Short/Long Term Stocks: NSE NH Stock Forecast

Recurrent Neural Networks (RNNs) is a sub type of neural networks that use feedback connections. Several types of RNN models are used in predicting financial time series. This study was conducted to develop models to predict daily stock prices based on Recurrent Neural Network (RNN) Approach and to measure the accuracy of the models developed and identify the shortcomings of the models if present. We evaluate Narayana Hrudayalaya Ltd. prediction models with Ensemble Learning (ML) and Beta^1,2,3,4 and conclude that the NSE NH 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 Sell NSE NH stock.

Keywords: NSE NH, Narayana Hrudayalaya Ltd., stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What statistical methods are used to analyze data?
2. Technical Analysis with Algorithmic Trading
3. How useful are statistical predictions?

NSE NH Target Price Prediction Modeling Methodology

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks' historical data. Most of these existing approaches have focused on short term prediction using stocks' historical price and technical indicators. We consider Narayana Hrudayalaya Ltd. Stock Decision Process with Beta where A is the set of discrete actions of NSE NH 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(Beta)^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(Ensemble Learning (ML)) X S(n):→ (n+6 month) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE NH Narayana Hrudayalaya Ltd.
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 Sell NSE NH 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

Narayana Hrudayalaya Ltd. assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Beta^1,2,3,4 and conclude that the NSE NH 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 Sell NSE NH stock.

Financial State Forecast for NSE NH Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Operational Risk	37	74
Market Risk	70	37
Technical Analysis	48	52
Fundamental Analysis	84	79
Risk Unsystematic	59	52

Prediction Confidence Score

Trust metric by Neural Network: 82 out of 100 with 633 signals.

References

1. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
2. Wager S, Athey S. 2017. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113:1228–42
3. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
4. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
5. Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
6. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
7. J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.

Frequently Asked Questions

Q: What is the prediction methodology for NSE NH stock?
A: NSE NH stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Beta
Q: Is NSE NH stock a buy or sell?
A: The dominant strategy among neural network is to Sell NSE NH Stock.
Q: Is Narayana Hrudayalaya Ltd. stock a good investment?
A: The consensus rating for Narayana Hrudayalaya Ltd. is Sell and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE NH stock?
A: The consensus rating for NSE NH is Sell.
Q: What is the prediction period for NSE NH stock?
A: The prediction period for NSE NH is (n+6 month)

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