Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. We evaluate Omaxe Limited prediction models with Modular Neural Network (CNN Layer) and Sign Test1,2,3,4 and conclude that the NSE OMAXE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE OMAXE stock.

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

Key Points

1. Stock Rating
2. Market Signals
3. How can neural networks improve predictions?

NSE OMAXE Target Price Prediction Modeling Methodology

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine-learning models in a stock market. We consider Omaxe Limited Stock Decision Process with Sign Test where A is the set of discrete actions of NSE OMAXE 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(Sign 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 (CNN Layer)) X S(n):→ (n+3 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 OMAXE 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 OMAXE Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: NSE OMAXE Omaxe Limited
Time series to forecast n: 01 Oct 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE OMAXE 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

Omaxe Limited assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (CNN Layer) with Sign Test1,2,3,4 and conclude that the NSE OMAXE stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Hold NSE OMAXE stock.

Financial State Forecast for NSE OMAXE Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2B1
Operational Risk 7435
Market Risk3445
Technical Analysis5954
Fundamental Analysis6068
Risk Unsystematic4378

Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 570 signals.

References

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2. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
3. V. Borkar. Q-learning for risk-sensitive control. Mathematics of Operations Research, 27:294–311, 2002.
4. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
5. J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
6. Burkov A. 2019. The Hundred-Page Machine Learning Book. Quebec City, Can.: Andriy Burkov
7. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
Frequently Asked QuestionsQ: What is the prediction methodology for NSE OMAXE stock?
A: NSE OMAXE stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Sign Test
Q: Is NSE OMAXE stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE OMAXE Stock.
Q: Is Omaxe Limited stock a good investment?
A: The consensus rating for Omaxe Limited is Hold and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE OMAXE stock?
A: The consensus rating for NSE OMAXE is Hold.
Q: What is the prediction period for NSE OMAXE stock?
A: The prediction period for NSE OMAXE is (n+3 month)