We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We evaluate Bata India Limited prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Paired T-Test1,2,3,4 and conclude that the NSE BATAINDIA stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE BATAINDIA stock.
Keywords: NSE BATAINDIA, Bata India Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Trading Interaction
- Fundemental Analysis with Algorithmic Trading
- How do you decide buy or sell a stock?
NSE BATAINDIA Target Price Prediction Modeling Methodology
The prediction of stock price performance is a difficult and complex problem. Multivariate analytical techniques using both quantitative and qualitative variables have repeatedly been used to help form the basis of investor stock price expectations and, hence, influence investment decision making. However, the performance of multivariate analytical techniques is often less than conclusive and needs to be improved to more accurately forecast stock price performance. A neural network method has demonstrated its capability of addressing complex problems. We consider Bata India Limited Stock Decision Process with Paired T-Test where A is the set of discrete actions of NSE BATAINDIA 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(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of NSE BATAINDIA stock
j:Nash equilibria
k:Dominated move
a:Best response for target price
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How do AC Investment Research machine learning (predictive) algorithms actually work?
NSE BATAINDIA Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: NSE BATAINDIA Bata India Limited
Time series to forecast n: 26 Sep 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE BATAINDIA 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
Bata India Limited assigned short-term B2 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Paired T-Test1,2,3,4 and conclude that the NSE BATAINDIA stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE BATAINDIA stock.
Financial State Forecast for NSE BATAINDIA Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B2 | B3 |
| Operational Risk | 64 | 31 |
| Market Risk | 64 | 37 |
| Technical Analysis | 62 | 40 |
| Fundamental Analysis | 43 | 89 |
| Risk Unsystematic | 38 | 30 |
Prediction Confidence Score
References
- Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]
- Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
- Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
- M. Petrik and D. Subramanian. An approximate solution method for large risk-averse Markov decision processes. In Proceedings of the 28th International Conference on Uncertainty in Artificial Intelligence, 2012.
- D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
Frequently Asked Questions
Q: What is the prediction methodology for NSE BATAINDIA stock?A: NSE BATAINDIA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Paired T-Test
Q: Is NSE BATAINDIA stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE BATAINDIA Stock.
Q: Is Bata India Limited stock a good investment?
A: The consensus rating for Bata India Limited is Hold and assigned short-term B2 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of NSE BATAINDIA stock?
A: The consensus rating for NSE BATAINDIA is Hold.
Q: What is the prediction period for NSE BATAINDIA stock?
A: The prediction period for NSE BATAINDIA is (n+1 year)
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