In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We evaluate Albert David Limited prediction models with Reinforcement Machine Learning (ML) and Paired T-Test1,2,3,4 and conclude that the NSE ALBERTDAVD 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 ALBERTDAVD stock.
Keywords: NSE ALBERTDAVD, Albert David Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- How useful are statistical predictions?
- What statistical methods are used to analyze data?
- What is neural prediction?
NSE ALBERTDAVD Target Price Prediction Modeling Methodology
Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We consider Albert David Limited Stock Decision Process with Paired T-Test where A is the set of discrete actions of NSE ALBERTDAVD 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(Paired T-Test)5,6,7= X R(Reinforcement Machine Learning (ML)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of NSE ALBERTDAVD 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 ALBERTDAVD Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: NSE ALBERTDAVD Albert David 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 ALBERTDAVD 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
Albert David Limited assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Reinforcement Machine Learning (ML) with Paired T-Test1,2,3,4 and conclude that the NSE ALBERTDAVD 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 ALBERTDAVD stock.
Financial State Forecast for NSE ALBERTDAVD Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | Ba3 |
| Operational Risk | 31 | 46 |
| Market Risk | 68 | 40 |
| Technical Analysis | 44 | 76 |
| Fundamental Analysis | 69 | 87 |
| Risk Unsystematic | 60 | 64 |
Prediction Confidence Score
References
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
- V. Konda and J. Tsitsiklis. Actor-Critic algorithms. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1008–1014, 2000
- Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
- Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
- Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
Frequently Asked Questions
Q: What is the prediction methodology for NSE ALBERTDAVD stock?A: NSE ALBERTDAVD stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Paired T-Test
Q: Is NSE ALBERTDAVD stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE ALBERTDAVD Stock.
Q: Is Albert David Limited stock a good investment?
A: The consensus rating for Albert David Limited is Hold and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE ALBERTDAVD stock?
A: The consensus rating for NSE ALBERTDAVD is Hold.
Q: What is the prediction period for NSE ALBERTDAVD stock?
A: The prediction period for NSE ALBERTDAVD is (n+16 weeks)
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