How do you know when a stock will go up or down? (NSE NIITLTD Stock Forecast)

In this paper, we propose a hybrid machine learning system based on Genetic Algor ithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. We evaluate NIIT Limited prediction models with Active Learning (ML) and Multiple Regression^1,2,3,4 and conclude that the NSE NIITLTD stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to SellHold NSE NIITLTD stock.

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

Key Points

1. Buy, Sell and Hold Signals
2. Nash Equilibria
3. Operational Risk

NSE NIITLTD Target Price Prediction Modeling Methodology

Recently, numerous investigations for stock price prediction and portfolio management using machine learning have been trying to develop efficient mechanical trading systems. But these systems have a limitation in that they are mainly based on the supervised learning which is not so adequate for learning problems with long-term goals and delayed rewards. This paper proposes a method of applying reinforcement learning, suitable for modeling and learning various kinds of interactions in real situations, to the problem of stock price prediction. We consider NIIT Limited Stock Decision Process with Multiple Regression where A is the set of discrete actions of NSE NIITLTD 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(Multiple Regression)^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(Active Learning (ML)) X S(n):→ (n+16 weeks) $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of NSE NIITLTD 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 NIITLTD Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: NSE NIITLTD NIIT Limited
Time series to forecast n: 27 Sep 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to SellHold NSE NIITLTD 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

NIIT Limited assigned short-term Baa2 & long-term B1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Multiple Regression^1,2,3,4 and conclude that the NSE NIITLTD stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to SellHold NSE NIITLTD stock.

Financial State Forecast for NSE NIITLTD Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	B1
Operational Risk	78	43
Market Risk	66	38
Technical Analysis	87	63
Fundamental Analysis	86	55
Risk Unsystematic	55	83

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 765 signals.

References

1. S. Bhatnagar, H. Prasad, and L. Prashanth. Stochastic recursive algorithms for optimization, volume 434. Springer, 2013
2. M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
3. Athey S, Tibshirani J, Wager S. 2016b. Generalized random forests. arXiv:1610.01271 [stat.ME]
4. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
5. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
6. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
7. Keane MP. 2013. Panel data discrete choice models of consumer demand. In The Oxford Handbook of Panel Data, ed. BH Baltagi, pp. 54–102. Oxford, UK: Oxford Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for NSE NIITLTD stock?
A: NSE NIITLTD stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Multiple Regression
Q: Is NSE NIITLTD stock a buy or sell?
A: The dominant strategy among neural network is to SellHold NSE NIITLTD Stock.
Q: Is NIIT Limited stock a good investment?
A: The consensus rating for NIIT Limited is SellHold and assigned short-term Baa2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE NIITLTD stock?
A: The consensus rating for NSE NIITLTD is SellHold.
Q: What is the prediction period for NSE NIITLTD stock?
A: The prediction period for NSE NIITLTD is (n+16 weeks)

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