## Abstract

We do not deal with the refunds of leases as maturities of the debt (even if the standard of international financial information shows them as such in the cash flow state) because we have already reduced the FFO by means of said lease cash output.** We evaluate the prediction models (Volume with Pearson Correlation) ^{1,2,3} and conclude that the INTU 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 Hold INTU stock.**

**INTU, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis.**

*Keywords:*## Introduction

We consider the full spectrum of human trading interaction (varying from data based analysis to market signals, from trend actions to speculative ones and many more) and adapt them to the machine learning model with support of engineers to mimic and future-reflect everyday trading experiences. To do that we focus on an approach known as Decision making using Game Theory. We apply principles from Game Theory to model the relationships between rating actions, news, market signals and decision making.

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?

## INTU Stock Forecast (Buy or Sell) for (n+16 weeks)

**Stock/Index:**INTU Intuit

**Time series to forecast n: 05 Aug 2022**for (n+16 weeks)

**According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold INTU 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%**

*As part of stock rating surveillance, Neural network continuously analyze real-time and historical data. If network see events taking place that impact our view on an issuer's relative performance, we adjust our ratings accordingly to communicate our views so the market has the correct perception of how we view relative stock performance.

## What Are the Top Stocks to Invest in Right Now?

## Forecast Model for INTU

- 99.9 %, one -year svar proxy to get the regulatory stress Var (SVAR) load 2.3 multiplier. Basel 2.5 Unlike the 3.0 and 4.0 multipliers for banks that do not reside in the judicial regions, subject to the market risk framework, these multiplier fat does not contain add -on for comet. The reason for this is that in our opinion, the regulatory Svar already captures important stress periods.
- If the operating regulatory capital requirements are subject to capital requirements, a capital -based financial trigger will be based on a regulatory capital ratio. If not, the trigger shall be based on the ratio of equity-self by using the equity and assets of the reported members of the organization. We define MLI equity as equity paid from shareholders and accumulated snow reserves.
- The analytical approach for a joint business operations of a group is determined by the relevant methodologies to evaluate companies, financial institutions, insurance companies or others, such as fully reinforcing, partially reinforcing or calculating the operations while evaluating the SACP group. types of assets.
- Under our RACF, when a bank does not explain which model or which model combination it uses, we multiply it with any regulatory fee calculated using internal models (including, SVAR, IRC and CRM). We apply this multiplier, especially when a bank informs the sum of the regulatory fee calculated according to internal models without any deterioration by the component.
- When the dominant degree is 'B' or lower, the specific default scenario may be more predictable. If the sovereign degree is 'B' or 'B-', we can develop a country-specific scenario to determine whether we can evaluate the assets on the sovereigns. For 'CCC+' and the following dominant ratings, we expect the current stressful conditions to represent both our basic situation and the expected default scenario.
- In our opinion, the loan of financial institutions is generally lower than the credit of the sovereigns in which financial institutions reside. In order to reflect this, the RAC risk weight of financial institutions is higher than the RAC risk weight obtained from Model or the RAC risk weight corresponding to the degree of foreign currency in the sovereign where the enterprise is residence.
- The second multiplier is calculated as the following product: 1 + (1 + plug-in)*(1-non-MUAF exposure as a % share of Total OTC derivative exposure)/ Opposite exposure exposure as a total share of OTC derivatives exposure. This plug -in represents our estimation about the increasing risks represented by the other parties exempted by the other parties that are not exempt per unit.

## Conclusions

INTU assigned short-term B3 & long-term Ba2 forecasted stock rating.** We evaluate the prediction models (Volume with Pearson Correlation) ^{1,2,3} and conclude that the INTU 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 Hold INTU stock.**

### Financial State Forecast for Intuit

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

Outlook* | B3 | Ba2 |

Operational Risk | 63 | 71 |

Market Risk | 60 | 52 |

Technical Analysis | 40 | 77 |

Fundamental Analysis | 53 | 83 |

Risk Unsystematic | 34 | 57 |

### Prediction Confidence Score

## References

- Lagoudakis, Michail and Parr, Ronald. Reinforcement learning as classification: Leveraging modern classifiers. In ICML, volume 3, pp. 424–431, 2003.
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010.
- Prokhorov, Danil V, Wunsch, Donald C, et al. Adaptive critic designs. Neural Networks, IEEE Transactions on, 8(5):997–1007, 1997.