**Outlook:**Blue Safari Group Acquisition Corp. Class A Ordinary Share is assigned short-term Baa2 & long-term B1 estimated rating.

**AUC Score :**

**Short-Term Revised* :**

**Dominant Strategy :**Sell

**Time series to forecast n:** for 1 Year

**Methodology :**Modular Neural Network (Market Volatility Analysis)

**Hypothesis Testing :**Polynomial Regression

## Summary

Blue Safari Group Acquisition Corp. Class A Ordinary Share prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression^{1,2,3,4}and it is concluded that the BSGA stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.

**According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell**

***Revision**

We revised our short-term strategy to

## Key Points

- Operational Risk
- Can stock prices be predicted?
- What are the most successful trading algorithms?

## BSGA Target Price Prediction Modeling Methodology

We consider Blue Safari Group Acquisition Corp. Class A Ordinary Share Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of BSGA 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(Polynomial 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):→ 1 Year $\sum _{i=1}^{n}\left({s}_{i}\right)$

n:Time series to forecast

p:Price signals of BSGA stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

### Modular Neural Network (Market Volatility Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for market volatility analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market volatility analysis, MNNs can be used to identify patterns in market data that suggest that the market is becoming more or less volatile. This information can then be used to make predictions about future price movements.### Polynomial Regression

Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.

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?

## BSGA Stock Forecast (Buy or Sell) for 1 Year

**Sample Set:**Neural Network

**Stock/Index:**BSGA Blue Safari Group Acquisition Corp. Class A Ordinary Share

**Time series to forecast:**1 Year

**According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell**

**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 (Grey to Black): *Technical Analysis%**

## IFRS Reconciliation Adjustments for Blue Safari Group Acquisition Corp. Class A Ordinary Share

- An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
- An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.
- When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
- In some circumstances, the renegotiation or modification of the contractual cash flows of a financial asset can lead to the derecognition of the existing financial asset in accordance with this Standard. When the modification of a financial asset results in the derecognition of the existing financial asset and the subsequent recognition of the modified financial asset, the modified asset is considered a 'new' financial asset for the purposes of this Standard.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

## Conclusions

Blue Safari Group Acquisition Corp. Class A Ordinary Share is assigned short-term Baa2 & long-term B1 estimated rating. Blue Safari Group Acquisition Corp. Class A Ordinary Share prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression^{1,2,3,4} and it is concluded that the BSGA stock is predictable in the short/long term. ** According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell**

### BSGA Blue Safari Group Acquisition Corp. Class A Ordinary Share Financial Analysis*

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

Outlook* | Baa2 | B1 |

Income Statement | B3 | C |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Ba1 | B1 |

Cash Flow | Baa2 | Ba3 |

Rates of Return and Profitability | Baa2 | Caa2 |

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.

How does neural network examine financial reports and understand financial state of the company?

### Prediction Confidence Score

## References

- M. Ono, M. Pavone, Y. Kuwata, and J. Balaram. Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Autonomous Robots, 39(4):555–571, 2015
- K. Tumer and D. Wolpert. A survey of collectives. In K. Tumer and D. Wolpert, editors, Collectives and the Design of Complex Systems, pages 1–42. Springer, 2004.
- Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press
- Bierens HJ. 1987. Kernel estimators of regression functions. In Advances in Econometrics: Fifth World Congress, Vol. 1, ed. TF Bewley, pp. 99–144. Cambridge, UK: Cambridge Univ. Press
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276

## Frequently Asked Questions

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

Q: Is BSGA stock a buy or sell?

A: The dominant strategy among neural network is to Sell BSGA Stock.

Q: Is Blue Safari Group Acquisition Corp. Class A Ordinary Share stock a good investment?

A: The consensus rating for Blue Safari Group Acquisition Corp. Class A Ordinary Share is Sell and is assigned short-term Baa2 & long-term B1 estimated rating.

Q: What is the consensus rating of BSGA stock?

A: The consensus rating for BSGA is Sell.

Q: What is the prediction period for BSGA stock?

A: The prediction period for BSGA is 1 Year

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