Honda Siel Power Products Limited Stock Forecast, Price & Rating (NSE HONDAPOWER)

Honda Siel Power Products Limited Research Report

Summary

Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We evaluate Honda Siel Power Products Limited prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression^1,2,3,4 and conclude that the NSE HONDAPOWER 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 Sell NSE HONDAPOWER stock.

Key Points

1. How useful are statistical predictions?
2. Dominated Move
3. Prediction Modeling

NSE HONDAPOWER Target Price Prediction Modeling Methodology

We consider Honda Siel Power Products Limited Stock Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of NSE HONDAPOWER 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(Ridge 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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of NSE HONDAPOWER stock

j:Nash equilibria (Neural Network)

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 HONDAPOWER Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NSE HONDAPOWER Honda Siel Power Products Limited
Time series to forecast n: 20 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Sell NSE HONDAPOWER 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 Honda Siel Power Products Limited

1. The assessment of whether lifetime expected credit losses should be recognised is based on significant increases in the likelihood or risk of a default occurring since initial recognition (irrespective of whether a financial instrument has been repriced to reflect an increase in credit risk) instead of on evidence of a financial asset being credit-impaired at the reporting date or an actual default occurring. Generally, there will be a significant increase in credit risk before a financial asset becomes credit-impaired or an actual default occurs.
2. Subject to the conditions in paragraphs 4.1.5 and 4.2.2, this Standard allows an entity to designate a financial asset, a financial liability, or a group of financial instruments (financial assets, financial liabilities or both) as at fair value through profit or loss provided that doing so results in more relevant information.
3. If changes are made in addition to those changes required by interest rate benchmark reform to the financial asset or financial liability designated in a hedging relationship (as described in paragraphs 5.4.6–5.4.8) or to the designation of the hedging relationship (as required by paragraph 6.9.1), an entity shall first apply the applicable requirements in this Standard to determine if those additional changes result in the discontinuation of hedge accounting. If the additional changes do not result in the discontinuation of hedge accounting, an entity shall amend the formal designation of the hedging relationship as specified in paragraph 6.9.1.
4. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.

*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

Honda Siel Power Products Limited assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Ridge Regression^1,2,3,4 and conclude that the NSE HONDAPOWER 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 Sell NSE HONDAPOWER stock.

Financial State Forecast for NSE HONDAPOWER Honda Siel Power Products Limited Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Operational Risk	65	83
Market Risk	59	48
Technical Analysis	44	85
Fundamental Analysis	48	33
Risk Unsystematic	80	65

Prediction Confidence Score

Trust metric by Neural Network: 91 out of 100 with 624 signals.

References

1. Andrews, D. W. K. W. Ploberger (1994), "Optimal tests when a nuisance parameter is present only under the alternative," Econometrica, 62, 1383–1414.
2. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
3. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
4. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
5. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
6. Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
7. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.

Frequently Asked Questions

Q: What is the prediction methodology for NSE HONDAPOWER stock?
A: NSE HONDAPOWER stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Ridge Regression
Q: Is NSE HONDAPOWER stock a buy or sell?
A: The dominant strategy among neural network is to Sell NSE HONDAPOWER Stock.
Q: Is Honda Siel Power Products Limited stock a good investment?
A: The consensus rating for Honda Siel Power Products Limited is Sell and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE HONDAPOWER stock?
A: The consensus rating for NSE HONDAPOWER is Sell.
Q: What is the prediction period for NSE HONDAPOWER stock?
A: The prediction period for NSE HONDAPOWER is (n+1 year)

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