Nowadays, the stock market's prediction is a topic that attracted researchers in the world. Stock market prediction is a process that requires a comprehensive understanding of the data stock movement and analysis it accurately. Therefore, it needs intelligent methods to deal with this task to ensure that the prediction is as correct as possible, which will return profitable benefits to investors. The main goal of this article is the employment of effective machine learning techniques to build a strong model for stock market prediction. We evaluate V2 Retail Limited prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Polynomial Regression1,2,3,4 and conclude that the NSE V2RETAIL 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 Hold NSE V2RETAIL stock.
Keywords: NSE V2RETAIL, V2 Retail Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Technical Analysis with Algorithmic Trading
- What is a prediction confidence?
- What is prediction model?
NSE V2RETAIL Target Price Prediction Modeling Methodology
Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We consider V2 Retail Limited Stock Decision Process with Polynomial Regression where A is the set of discrete actions of NSE V2RETAIL 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(Polynomial Regression)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of NSE V2RETAIL 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 V2RETAIL Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: NSE V2RETAIL V2 Retail Limited
Time series to forecast n: 02 Oct 2022 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE V2RETAIL 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
V2 Retail Limited assigned short-term Ba3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Polynomial Regression1,2,3,4 and conclude that the NSE V2RETAIL 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 Hold NSE V2RETAIL stock.
Financial State Forecast for NSE V2RETAIL Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba3 | Ba3 |
| Operational Risk | 75 | 60 |
| Market Risk | 56 | 43 |
| Technical Analysis | 88 | 83 |
| Fundamental Analysis | 72 | 57 |
| Risk Unsystematic | 38 | 77 |
Prediction Confidence Score
References
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- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- uyer, S. Whiteson, B. Bakker, and N. A. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I, pages 656–671, 2008.
Frequently Asked Questions
Q: What is the prediction methodology for NSE V2RETAIL stock?A: NSE V2RETAIL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Polynomial Regression
Q: Is NSE V2RETAIL stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE V2RETAIL Stock.
Q: Is V2 Retail Limited stock a good investment?
A: The consensus rating for V2 Retail Limited is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE V2RETAIL stock?
A: The consensus rating for NSE V2RETAIL is Hold.
Q: What is the prediction period for NSE V2RETAIL stock?
A: The prediction period for NSE V2RETAIL is (n+16 weeks)
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