Outlook: Direct Selling Acquisition Corp. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 12 Feb 2023 for (n+8 weeks)
Methodology : Transfer Learning (ML)

## Abstract

Direct Selling Acquisition Corp. Class A Common Stock prediction model is evaluated with Transfer Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the DSAQ stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

## Key Points

1. Reaction Function
2. What is prediction in deep learning?
3. Market Risk

## DSAQ Target Price Prediction Modeling Methodology

We consider Direct Selling Acquisition Corp. Class A Common Stock Decision Process with Transfer Learning (ML) where A is the set of discrete actions of DSAQ 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(Logistic Regression)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ (n+8 weeks) $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of DSAQ stock

j:Nash equilibria (Neural Network)

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?

## DSAQ Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: DSAQ Direct Selling Acquisition Corp. Class A Common Stock
Time series to forecast n: 12 Feb 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) 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 Direct Selling Acquisition Corp. Class A Common Stock

1. The following example describes a situation in which an accounting mismatch would be created in profit or loss if the effects of changes in the credit risk of the liability were presented in other comprehensive income. A mortgage bank provides loans to customers and funds those loans by selling bonds with matching characteristics (eg amount outstanding, repayment profile, term and currency) in the market. The contractual terms of the loan permit the mortgage customer to prepay its loan (ie satisfy its obligation to the bank) by buying the corresponding bond at fair value in the market and delivering that bond to the mortgage bank. As a result of that contractual prepayment right, if the credit quality of the bond worsens (and, thus, the fair value of the mortgage bank's liability decreases), the fair value of the mortgage bank's loan asset also decreases. The change in the fair value of the asset reflects the mortgage customer's contractual right to prepay the mortgage loan by buying the underlying bond at fair value (which, in this example, has decreased) and delivering the bond to the mortgage bank. Consequently, the effects of changes in the credit risk of the liability (the bond) will be offset in profit or loss by a corresponding change in the fair value of a financial asset (the loan). If the effects of changes in the liability's credit risk were presented in other comprehensive income there would be an accounting mismatch in profit or loss. Consequently, the mortgage bank is required to present all changes in fair value of the liability (including the effects of changes in the liability's credit risk) in profit or loss.
2. When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
3. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
4. Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.

*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

Direct Selling Acquisition Corp. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Direct Selling Acquisition Corp. Class A Common Stock prediction model is evaluated with Transfer Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the DSAQ stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Sell

### DSAQ Direct Selling Acquisition Corp. Class A Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2Caa2
Balance SheetCaa2B3
Leverage RatiosCaa2Ba3
Cash FlowBa3Baa2
Rates of Return and ProfitabilityB2B1

*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

Trust metric by Neural Network: 77 out of 100 with 668 signals.

## References

1. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
2. Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
3. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Can neural networks predict stock market?(ATVI Stock Forecast). AC Investment Research Journal, 101(3).
4. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
5. Thompson WR. 1933. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25:285–94
6. Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
7. C. Wu and Y. Lin. Minimizing risk models in Markov decision processes with policies depending on target values. Journal of Mathematical Analysis and Applications, 231(1):47–67, 1999
Frequently Asked QuestionsQ: What is the prediction methodology for DSAQ stock?
A: DSAQ stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Logistic Regression
Q: Is DSAQ stock a buy or sell?
A: The dominant strategy among neural network is to Sell DSAQ Stock.
Q: Is Direct Selling Acquisition Corp. Class A Common Stock stock a good investment?
A: The consensus rating for Direct Selling Acquisition Corp. Class A Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of DSAQ stock?
A: The consensus rating for DSAQ is Sell.
Q: What is the prediction period for DSAQ stock?
A: The prediction period for DSAQ is (n+8 weeks)

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