Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. We evaluate Mishra Dhatu Nigam Limited prediction models with Active Learning (ML) and Factor1,2,3,4 and conclude that the NSE MIDHANI 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 MIDHANI stock.

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

## Key Points

1. How accurate is machine learning in stock market?
2. What is Markov decision process in reinforcement learning?
3. Market Outlook

## NSE MIDHANI Target Price Prediction Modeling Methodology

With technological advancements, big data can be easily generated and collected in many applications. Embedded in these big data are useful information and knowledge that can be discovered by machine learning and data mining models, techniques or algorithms. We consider Mishra Dhatu Nigam Limited Stock Decision Process with Factor where A is the set of discrete actions of NSE MIDHANI 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(Factor)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(Active Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of NSE MIDHANI 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 MIDHANI Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: NSE MIDHANI Mishra Dhatu Nigam Limited
Time series to forecast n: 02 Oct 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy NSE MIDHANI 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

Mishra Dhatu Nigam Limited assigned short-term B2 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Factor1,2,3,4 and conclude that the NSE MIDHANI 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 MIDHANI stock.

### Financial State Forecast for NSE MIDHANI Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Operational Risk 4689
Market Risk3272
Technical Analysis6835
Fundamental Analysis8545
Risk Unsystematic4767

### Prediction Confidence Score

Trust metric by Neural Network: 83 out of 100 with 609 signals.

## References

1. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
2. J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
3. Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
4. Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
5. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
6. 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
7. Imbens G, Wooldridge J. 2009. Recent developments in the econometrics of program evaluation. J. Econ. Lit. 47:5–86
Frequently Asked QuestionsQ: What is the prediction methodology for NSE MIDHANI stock?
A: NSE MIDHANI stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Factor
Q: Is NSE MIDHANI stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSE MIDHANI Stock.
Q: Is Mishra Dhatu Nigam Limited stock a good investment?
A: The consensus rating for Mishra Dhatu Nigam Limited is Buy and assigned short-term B2 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of NSE MIDHANI stock?
A: The consensus rating for NSE MIDHANI is Buy.
Q: What is the prediction period for NSE MIDHANI stock?
A: The prediction period for NSE MIDHANI is (n+1 year)