**Outlook:**Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Buy

**Time series to forecast n: 04 Feb 2023**for (n+3 month)

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

## Abstract

Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Spearman Correlation^{1,2,3,4}and it is concluded that the GS^A stock is predictable in the short/long term.

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

## Key Points

- What statistical methods are used to analyze data?
- What is statistical models in machine learning?
- Prediction Modeling

## GS^A Target Price Prediction Modeling Methodology

We consider Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of GS^A 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(Spearman Correlation)

^{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 Direction Analysis)) X S(n):→ (n+3 month) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of GS^A 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?

## GS^A Stock Forecast (Buy or Sell) for (n+3 month)

**Sample Set:**Neural Network

**Stock/Index:**GS^A Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A

**Time series to forecast n: 04 Feb 2023**for (n+3 month)

**According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

**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 Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A

- When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.
- To calculate the change in the value of the hedged item for the purpose of measuring hedge ineffectiveness, an entity may use a derivative that would have terms that match the critical terms of the hedged item (this is commonly referred to as a 'hypothetical derivative'), and, for example for a hedge of a forecast transaction, would be calibrated using the hedged price (or rate) level. For example, if the hedge was for a two-sided risk at the current market level, the hypothetical derivative would represent a hypothetical forward contract that is calibrated to a value of nil at the time of designation of the hedging relationship. If the hedge was for example for a one-sided risk, the hypothetical derivative would represent the intrinsic value of a hypothetical option that at the time of designation of the hedging relationship is at the money if the hedged price level is the current market level, or out of the money if the hedged price level is above (or, for a hedge of a long position, below) the current market level. Using a hypothetical derivative is one possible way of calculating the change in the value of the hedged item. The hypothetical derivative replicates the hedged item and hence results in the same outcome as if that change in value was determined by a different approach. Hence, using a 'hypothetical derivative' is not a method in its own right but a mathematical expedient that can only be used to calculate the value of the hedged item. Consequently, a 'hypothetical derivative' cannot be used to include features in the value of the hedged item that only exist in the hedging instrument (but not in the hedged item). An example is debt denominated in a foreign currency (irrespective of whether it is fixed-rate or variable-rate debt). When using a hypothetical derivative to calculate the change in the value of such debt or the present value of the cumulative change in its cash flows, the hypothetical derivative cannot simply impute a charge for exchanging different currencies even though actual derivatives under which different currencies are exchanged might include such a charge (for example, cross-currency interest rate swaps).
- It would not be acceptable to designate only some of the financial assets and financial liabilities giving rise to the inconsistency as at fair value through profit or loss if to do so would not eliminate or significantly reduce the inconsistency and would therefore not result in more relevant information. However, it would be acceptable to designate only some of a number of similar financial assets or similar financial liabilities if doing so achieves a significant reduction (and possibly a greater reduction than other allowable designations) in the inconsistency. For example, assume an entity has a number of similar financial liabilities that sum to CU100 and a number of similar financial assets that sum to CU50 but are measured on a different basis. The entity may significantly reduce the measurement inconsistency by designating at initial recognition all of the assets but only some of the liabilities (for example, individual liabilities with a combined total of CU45) as at fair value through profit or loss. However, because designation as at fair value through profit or loss can be applied only to the whole of a financial instrument, the entity in this example must designate one or more liabilities in their entirety. It could not designate either a component of a liability (eg changes in value attributable to only one risk, such as changes in a benchmark interest rate) or a proportion (ie percentage) of a liability.
- An entity applies IAS 21 to financial assets and financial liabilities that are monetary items in accordance with IAS 21 and denominated in a foreign currency. IAS 21 requires any foreign exchange gains and losses on monetary assets and monetary liabilities to be recognised in profit or loss. An exception is a monetary item that is designated as a hedging instrument in a cash flow hedge (see paragraph 6.5.11), a hedge of a net investment (see paragraph 6.5.13) or a fair value hedge of an equity instrument for which an entity has elected to present changes in fair value in other comprehensive income in accordance with paragraph 5.7.5 (see paragraph 6.5.8).

*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

Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A is assigned short-term Ba1 & long-term Ba1 estimated rating. Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Spearman Correlation^{1,2,3,4} and it is concluded that the GS^A stock is predictable in the short/long term. ** According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy**

### GS^A Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | C | Caa2 |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Ba3 | Ba2 |

Cash Flow | Ba3 | Baa2 |

Rates of Return and Profitability | B3 | 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

- Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
- K. Boda, J. Filar, Y. Lin, and L. Spanjers. Stochastic target hitting time and the problem of early retirement. Automatic Control, IEEE Transactions on, 49(3):409–419, 2004
- Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
- Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
- R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998

## Frequently Asked Questions

Q: What is the prediction methodology for GS^A stock?A: GS^A stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Spearman Correlation

Q: Is GS^A stock a buy or sell?

A: The dominant strategy among neural network is to Buy GS^A Stock.

Q: Is Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A stock a good investment?

A: The consensus rating for Goldman Sachs Group Inc. (The) Depositary Shares each representing 1/1000th Interest in a Share of Floating Rate Non-Cumulative Preferred Stock Series A is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of GS^A stock?

A: The consensus rating for GS^A is Buy.

Q: What is the prediction period for GS^A stock?

A: The prediction period for GS^A is (n+3 month)