One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. ** We evaluate Nifty 50 Index prediction models with Statistical Inference (ML) and Chi-Square ^{1,2,3,4} and conclude that the Nifty 50 Index stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold Nifty 50 Index stock.**

**Nifty 50 Index, Nifty 50 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Can machine learning predict?
- Stock Rating
- Probability Distribution

## Nifty 50 Index Target Price Prediction Modeling Methodology

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. We consider Nifty 50 Index Stock Decision Process with Chi-Square where A is the set of discrete actions of Nifty 50 Index 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ (n+8 weeks) $\overrightarrow{R}=\left({r}_{1},{r}_{2},{r}_{3}\right)$

n:Time series to forecast

p:Price signals of Nifty 50 Index 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?

## Nifty 50 Index Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**Nifty 50 Index Nifty 50 Index

**Time series to forecast n: 04 Oct 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold Nifty 50 Index 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

Nifty 50 Index assigned short-term Ba2 & long-term B3 forecasted stock rating.** We evaluate the prediction models Statistical Inference (ML) with Chi-Square ^{1,2,3,4} and conclude that the Nifty 50 Index stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Hold Nifty 50 Index stock.**

### Financial State Forecast for Nifty 50 Index Stock Options & Futures

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

Outlook* | Ba2 | B3 |

Operational Risk | 44 | 40 |

Market Risk | 67 | 42 |

Technical Analysis | 73 | 32 |

Fundamental Analysis | 88 | 75 |

Risk Unsystematic | 72 | 42 |

### Prediction Confidence Score

## References

- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
- Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
- 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
- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
- Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
- Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.

## Frequently Asked Questions

Q: What is the prediction methodology for Nifty 50 Index stock?A: Nifty 50 Index stock prediction methodology: We evaluate the prediction models Statistical Inference (ML) and Chi-Square

Q: Is Nifty 50 Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold Nifty 50 Index Stock.

Q: Is Nifty 50 Index stock a good investment?

A: The consensus rating for Nifty 50 Index is Hold and assigned short-term Ba2 & long-term B3 forecasted stock rating.

Q: What is the consensus rating of Nifty 50 Index stock?

A: The consensus rating for Nifty 50 Index is Hold.

Q: What is the prediction period for Nifty 50 Index stock?

A: The prediction period for Nifty 50 Index is (n+8 weeks)