Dalmia Bharat Limited assigned short-term B1 & long-term B1 forecasted stock rating. (Forecast)

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. We evaluate Dalmia Bharat Limited prediction models with Ensemble Learning (ML) and Chi-Square^1,2,3,4 and conclude that the NSE DALBHARAT 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 Hold NSE DALBHARAT stock.

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

Key Points

1. Nash Equilibria
2. Investment Risk
3. Which neural network is best for prediction?

NSE DALBHARAT Target Price Prediction Modeling Methodology

This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms. We consider Dalmia Bharat Limited Stock Decision Process with Chi-Square where A is the set of discrete actions of NSE DALBHARAT 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(Chi-Square)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Ensemble Learning (ML)) X S(n):→ (n+1 year) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE DALBHARAT Dalmia Bharat 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 Hold NSE DALBHARAT 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

Dalmia Bharat Limited assigned short-term B1 & long-term B1 forecasted stock rating. We evaluate the prediction models Ensemble Learning (ML) with Chi-Square^1,2,3,4 and conclude that the NSE DALBHARAT 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 Hold NSE DALBHARAT stock.

Financial State Forecast for NSE DALBHARAT Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B1	B1
Operational Risk	53	79
Market Risk	75	78
Technical Analysis	84	36
Fundamental Analysis	43	36
Risk Unsystematic	46	62

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 528 signals.

References

1. 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]
2. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
3. Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
4. Bennett J, Lanning S. 2007. The Netflix prize. In Proceedings of KDD Cup and Workshop 2007, p. 35. New York: ACM
5. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
6. Babula, R. A. (1988), "Contemporaneous correlation and modeling Canada's imports of U.S. crops," Journal of Agricultural Economics Research, 41, 33–38.
7. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]

Frequently Asked Questions

Q: What is the prediction methodology for NSE DALBHARAT stock?
A: NSE DALBHARAT stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Chi-Square
Q: Is NSE DALBHARAT stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE DALBHARAT Stock.
Q: Is Dalmia Bharat Limited stock a good investment?
A: The consensus rating for Dalmia Bharat Limited is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of NSE DALBHARAT stock?
A: The consensus rating for NSE DALBHARAT is Hold.
Q: What is the prediction period for NSE DALBHARAT stock?
A: The prediction period for NSE DALBHARAT is (n+1 year)

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