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 evaluate Madhya Bharat Agro Products Limited prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Logistic Regression1,2,3,4 and conclude that the NSE MBAPL 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 MBAPL stock.

Keywords: NSE MBAPL, Madhya Bharat Agro Products 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. Dominated Move

## NSE MBAPL Target Price Prediction Modeling Methodology

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More specifically, the stock market's movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. We consider Madhya Bharat Agro Products Limited Stock Decision Process with Logistic Regression where A is the set of discrete actions of NSE MBAPL 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(Logistic Regression)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 (Social Media Sentiment Analysis)) X S(n):→ (n+1 year) $\stackrel{\to }{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

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

Sample Set: Neural Network
Stock/Index: NSE MBAPL Madhya Bharat Agro Products Limited
Time series to forecast n: 11 Nov 2022 for (n+1 year)

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

1. The fair value of a financial instrument at initial recognition is normally the transaction price (ie the fair value of the consideration given or received, see also paragraph B5.1.2A and IFRS 13). However, if part of the consideration given or received is for something other than the financial instrument, an entity shall measure the fair value of the financial instrument. For example, the fair value of a long-term loan or receivable that carries no interest can be measured as the present value of all future cash receipts discounted using the prevailing market rate(s) of interest for a similar instrument (similar as to currency, term, type of interest rate and other factors) with a similar credit rating. Any additional amount lent is an expense or a reduction of income unless it qualifies for recognition as some other type of asset.
2. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
3. IFRS 7 defines credit risk as 'the risk that one party to a financial instrument will cause a financial loss for the other party by failing to discharge an obligation'. The requirement in paragraph 5.7.7(a) relates to the risk that the issuer will fail to perform on that particular liability. It does not necessarily relate to the creditworthiness of the issuer. For example, if an entity issues a collateralised liability and a non-collateralised liability that are otherwise identical, the credit risk of those two liabilities will be different, even though they are issued by the same entity. The credit risk on the collateralised liability will be less than the credit risk of the non-collateralised liability. The credit risk for a collateralised liability may be close to zero.
4. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

Madhya Bharat Agro Products Limited assigned short-term Ba3 & long-term Baa2 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Logistic Regression1,2,3,4 and conclude that the NSE MBAPL 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 MBAPL stock.

### Financial State Forecast for NSE MBAPL Madhya Bharat Agro Products Limited Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*Ba3Baa2
Operational Risk 8484
Market Risk7273
Technical Analysis5480
Fundamental Analysis7571
Risk Unsystematic3089

### Prediction Confidence Score

Trust metric by Neural Network: 90 out of 100 with 651 signals.

## References

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Frequently Asked QuestionsQ: What is the prediction methodology for NSE MBAPL stock?
A: NSE MBAPL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Logistic Regression
Q: Is NSE MBAPL stock a buy or sell?
A: The dominant strategy among neural network is to Hold NSE MBAPL Stock.
Q: Is Madhya Bharat Agro Products Limited stock a good investment?
A: The consensus rating for Madhya Bharat Agro Products Limited is Hold and assigned short-term Ba3 & long-term Baa2 forecasted stock rating.
Q: What is the consensus rating of NSE MBAPL stock?
A: The consensus rating for NSE MBAPL is Hold.
Q: What is the prediction period for NSE MBAPL stock?
A: The prediction period for NSE MBAPL is (n+1 year)