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 Marksans Pharma Limited prediction models with Deductive Inference (ML) and Ridge Regression1,2,3,4 and conclude that the NSE MARKSANS 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 Hold NSE MARKSANS stock.
Keywords: NSE MARKSANS, Marksans Pharma Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Can neural networks predict stock market?
- Buy, Sell and Hold Signals
- What is statistical models in machine learning?
NSE MARKSANS 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 Marksans Pharma Limited Stock Decision Process with Ridge Regression where A is the set of discrete actions of NSE MARKSANS 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= X R(Deductive Inference (ML)) X S(n):→ (n+8 weeks)
n:Time series to forecast
p:Price signals of NSE MARKSANS 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 MARKSANS Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: NSE MARKSANS Marksans Pharma Limited
Time series to forecast n: 28 Sep 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold NSE MARKSANS 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
Marksans Pharma Limited assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Ridge Regression1,2,3,4 and conclude that the NSE MARKSANS 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 Hold NSE MARKSANS stock.
Financial State Forecast for NSE MARKSANS Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B1 |
| Operational Risk | 35 | 59 |
| Market Risk | 76 | 86 |
| Technical Analysis | 63 | 48 |
| Fundamental Analysis | 75 | 47 |
| Risk Unsystematic | 35 | 55 |
Prediction Confidence Score
References
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
- V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
- Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
Frequently Asked Questions
Q: What is the prediction methodology for NSE MARKSANS stock?A: NSE MARKSANS stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Ridge Regression
Q: Is NSE MARKSANS stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE MARKSANS Stock.
Q: Is Marksans Pharma Limited stock a good investment?
A: The consensus rating for Marksans Pharma Limited is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE MARKSANS stock?
A: The consensus rating for NSE MARKSANS is Hold.
Q: What is the prediction period for NSE MARKSANS stock?
A: The prediction period for NSE MARKSANS is (n+8 weeks)
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