Prediction of the trend of the stock market is very crucial. If someone has robust forecasting tools, then he/she will increase the return on investment and can get rich easily and quickly. Because there are a lot of factors that can influence the stock market, the stock forecasting problem has always been very complicated. Support Vector Regression is a tool from machine learning that can build a regression model on the historical time series data in the purpose of predicting the future trend of the stock price. We evaluate Rajesh Exports Limited prediction models with Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NSE RAJESHEXPO 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 Hold NSE RAJESHEXPO stock.

Keywords: NSE RAJESHEXPO, Rajesh Exports Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.

Key Points

1. What are main components of Markov decision process?
2. What is a prediction confidence?
3. Prediction Modeling

NSE RAJESHEXPO Target Price Prediction Modeling Methodology

Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modeling of finance time series importantly guide investors' decisions and trades. In addition, in a dynamic environment such as the stock market, the nonlinearity of the time series is pronounced, immediately affecting the efficacy of stock price forecasts. Thus, this paper proposes an intelligent time series prediction system that uses sliding-window metaheuristic optimization for the purpose of predicting the stock prices. We consider Rajesh Exports Limited Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of NSE RAJESHEXPO 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(Wilcoxon Rank-Sum Test)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of NSE RAJESHEXPO 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 RAJESHEXPO Stock Forecast (Buy or Sell) for (n+6 month)

Sample Set: Neural Network
Stock/Index: NSE RAJESHEXPO Rajesh Exports Limited
Time series to forecast n: 27 Sep 2022 for (n+6 month)

According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold NSE RAJESHEXPO 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

Rajesh Exports Limited assigned short-term B1 & long-term B2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Wilcoxon Rank-Sum Test1,2,3,4 and conclude that the NSE RAJESHEXPO 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 Hold NSE RAJESHEXPO stock.

Financial State Forecast for NSE RAJESHEXPO Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1B2
Operational Risk 7872
Market Risk5051
Technical Analysis5870
Fundamental Analysis4146
Risk Unsystematic7035

Prediction Confidence Score

Trust metric by Neural Network: 88 out of 100 with 547 signals.

References

1. D. S. Bernstein, S. Zilberstein, and N. Immerman. The complexity of decentralized control of Markov Decision Processes. In UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, pages 32–37, 2000.
2. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
3. Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
4. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
5. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
6. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
7. Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
Frequently Asked QuestionsQ: What is the prediction methodology for NSE RAJESHEXPO stock?
A: NSE RAJESHEXPO stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Rank-Sum Test
Q: Is NSE RAJESHEXPO stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE RAJESHEXPO Stock.
Q: Is Rajesh Exports Limited stock a good investment?
A: The consensus rating for Rajesh Exports Limited is Hold and assigned short-term B1 & long-term B2 forecasted stock rating.
Q: What is the consensus rating of NSE RAJESHEXPO stock?
A: The consensus rating for NSE RAJESHEXPO is Hold.
Q: What is the prediction period for NSE RAJESHEXPO stock?
A: The prediction period for NSE RAJESHEXPO is (n+6 month)