The main objective of this research is to predict the market performance on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. ** We evaluate AIG prediction models with Modular Neural Network (DNN Layer) and Paired T-Test ^{1,2,3,4} and conclude that the AIG stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold AIG stock.**

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

*Keywords:*## Key Points

- How do you know when a stock will go up or down?
- Can machine learning predict?
- Trust metric by Neural Network

## AIG Target Price Prediction Modeling Methodology

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. We consider AIG Stock Decision Process with Paired T-Test where A is the set of discrete actions of AIG 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(Paired T-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 (DNN Layer)) X S(n):→ (n+4 weeks) $\sum _{i=1}^{n}\left({a}_{i}\right)$

n:Time series to forecast

p:Price signals of AIG 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?

## AIG Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**AIG AIG

**Time series to forecast n: 08 Nov 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold AIG 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 AIG

- In almost every lending transaction the creditor's instrument is ranked relative to the instruments of the debtor's other creditors. An instrument that is subordinated to other instruments may have contractual cash flows that are payments of principal and interest on the principal amount outstanding if the debtor's non-payment is a breach of contract and the holder has a contractual right to unpaid amounts of principal and interest on the principal amount outstanding even in the event of the debtor's bankruptcy. For example, a trade receivable that ranks its creditor as a general creditor would qualify as having payments of principal and interest on the principal amount outstanding. This is the case even if the debtor issued loans that are collateralised, which in the event of bankruptcy would give that loan holder priority over the claims of the general creditor in respect of the collateral but does not affect the contractual right of the general creditor to unpaid principal and other amounts due.
- A firm commitment to acquire a business in a business combination cannot be a hedged item, except for foreign currency risk, because the other risks being hedged cannot be specifically identified and measured. Those other risks are general business risks.
- An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
- For example, Entity A, whose functional currency is its local currency, has a firm commitment to pay FC150,000 for advertising expenses in nine months' time and a firm commitment to sell finished goods for FC150,000 in 15 months' time. Entity A enters into a foreign currency derivative that settles in nine months' time under which it receives FC100 and pays CU70. Entity A has no other exposures to FC. Entity A does not manage foreign currency risk on a net basis. Hence, Entity A cannot apply hedge accounting for a hedging relationship between the foreign currency derivative and a net position of FC100 (consisting of FC150,000 of the firm purchase commitment—ie advertising services—and FC149,900 (of the FC150,000) of the firm sale commitment) for a nine-month period.

*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

AIG assigned short-term Caa2 & long-term B1 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (DNN Layer) with Paired T-Test ^{1,2,3,4} and conclude that the AIG stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold AIG stock.**

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

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

Outlook* | Caa2 | B1 |

Operational Risk | 33 | 42 |

Market Risk | 55 | 41 |

Technical Analysis | 37 | 72 |

Fundamental Analysis | 50 | 90 |

Risk Unsystematic | 43 | 43 |

### Prediction Confidence Score

## References

- Friedberg R, Tibshirani J, Athey S, Wager S. 2018. Local linear forests. arXiv:1807.11408 [stat.ML]
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
- M. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
- Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
- J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
- 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.
- Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]

## Frequently Asked Questions

Q: What is the prediction methodology for AIG stock?A: AIG stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Paired T-Test

Q: Is AIG stock a buy or sell?

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

Q: Is AIG stock a good investment?

A: The consensus rating for AIG is Hold and assigned short-term Caa2 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of AIG stock?

A: The consensus rating for AIG is Hold.

Q: What is the prediction period for AIG stock?

A: The prediction period for AIG is (n+4 weeks)