 60 |

Market Risk | 34 | 89 |

Technical Analysis | 69 | 75 |

Fundamental Analysis | 71 | 58 |

Risk Unsystematic | 51 | 83 |

### Prediction Confidence Score

## References

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- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
- Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
- C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.

## Frequently Asked Questions

Q: What is the prediction methodology for NSE CROMPTON stock?A: NSE CROMPTON stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Logistic Regression

Q: Is NSE CROMPTON stock a buy or sell?

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

Q: Is Crompton Greaves Consumer Electricals Limited stock a good investment?

A: The consensus rating for Crompton Greaves Consumer Electricals Limited is Hold and assigned short-term B2 & long-term Baa2 forecasted stock rating.

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

A: The consensus rating for NSE CROMPTON is Hold.

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

A: The prediction period for NSE CROMPTON is (n+3 month)

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