Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. We evaluate K.P.R. Mill Limited prediction models with Multi-Instance Learning (ML) and Lasso Regression1,2,3,4 and conclude that the NSE KPRMILL 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 KPRMILL stock.
Keywords: NSE KPRMILL, K.P.R. Mill Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Market Outlook
- Prediction Modeling
- Nash Equilibria
NSE KPRMILL Target Price Prediction Modeling Methodology
Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. We consider K.P.R. Mill Limited Stock Decision Process with Lasso Regression where A is the set of discrete actions of NSE KPRMILL 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(Lasso Regression)5,6,7= X R(Multi-Instance Learning (ML)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of NSE KPRMILL 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 KPRMILL Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: NSE KPRMILL K.P.R. Mill Limited
Time series to forecast n: 01 Oct 2022 for (n+1 year)
According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Hold NSE KPRMILL 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
K.P.R. Mill Limited assigned short-term B3 & long-term B2 forecasted stock rating. We evaluate the prediction models Multi-Instance Learning (ML) with Lasso Regression1,2,3,4 and conclude that the NSE KPRMILL 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 KPRMILL stock.
Financial State Forecast for NSE KPRMILL Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B3 | B2 |
| Operational Risk | 39 | 56 |
| Market Risk | 71 | 47 |
| Technical Analysis | 57 | 69 |
| Fundamental Analysis | 32 | 37 |
| Risk Unsystematic | 41 | 51 |
Prediction Confidence Score
References
- J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
- F. A. Oliehoek and C. Amato. A Concise Introduction to Decentralized POMDPs. SpringerBriefs in Intelligent Systems. Springer, 2016
- Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
- M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
- E. Collins. Using Markov decision processes to optimize a nonlinear functional of the final distribution, with manufacturing applications. In Stochastic Modelling in Innovative Manufacturing, pages 30–45. Springer, 1997
- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- 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.
Frequently Asked Questions
Q: What is the prediction methodology for NSE KPRMILL stock?A: NSE KPRMILL stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Lasso Regression
Q: Is NSE KPRMILL stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE KPRMILL Stock.
Q: Is K.P.R. Mill Limited stock a good investment?
A: The consensus rating for K.P.R. Mill Limited is Hold and assigned short-term B3 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE KPRMILL stock?
A: The consensus rating for NSE KPRMILL is Hold.
Q: What is the prediction period for NSE KPRMILL stock?
A: The prediction period for NSE KPRMILL is (n+1 year)
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