Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. We evaluate Hindustan Zinc Limited prediction models with Deductive Inference (ML) and Polynomial Regression1,2,3,4 and conclude that the NSE HINDZINC stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE HINDZINC stock.
Keywords: NSE HINDZINC, Hindustan Zinc Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Probability Distribution
- Operational Risk
- Market Signals
NSE HINDZINC Target Price Prediction Modeling Methodology
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. We consider Hindustan Zinc Limited Stock Decision Process with Polynomial Regression where A is the set of discrete actions of NSE HINDZINC 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(Deductive Inference (ML)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of NSE HINDZINC stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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NSE HINDZINC Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: NSE HINDZINC Hindustan Zinc Limited
Time series to forecast n: 03 Oct 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE HINDZINC 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
Hindustan Zinc Limited assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Polynomial Regression1,2,3,4 and conclude that the NSE HINDZINC stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE HINDZINC stock.
Financial State Forecast for NSE HINDZINC Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba1 |
| Operational Risk | 34 | 80 |
| Market Risk | 72 | 74 |
| Technical Analysis | 34 | 67 |
| Fundamental Analysis | 78 | 65 |
| Risk Unsystematic | 40 | 68 |
Prediction Confidence Score
References
- Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I. 2018b. Generic machine learning inference on heteroge- nous treatment effects in randomized experiments. NBER Work. Pap. 24678
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
- J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
- Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
- Athey S. 2019. The impact of machine learning on economics. In The Economics of Artificial Intelligence: An Agenda, ed. AK Agrawal, J Gans, A Goldfarb. Chicago: Univ. Chicago Press. In press
- Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
Frequently Asked Questions
Q: What is the prediction methodology for NSE HINDZINC stock?A: NSE HINDZINC stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Polynomial Regression
Q: Is NSE HINDZINC stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE HINDZINC Stock.
Q: Is Hindustan Zinc Limited stock a good investment?
A: The consensus rating for Hindustan Zinc Limited is Hold and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of NSE HINDZINC stock?
A: The consensus rating for NSE HINDZINC is Hold.
Q: What is the prediction period for NSE HINDZINC stock?
A: The prediction period for NSE HINDZINC is (n+4 weeks)
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