The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction. We evaluate Nifty 50 Index prediction models with Deductive Inference (ML) and Ridge Regression1,2,3,4 and conclude that the Nifty 50 Index 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 Nifty 50 Index stock.
Keywords: Nifty 50 Index, Nifty 50 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How do predictive algorithms actually work?
- How useful are statistical predictions?
- Game Theory
Nifty 50 Index Target Price Prediction Modeling Methodology
In modern financial market, the most crucial problem is to find essential approach to outline and visualizing the predictions in stock-markets to be made by individuals in order to attain maximum profit by investments. The stock market is a transformative, non-straight dynamical and complex system. Long term investment is one of the major investment decisions. Though, evaluating shares and calculating elementary values for companies for long term investment is difficult. In this paper we are going to present comparison of machine learning aided algorithms to evaluate the stock prices in the future to analyze market behaviour. We consider Nifty 50 Index Stock Decision Process with Ridge Regression where A is the set of discrete actions of Nifty 50 Index 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 Nifty 50 Index 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?
Nifty 50 Index Stock Forecast (Buy or Sell) for (n+8 weeks)
Sample Set: Neural NetworkStock/Index: Nifty 50 Index Nifty 50 Index
Time series to forecast n: 10 Sep 2022 for (n+8 weeks)
According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold Nifty 50 Index 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
Nifty 50 Index assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Deductive Inference (ML) with Ridge Regression1,2,3,4 and conclude that the Nifty 50 Index 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 Nifty 50 Index stock.
Financial State Forecast for Nifty 50 Index Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | Ba1 |
| Operational Risk | 44 | 62 |
| Market Risk | 34 | 78 |
| Technical Analysis | 44 | 41 |
| Fundamental Analysis | 52 | 82 |
| Risk Unsystematic | 59 | 86 |
Prediction Confidence Score
References
- J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
- Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
- L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
- Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
Frequently Asked Questions
Q: What is the prediction methodology for Nifty 50 Index stock?A: Nifty 50 Index stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Ridge Regression
Q: Is Nifty 50 Index stock a buy or sell?
A: The dominant strategy among neural network is to Hold Nifty 50 Index Stock.
Q: Is Nifty 50 Index stock a good investment?
A: The consensus rating for Nifty 50 Index is Hold and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of Nifty 50 Index stock?
A: The consensus rating for Nifty 50 Index is Hold.
Q: What is the prediction period for Nifty 50 Index stock?
A: The prediction period for Nifty 50 Index is (n+8 weeks)
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