NSE PHOENIXLTD Target Price Prediction (Forecast)

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. We evaluate The Phoenix Mills Limited prediction models with Multi-Task Learning (ML) and Linear Regression^1,2,3,4 and conclude that the NSE PHOENIXLTD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE PHOENIXLTD stock.

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

Key Points

1. Stock Forecast Based On a Predictive Algorithm
2. Reaction Function
3. Is now good time to invest?

NSE PHOENIXLTD Target Price Prediction Modeling Methodology

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. We consider The Phoenix Mills Limited Stock Decision Process with Linear Regression where A is the set of discrete actions of NSE PHOENIXLTD 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(Linear 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(Multi-Task Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of NSE PHOENIXLTD 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 PHOENIXLTD Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NSE PHOENIXLTD The Phoenix Mills Limited
Time series to forecast n: 28 Sep 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE PHOENIXLTD 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

The Phoenix Mills Limited assigned short-term B2 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Task Learning (ML) with Linear Regression^1,2,3,4 and conclude that the NSE PHOENIXLTD stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE PHOENIXLTD stock.

Financial State Forecast for NSE PHOENIXLTD Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B2
Operational Risk	73	32
Market Risk	53	81
Technical Analysis	30	54
Fundamental Analysis	32	44
Risk Unsystematic	84	45

Prediction Confidence Score

Trust metric by Neural Network: 86 out of 100 with 874 signals.

References

1. Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
2. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
3. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
4. Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
5. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
6. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
7. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32

Frequently Asked Questions

Q: What is the prediction methodology for NSE PHOENIXLTD stock?
A: NSE PHOENIXLTD stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Linear Regression
Q: Is NSE PHOENIXLTD stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE PHOENIXLTD Stock.
Q: Is The Phoenix Mills Limited stock a good investment?
A: The consensus rating for The Phoenix Mills Limited is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE PHOENIXLTD stock?
A: The consensus rating for NSE PHOENIXLTD is Hold.
Q: What is the prediction period for NSE PHOENIXLTD stock?
A: The prediction period for NSE PHOENIXLTD is (n+1 year)

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