In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. We evaluate Rail Vikas Nigam Limited prediction models with Modular Neural Network (Emotional Trigger/Responses Analysis) and Ridge Regression1,2,3,4 and conclude that the NSE RVNL stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy NSE RVNL stock.
Keywords: NSE RVNL, Rail Vikas Nigam 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?
- What are buy sell or hold recommendations?
- Buy, Sell and Hold Signals
NSE RVNL 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 Rail Vikas Nigam Limited Stock Decision Process with Ridge Regression where A is the set of discrete actions of NSE RVNL 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 (Emotional Trigger/Responses Analysis)) X S(n):→ (n+6 month)
n:Time series to forecast
p:Price signals of NSE RVNL 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 RVNL Stock Forecast (Buy or Sell) for (n+6 month)
Sample Set: Neural NetworkStock/Index: NSE RVNL Rail Vikas Nigam Limited
Time series to forecast n: 26 Sep 2022 for (n+6 month)
According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy NSE RVNL 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
Rail Vikas Nigam Limited assigned short-term Ba1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) with Ridge Regression1,2,3,4 and conclude that the NSE RVNL stock is predictable in the short/long term. According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Buy NSE RVNL stock.
Financial State Forecast for NSE RVNL Stock Options & Futures
| Rating | Short-Term | Long-Term Senior |
|---|---|---|
| Outlook* | Ba1 | Ba3 |
| Operational Risk | 83 | 61 |
| Market Risk | 59 | 69 |
| Technical Analysis | 87 | 67 |
| Fundamental Analysis | 86 | 79 |
| Risk Unsystematic | 40 | 41 |
Prediction Confidence Score
References
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
- Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
- Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
- P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
Frequently Asked Questions
Q: What is the prediction methodology for NSE RVNL stock?A: NSE RVNL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and Ridge Regression
Q: Is NSE RVNL stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSE RVNL Stock.
Q: Is Rail Vikas Nigam Limited stock a good investment?
A: The consensus rating for Rail Vikas Nigam Limited is Buy and assigned short-term Ba1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE RVNL stock?
A: The consensus rating for NSE RVNL is Buy.
Q: What is the prediction period for NSE RVNL stock?
A: The prediction period for NSE RVNL is (n+6 month)
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