The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods.** We evaluate BSE Sensex 30 Index prediction models with Modular Neural Network (Financial Sentiment Analysis) and Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the BSE Sensex 30 Index 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 Buy BSE Sensex 30 Index stock.**

**BSE Sensex 30 Index, BSE Sensex 30 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trading Interaction
- How accurate is machine learning in stock market?
- Can machine learning predict?

## BSE Sensex 30 Index Target Price Prediction Modeling Methodology

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach. We consider BSE Sensex 30 Index Stock Decision Process with Statistical Hypothesis Testing where A is the set of discrete actions of BSE Sensex 30 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(Statistical Hypothesis Testing)

^{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) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of BSE Sensex 30 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?

## BSE Sensex 30 Index Stock Forecast (Buy or Sell) for (n+16 weeks)

**Sample Set:**Neural Network

**Stock/Index:**BSE Sensex 30 Index BSE Sensex 30 Index

**Time series to forecast n: 11 Sep 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Buy BSE Sensex 30 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

BSE Sensex 30 Index assigned short-term B1 & long-term Ba1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Statistical Hypothesis Testing ^{1,2,3,4} and conclude that the BSE Sensex 30 Index 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 Buy BSE Sensex 30 Index stock.**

### Financial State Forecast for BSE Sensex 30 Index Stock Options & Futures

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

Outlook* | B1 | Ba1 |

Operational Risk | 45 | 81 |

Market Risk | 77 | 83 |

Technical Analysis | 68 | 59 |

Fundamental Analysis | 42 | 72 |

Risk Unsystematic | 65 | 57 |

### Prediction Confidence Score

## References

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- Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
- Lai TL, Robbins H. 1985. Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6:4–22
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]

## Frequently Asked Questions

Q: What is the prediction methodology for BSE Sensex 30 Index stock?A: BSE Sensex 30 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Statistical Hypothesis Testing

Q: Is BSE Sensex 30 Index stock a buy or sell?

A: The dominant strategy among neural network is to Buy BSE Sensex 30 Index Stock.

Q: Is BSE Sensex 30 Index stock a good investment?

A: The consensus rating for BSE Sensex 30 Index is Buy and assigned short-term B1 & long-term Ba1 forecasted stock rating.

Q: What is the consensus rating of BSE Sensex 30 Index stock?

A: The consensus rating for BSE Sensex 30 Index is Buy.

Q: What is the prediction period for BSE Sensex 30 Index stock?

A: The prediction period for BSE Sensex 30 Index is (n+16 weeks)

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