**Outlook:**FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock assigned short-term B1 & long-term B1 forecasted stock rating.

**Dominant Strategy :**Hold

**Time series to forecast n: 15 Dec 2022**for (n+8 weeks)

**Methodology :**Supervised Machine Learning (ML)

## Abstract

Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users' moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach.(Dase, R.K. and Pawar, D.D., 2010. Application of Artificial Neural Network for stock market predictions: A review of literature. International Journal of Machine Intelligence, 2(2), pp.14-17.)** We evaluate FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock prediction models with Supervised Machine Learning (ML) and Logistic Regression ^{1,2,3,4} and conclude that the FRC^K stock is predictable in the short/long term. **

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold**

## Key Points

- Technical Analysis with Algorithmic Trading
- What is prediction in deep learning?
- How can neural networks improve predictions?

## FRC^K Target Price Prediction Modeling Methodology

We consider FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of FRC^K 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Supervised Machine Learning (ML)) X S(n):→ (n+8 weeks) $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of FRC^K 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?

## FRC^K Stock Forecast (Buy or Sell) for (n+8 weeks)

**Sample Set:**Neural Network

**Stock/Index:**FRC^K FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock

**Time series to forecast n: 15 Dec 2022**for (n+8 weeks)

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold**

**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%**

## Adjusted IFRS* Prediction Methods for FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock

- If a financial asset contains a contractual term that could change the timing or amount of contractual cash flows (for example, if the asset can be prepaid before maturity or its term can be extended), the entity must determine whether the contractual cash flows that could arise over the life of the instrument due to that contractual term are solely payments of principal and interest on the principal amount outstanding. To make this determination, the entity must assess the contractual cash flows that could arise both before, and after, the change in contractual cash flows. The entity may also need to assess the nature of any contingent event (ie the trigger) that would change the timing or amount of the contractual cash flows. While the nature of the contingent event in itself is not a determinative factor in assessing whether the contractual cash flows are solely payments of principal and interest, it may be an indicator. For example, compare a financial instrument with an interest rate that is reset to a higher rate if the debtor misses a particular number of payments to a financial instrument with an interest rate that is reset to a higher rate if a specified equity index reaches a particular level. It is more likely in the former case that the contractual cash flows over the life of the instrument will be solely payments of principal and interest on the principal amount outstanding because of the relationship between missed payments and an increase in credit risk. (See also paragraph B4.1.18.)
- Adjusting the hedge ratio by decreasing the volume of the hedged item does not affect how the changes in the fair value of the hedging instrument are measured. The measurement of the changes in the value of the hedged item related to the volume that continues to be designated also remains unaffected. However, from the date of rebalancing, the volume by which the hedged item was decreased is no longer part of the hedging relationship. For example, if an entity originally hedged a volume of 100 tonnes of a commodity at a forward price of CU80 and reduces that volume by 10 tonnes on rebalancing, the hedged item after rebalancing would be 90 tonnes hedged at CU80. The 10 tonnes of the hedged item that are no longer part of the hedging relationship would be accounted for in accordance with the requirements for the discontinuation of hedge accounting (see paragraphs 6.5.6–6.5.7 and B6.5.22–B6.5.28).
- The risk of a default occurring on financial instruments that have comparable credit risk is higher the longer the expected life of the instrument; for example, the risk of a default occurring on an AAA-rated bond with an expected life of 10 years is higher than that on an AAA-rated bond with an expected life of five years.
- If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

## Conclusions

FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock assigned short-term B1 & long-term B1 forecasted stock rating.** We evaluate the prediction models Supervised Machine Learning (ML) with Logistic Regression ^{1,2,3,4} and conclude that the FRC^K stock is predictable in the short/long term.**

**According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold**

### Financial State Forecast for FRC^K FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock Options & Futures

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

Outlook* | B1 | B1 |

Operational Risk | 80 | 60 |

Market Risk | 56 | 72 |

Technical Analysis | 65 | 65 |

Fundamental Analysis | 57 | 68 |

Risk Unsystematic | 54 | 37 |

### Prediction Confidence Score

## References

- Bai J. 2003. Inferential theory for factor models of large dimensions. Econometrica 71:135–71
- Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA

## Frequently Asked Questions

Q: What is the prediction methodology for FRC^K stock?A: FRC^K stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Logistic Regression

Q: Is FRC^K stock a buy or sell?

A: The dominant strategy among neural network is to Hold FRC^K Stock.

Q: Is FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock stock a good investment?

A: The consensus rating for FIRST REPUBLIC BANK Depositary Shares Each Representing a 1/40th Interest in a Share of 4.125% Noncumulative Perpetual Series K Preferred Stock is Hold and assigned short-term B1 & long-term B1 forecasted stock rating.

Q: What is the consensus rating of FRC^K stock?

A: The consensus rating for FRC^K is Hold.

Q: What is the prediction period for FRC^K stock?

A: The prediction period for FRC^K is (n+8 weeks)