**Outlook:**Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E is assigned short-term Ba1 & long-term Ba1 estimated rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 21 Mar 2023**for (n+1 year)

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

## Abstract

Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Beta^{1,2,3,4}and it is concluded that the CUBI^E stock is predictable in the short/long term.

**According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

## Key Points

- Can neural networks predict stock market?
- Technical Analysis with Algorithmic Trading
- Can machine learning predict?

## CUBI^E Target Price Prediction Modeling Methodology

We consider Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of CUBI^E 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(Beta)

^{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):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of CUBI^E 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?

## CUBI^E Stock Forecast (Buy or Sell) for (n+1 year)

**Sample Set:**Neural Network

**Stock/Index:**CUBI^E Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E

**Time series to forecast n: 21 Mar 2023**for (n+1 year)

**According to price forecasts for (n+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 Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E

- An entity can also designate only changes in the cash flows or fair value of a hedged item above or below a specified price or other variable (a 'one-sided risk'). The intrinsic value of a purchased option hedging instrument (assuming that it has the same principal terms as the designated risk), but not its time value, reflects a one-sided risk in a hedged item. For example, an entity can designate the variability of future cash flow outcomes resulting from a price increase of a forecast commodity purchase. In such a situation, the entity designates only cash flow losses that result from an increase in the price above the specified level. The hedged risk does not include the time value of a purchased option, because the time value is not a component of the forecast transaction that affects profit or loss.
- At the date of initial application, an entity is permitted to make the designation in paragraph 2.5 for contracts that already exist on the date but only if it designates all similar contracts. The change in the net assets resulting from such designations shall be recognised in retained earnings at the date of initial application.
- An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
- This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.

*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

Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E is assigned short-term Ba1 & long-term Ba1 estimated rating. Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Beta^{1,2,3,4} and it is concluded that the CUBI^E stock is predictable in the short/long term. ** According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell**

### CUBI^E Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E Financial Analysis*

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

Outlook* | Ba1 | Ba1 |

Income Statement | Caa2 | Baa2 |

Balance Sheet | B3 | Ba3 |

Leverage Ratios | Caa2 | Ba3 |

Cash Flow | Baa2 | Baa2 |

Rates of Return and Profitability | B1 | B3 |

*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

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- V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
- Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
- 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

## Frequently Asked Questions

Q: What is the prediction methodology for CUBI^E stock?A: CUBI^E stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Beta

Q: Is CUBI^E stock a buy or sell?

A: The dominant strategy among neural network is to Sell CUBI^E Stock.

Q: Is Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E stock a good investment?

A: The consensus rating for Customers Bancorp Inc Fixed-to-Floating Rate Non-Cumulative Perpetual Preferred Stock Series E is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.

Q: What is the consensus rating of CUBI^E stock?

A: The consensus rating for CUBI^E is Sell.

Q: What is the prediction period for CUBI^E stock?

A: The prediction period for CUBI^E is (n+1 year)