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 Oil India Limited prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression1,2,3,4 and conclude that the NSE OIL 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 OIL stock.
Keywords: NSE OIL, Oil India 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
- How do you know when a stock will go up or down?
NSE OIL Target Price Prediction Modeling Methodology
Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. We consider Oil India Limited Stock Decision Process with Ridge Regression where A is the set of discrete actions of NSE OIL 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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+1 year)
n:Time series to forecast
p:Price signals of NSE OIL 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 OIL Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: NSE OIL Oil India Limited
Time series to forecast n: 30 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 OIL 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
Oil India Limited assigned short-term B1 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Ridge Regression1,2,3,4 and conclude that the NSE OIL 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 OIL stock.
Financial State Forecast for NSE OIL Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | B1 | B3 |
| Operational Risk | 37 | 66 |
| Market Risk | 79 | 52 |
| Technical Analysis | 44 | 45 |
| Fundamental Analysis | 66 | 32 |
| Risk Unsystematic | 65 | 30 |
Prediction Confidence Score
References
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- Zeileis A, Hothorn T, Hornik K. 2008. Model-based recursive partitioning. J. Comput. Graph. Stat. 17:492–514 Zhou Z, Athey S, Wager S. 2018. Offline multi-action policy learning: generalization and optimization. arXiv:1810.04778 [stat.ML]
- Mikolov T, Yih W, Zweig G. 2013c. Linguistic regularities in continuous space word representations. In Pro- ceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–51. New York: Assoc. Comput. Linguist.
Frequently Asked Questions
Q: What is the prediction methodology for NSE OIL stock?A: NSE OIL stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Ridge Regression
Q: Is NSE OIL stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE OIL Stock.
Q: Is Oil India Limited stock a good investment?
A: The consensus rating for Oil India Limited is Hold and assigned short-term B1 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of NSE OIL stock?
A: The consensus rating for NSE OIL is Hold.
Q: What is the prediction period for NSE OIL stock?
A: The prediction period for NSE OIL is (n+1 year)
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