The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We evaluate The Great Eastern Shipping Company Limited prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Linear Regression1,2,3,4 and conclude that the NSE GESHIP 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 Buy NSE GESHIP stock.
Keywords: NSE GESHIP, The Great Eastern Shipping Company Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.
Key Points
- Decision Making
- Trading Signals
- Fundemental Analysis with Algorithmic Trading
NSE GESHIP Target Price Prediction Modeling Methodology
This paper proposes genetic algorithms (GAs) approach to feature discretization and the determination of connection weights for artificial neural networks (ANNs) to predict the stock price index. Previous research proposed many hybrid models of ANN and GA for the method of training the network, feature subset selection, and topology optimization. We consider The Great Eastern Shipping Company Limited Stock Decision Process with Linear Regression where A is the set of discrete actions of NSE GESHIP 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(Linear Regression)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 GESHIP 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 GESHIP Stock Forecast (Buy or Sell) for (n+1 year)
Sample Set: Neural NetworkStock/Index: NSE GESHIP The Great Eastern Shipping Company 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 Buy NSE GESHIP 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
The Great Eastern Shipping Company Limited assigned short-term Ba2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Linear Regression1,2,3,4 and conclude that the NSE GESHIP 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 Buy NSE GESHIP stock.
Financial State Forecast for NSE GESHIP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba2 | Ba3 |
| Operational Risk | 82 | 85 |
| Market Risk | 85 | 35 |
| Technical Analysis | 74 | 79 |
| Fundamental Analysis | 65 | 79 |
| Risk Unsystematic | 40 | 53 |
Prediction Confidence Score
References
- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
- Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
- 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
- Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
- 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
- Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
Frequently Asked Questions
Q: What is the prediction methodology for NSE GESHIP stock?A: NSE GESHIP stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Linear Regression
Q: Is NSE GESHIP stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSE GESHIP Stock.
Q: Is The Great Eastern Shipping Company Limited stock a good investment?
A: The consensus rating for The Great Eastern Shipping Company Limited is Buy and assigned short-term Ba2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE GESHIP stock?
A: The consensus rating for NSE GESHIP is Buy.
Q: What is the prediction period for NSE GESHIP stock?
A: The prediction period for NSE GESHIP is (n+1 year)
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