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 evaluate American Financial Group prediction models with Modular Neural Network (Market Volatility Analysis) and Linear Regression ^{1,2,3,4} and conclude that the AFG stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold AFG stock.**

**AFG, American Financial Group, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- Trading Interaction
- Technical Analysis with Algorithmic Trading
- How do you decide buy or sell a stock?

## AFG Target Price Prediction Modeling Methodology

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. We consider American Financial Group Stock Decision Process with Linear Regression where A is the set of discrete actions of AFG 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(Linear 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 (Market Volatility Analysis)) X S(n):→ (n+6 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of AFG 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?

## AFG Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**AFG American Financial Group

**Time series to forecast n: 20 Sep 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold AFG 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

American Financial Group assigned short-term Ba3 & long-term Ba3 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) with Linear Regression ^{1,2,3,4} and conclude that the AFG stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period: The dominant strategy among neural network is to Hold AFG stock.**

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

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

Outlook* | Ba3 | Ba3 |

Operational Risk | 42 | 64 |

Market Risk | 90 | 72 |

Technical Analysis | 85 | 34 |

Fundamental Analysis | 70 | 75 |

Risk Unsystematic | 32 | 67 |

### Prediction Confidence Score

## References

- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
- Athey S, Bayati M, Imbens G, Zhaonan Q. 2019. Ensemble methods for causal effects in panel data settings. NBER Work. Pap. 25675
- Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
- Abadie A, Diamond A, Hainmueller J. 2015. Comparative politics and the synthetic control method. Am. J. Political Sci. 59:495–510
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- K. Tuyls and G. Weiss. Multiagent learning: Basics, challenges, and prospects. AI Magazine, 33(3): 41–52, 2012

## Frequently Asked Questions

Q: What is the prediction methodology for AFG stock?A: AFG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Linear Regression

Q: Is AFG stock a buy or sell?

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

Q: Is American Financial Group stock a good investment?

A: The consensus rating for American Financial Group is Hold and assigned short-term Ba3 & long-term Ba3 forecasted stock rating.

Q: What is the consensus rating of AFG stock?

A: The consensus rating for AFG is Hold.

Q: What is the prediction period for AFG stock?

A: The prediction period for AFG is (n+6 month)

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