NSE HLVLTD Stock Price Prediction

The stock market has been an attractive field for a large number of organizers and investors to derive useful predictions. Fundamental knowledge of stock market can be utilised with technical indicators to investigate different perspectives of the financial market; also, the influence of various events, financial news, and/or opinions on investors' decisions and hence, market trends have been observed. Such information can be exploited to make reliable predictions and achieve higher profitability. Computational intelligence has emerged with various deep neural network (DNN) techniques to address complex stock market problems. We evaluate HLV LIMITED prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Stepwise Regression^1,2,3,4 and conclude that the NSE HLVLTD stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellHold NSE HLVLTD stock.

Keywords: NSE HLVLTD, HLV LIMITED, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. Investment Risk
2. Prediction Modeling
3. Can we predict stock market using machine learning?

NSE HLVLTD Target Price Prediction Modeling Methodology

It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. We consider HLV LIMITED Stock Decision Process with Stepwise Regression where A is the set of discrete actions of NSE HLVLTD 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(Stepwise 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 weeks) $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of NSE HLVLTD 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 HLVLTD Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: NSE HLVLTD HLV LIMITED
Time series to forecast n: 06 Nov 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellHold NSE HLVLTD 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%

Adjusted IFRS* Prediction Methods for HLV LIMITED

1. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.
2. Paragraph 4.1.1(b) requires an entity to classify a financial asset on the basis of its contractual cash flow characteristics if the financial asset is held within a business model whose objective is to hold assets to collect contractual cash flows or within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets, unless paragraph 4.1.5 applies. To do so, the condition in paragraphs 4.1.2(b) and 4.1.2A(b) requires an entity to determine whether the asset's contractual cash flows are solely payments of principal and interest on the principal amount outstanding.
3. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period
4. An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

HLV LIMITED assigned short-term Baa2 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Stepwise Regression^1,2,3,4 and conclude that the NSE HLVLTD stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to SellHold NSE HLVLTD stock.

Financial State Forecast for NSE HLVLTD HLV LIMITED Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Baa2	Ba1
Operational Risk	84	87
Market Risk	79	57
Technical Analysis	59	82
Fundamental Analysis	76	66
Risk Unsystematic	68	63

Prediction Confidence Score

Trust metric by Neural Network: 78 out of 100 with 766 signals.

References

1. D. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
2. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
3. J. Peters, S. Vijayakumar, and S. Schaal. Natural actor-critic. In Proceedings of the Sixteenth European Conference on Machine Learning, pages 280–291, 2005.
4. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
5. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
6. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
7. Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]

Frequently Asked Questions

Q: What is the prediction methodology for NSE HLVLTD stock?
A: NSE HLVLTD stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Stepwise Regression
Q: Is NSE HLVLTD stock a buy or sell?
A: The dominant strategy among neural network is to SellHold NSE HLVLTD Stock.
Q: Is HLV LIMITED stock a good investment?
A: The consensus rating for HLV LIMITED is SellHold and assigned short-term Baa2 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of NSE HLVLTD stock?
A: The consensus rating for NSE HLVLTD is SellHold.
Q: What is the prediction period for NSE HLVLTD stock?
A: The prediction period for NSE HLVLTD is (n+8 weeks)

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