In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. We evaluate Mayur Uniquoters Ltd prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Spearman Correlation1,2,3,4 and conclude that the NSE MAYURUNIQ stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell NSE MAYURUNIQ stock.
Keywords: NSE MAYURUNIQ, Mayur Uniquoters Ltd, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- What statistical methods are used to analyze data?
- Market Signals
- Stock Forecast Based On a Predictive Algorithm
NSE MAYURUNIQ Target Price Prediction Modeling Methodology
Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto- Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. We consider Mayur Uniquoters Ltd Stock Decision Process with Spearman Correlation where A is the set of discrete actions of NSE MAYURUNIQ 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(Spearman Correlation)5,6,7= X R(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of NSE MAYURUNIQ 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 MAYURUNIQ Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: NSE MAYURUNIQ Mayur Uniquoters Ltd
Time series to forecast n: 30 Sep 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell NSE MAYURUNIQ 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
Mayur Uniquoters Ltd assigned short-term B3 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Spearman Correlation1,2,3,4 and conclude that the NSE MAYURUNIQ stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Sell NSE MAYURUNIQ stock.
Financial State Forecast for NSE MAYURUNIQ Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba3 |
| Operational Risk | 34 | 63 |
| Market Risk | 38 | 88 |
| Technical Analysis | 59 | 36 |
| Fundamental Analysis | 48 | 34 |
| Risk Unsystematic | 65 | 90 |
Prediction Confidence Score
References
- Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
- R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
- C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.
- S. J. Russell and A. Zimdars. Q-decomposition for reinforcement learning agents. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA, pages 656–663, 2003.
- Blei DM, Lafferty JD. 2009. Topic models. In Text Mining: Classification, Clustering, and Applications, ed. A Srivastava, M Sahami, pp. 101–24. Boca Raton, FL: CRC Press
Frequently Asked Questions
Q: What is the prediction methodology for NSE MAYURUNIQ stock?A: NSE MAYURUNIQ stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Spearman Correlation
Q: Is NSE MAYURUNIQ stock a buy or sell?
A: The dominant strategy among neural network is to Sell NSE MAYURUNIQ Stock.
Q: Is Mayur Uniquoters Ltd stock a good investment?
A: The consensus rating for Mayur Uniquoters Ltd is Sell and assigned short-term B3 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE MAYURUNIQ stock?
A: The consensus rating for NSE MAYURUNIQ is Sell.
Q: What is the prediction period for NSE MAYURUNIQ stock?
A: The prediction period for NSE MAYURUNIQ is (n+6 month)
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