NSE KEI Target Price Forecast

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We evaluate KEI Industries Limited prediction models with Modular Neural Network (DNN Layer) and Sign Test^1,2,3,4 and conclude that the NSE KEI 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 KEI stock.

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

Key Points

1. Can we predict stock market using machine learning?
2. Reaction Function
3. What is the best way to predict stock prices?

NSE KEI Target Price Prediction Modeling Methodology

The stock market prediction has attracted much attention from academia as well as business. Due to the non-linear, volatile and complex nature of the market, it is quite difficult to predict. As the stock markets grow bigger, more investors pay attention to develop a systematic approach to predict the stock market. We consider KEI Industries Limited Stock Decision Process with Sign Test where A is the set of discrete actions of NSE KEI 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (DNN Layer)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE KEI KEI 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 KEI 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

KEI Industries Limited assigned short-term Ba3 & long-term B1 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 KEI 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 KEI stock.

Financial State Forecast for NSE KEI Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Operational Risk	72	73
Market Risk	46	60
Technical Analysis	48	84
Fundamental Analysis	71	50
Risk Unsystematic	83	36

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 484 signals.

References

1. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
2. V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
3. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
4. O. Bardou, N. Frikha, and G. Pag`es. Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte Carlo Methods and Applications, 15(3):173–210, 2009.
5. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
6. E. van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.
7. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]

Frequently Asked Questions

Q: What is the prediction methodology for NSE KEI stock?
A: NSE KEI stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Sign Test
Q: Is NSE KEI stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE KEI Stock.
Q: Is KEI Industries Limited stock a good investment?
A: The consensus rating for KEI Industries Limited is Hold and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE KEI stock?
A: The consensus rating for NSE KEI is Hold.
Q: What is the prediction period for NSE KEI stock?
A: The prediction period for NSE KEI is (n+6 month)

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