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 evaluate SELECTIVE INS GROUP prediction models with Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the SIGI 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 SIGI stock.**

**SIGI, SELECTIVE INS GROUP, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Should I buy stocks now or wait amid such uncertainty?
- Probability Distribution
- Market Signals

## SIGI Target Price Prediction Modeling Methodology

This paper addresses problem of predicting direction of movement of stock and stock price index. The study compares four prediction models, Artificial Neural Network (ANN), Support Vector Machine (SVM), random forest and naive-Bayes with two approaches for input to these models. We consider SELECTIVE INS GROUP Stock Decision Process with Wilcoxon Sign-Rank Test where A is the set of discrete actions of SIGI 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 Î³ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Wilcoxon Sign-Rank Test)

^{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 (Market News Sentiment Analysis)) X S(n):→ (n+1 year) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of SIGI 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?

## SIGI Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**SIGI SELECTIVE INS GROUP

**Time series to forecast n: 22 Sep 2022**for (n+1 year)

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

SELECTIVE INS GROUP assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) with Wilcoxon Sign-Rank Test ^{1,2,3,4} and conclude that the SIGI 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 SIGI stock.**

### Financial State Forecast for SIGI Stock Options & Futures

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

Outlook* | B1 | B1 |

Operational Risk | 42 | 82 |

Market Risk | 64 | 30 |

Technical Analysis | 56 | 48 |

Fundamental Analysis | 63 | 43 |

Risk Unsystematic | 83 | 80 |

### Prediction Confidence Score

## References

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- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- Mnih A, Kavukcuoglu K. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, Vol. 26, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 2265–73. San Diego, CA: Neural Inf. Process. Syst. Found.
- Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
- Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer

## Frequently Asked Questions

Q: What is the prediction methodology for SIGI stock?A: SIGI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market News Sentiment Analysis) and Wilcoxon Sign-Rank Test

Q: Is SIGI stock a buy or sell?

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

Q: Is SELECTIVE INS GROUP stock a good investment?

A: The consensus rating for SELECTIVE INS GROUP is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of SIGI stock?

A: The consensus rating for SIGI is Hold.

Q: What is the prediction period for SIGI stock?

A: The prediction period for SIGI is (n+1 year)