In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. We evaluate The Great Eastern Shipping Company Limited prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Stepwise Regression1,2,3,4 and conclude that the NSE GESHIP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold 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
- Prediction Modeling
- Why do we need predictive models?
- Trading Signals
NSE GESHIP Target Price Prediction Modeling Methodology
Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN). We consider The Great Eastern Shipping Company Limited Stock Decision Process with Stepwise 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(Stepwise Regression)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+4 weeks)
n:Time series to forecast
p:Price signals of NSE GESHIP 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 GESHIP Stock Forecast (Buy or Sell) for (n+4 weeks)
Sample Set: Neural NetworkStock/Index: NSE GESHIP The Great Eastern Shipping Company Limited
Time series to forecast n: 29 Sep 2022 for (n+4 weeks)
According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold 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 Caa2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Stepwise Regression1,2,3,4 and conclude that the NSE GESHIP stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold NSE GESHIP stock.
Financial State Forecast for NSE GESHIP Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Caa2 | Ba3 |
| Operational Risk | 33 | 90 |
| Market Risk | 32 | 39 |
| Technical Analysis | 46 | 60 |
| Fundamental Analysis | 57 | 47 |
| Risk Unsystematic | 58 | 69 |
Prediction Confidence Score
References
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
- A. Shapiro, W. Tekaya, J. da Costa, and M. Soares. Risk neutral and risk averse stochastic dual dynamic programming method. European journal of operational research, 224(2):375–391, 2013
- R. Rockafellar and S. Uryasev. Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26(7):1443 – 1471, 2002
- 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
- Andrews, D. W. K. (1993), "Tests for parameter instability and structural change with unknown change point," Econometrica, 61, 821–856.
- Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Netw. 2:359–66
- C. Szepesvári. Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2010
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 (Speculative Sentiment Analysis) and Stepwise Regression
Q: Is NSE GESHIP stock a buy or sell?
A: The dominant strategy among neural network is to Hold 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 Hold and assigned short-term Caa2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE GESHIP stock?
A: The consensus rating for NSE GESHIP is Hold.
Q: What is the prediction period for NSE GESHIP stock?
A: The prediction period for NSE GESHIP is (n+4 weeks)
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