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 Vaibhav Global Limited prediction models with Modular Neural Network (Financial Sentiment Analysis) and Logistic Regression ^{1,2,3,4} and conclude that the NSE VAIBHAVGBL stock is predictable in the short/long term. **

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE VAIBHAVGBL stock.**

**NSE VAIBHAVGBL, Vaibhav Global Limited, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Stock Rating
- What are the most successful trading algorithms?
- What are main components of Markov decision process?

## NSE VAIBHAVGBL Target Price Prediction Modeling Methodology

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. We consider Vaibhav Global Limited Stock Decision Process with Logistic Regression where A is the set of discrete actions of NSE VAIBHAVGBL 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+16 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of NSE VAIBHAVGBL 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 VAIBHAVGBL Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**NSE VAIBHAVGBL Vaibhav Global Limited

**Time series to forecast n: 05 Nov 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE VAIBHAVGBL 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%**

## Adjusted IFRS* Prediction Methods for Vaibhav Global Limited

- An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
- When an entity separates the foreign currency basis spread from a financial instrument and excludes it from the designation of that financial instrument as the hedging instrument (see paragraph 6.2.4(b)), the application guidance in paragraphs B6.5.34–B6.5.38 applies to the foreign currency basis spread in the same manner as it is applied to the forward element of a forward contract.
- The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.
- An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.

*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

Vaibhav Global Limited assigned short-term Ba1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Logistic Regression ^{1,2,3,4} and conclude that the NSE VAIBHAVGBL stock is predictable in the short/long term.**

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold NSE VAIBHAVGBL stock.**

### Financial State Forecast for NSE VAIBHAVGBL Vaibhav Global Limited Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba1 | B1 |

Operational Risk | 82 | 53 |

Market Risk | 31 | 42 |

Technical Analysis | 67 | 53 |

Fundamental Analysis | 88 | 56 |

Risk Unsystematic | 84 | 85 |

### Prediction Confidence Score

## References

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## Frequently Asked Questions

Q: What is the prediction methodology for NSE VAIBHAVGBL stock?A: NSE VAIBHAVGBL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Logistic Regression

Q: Is NSE VAIBHAVGBL stock a buy or sell?

A: The dominant strategy among neural network is to Hold NSE VAIBHAVGBL Stock.

Q: Is Vaibhav Global Limited stock a good investment?

A: The consensus rating for Vaibhav Global Limited is Hold and assigned short-term Ba1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of NSE VAIBHAVGBL stock?

A: The consensus rating for NSE VAIBHAVGBL is Hold.

Q: What is the prediction period for NSE VAIBHAVGBL stock?

A: The prediction period for NSE VAIBHAVGBL is (n+16 weeks)