Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate Tata Power Company Limited prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the NSE TATAPOWER 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 TATAPOWER stock.

Keywords: NSE TATAPOWER, Tata Power Company Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

## Key Points

1. How do you decide buy or sell a stock?
2. What are buy sell or hold recommendations?
3. Can neural networks predict stock market?

## NSE TATAPOWER Target Price Prediction Modeling Methodology

Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. We consider Tata Power Company Limited Stock Decision Process with Spearman Correlation where A is the set of discrete actions of NSE TATAPOWER 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+6 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE TATAPOWER Tata Power Company 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 TATAPOWER 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

Tata Power Company Limited assigned short-term B2 & 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 TATAPOWER 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 TATAPOWER stock.

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

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 8956
Market Risk4657
Technical Analysis3965
Fundamental Analysis3734
Risk Unsystematic7186

### Prediction Confidence Score

Trust metric by Neural Network: 76 out of 100 with 784 signals.

## References

1. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
2. Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
3. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
4. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
5. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67:301–20
6. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
7. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
Frequently Asked QuestionsQ: What is the prediction methodology for NSE TATAPOWER stock?
A: NSE TATAPOWER stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Spearman Correlation
Q: Is NSE TATAPOWER stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE TATAPOWER Stock.
Q: Is Tata Power Company Limited stock a good investment?
A: The consensus rating for Tata Power Company Limited is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE TATAPOWER stock?
A: The consensus rating for NSE TATAPOWER is Hold.
Q: What is the prediction period for NSE TATAPOWER stock?
A: The prediction period for NSE TATAPOWER is (n+6 month)

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