Outlook: FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 22 Dec 2022 for (n+3 month)
Methodology : Active Learning (ML)

## Abstract

Social media comments have in the past had an instantaneous effect on stock markets. This paper investigates the sentiments expressed on the social media platform Twitter and their pr edictive impact on the Stock Market. (Saad, E.W., Prokhorov, D.V. and Wunsch, D.C., 1998. Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on neural networks, 9(6), pp.1456-1470.) We evaluate FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share prediction models with Active Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and conclude that the FRC^H stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

## Key Points

1. Can statistics predict the future?
3. What is the use of Markov decision process?

## FRC^H Target Price Prediction Modeling Methodology

We consider FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share Decision Process with Active Learning (ML) where A is the set of discrete actions of FRC^H 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(Statistical Hypothesis Testing)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(Active Learning (ML)) X S(n):→ (n+3 month) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of FRC^H 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^H Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: FRC^H FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share
Time series to forecast n: 22 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) 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%

## IFRS Reconciliation Adjustments for FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share

1. If there is a hedging relationship between a non-derivative monetary asset and a non-derivative monetary liability, changes in the foreign currency component of those financial instruments are presented in profit or loss.
2. When an entity first applies this Standard, it may choose as its accounting policy to continue to apply the hedge accounting requirements of IAS 39 instead of the requirements in Chapter 6 of this Standard. An entity shall apply that policy to all of its hedging relationships. An entity that chooses that policy shall also apply IFRIC 16 Hedges of a Net Investment in a Foreign Operation without the amendments that conform that Interpretation to the requirements in Chapter 6 of this Standard.
3. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).
4. For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.

*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

FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Active Learning (ML) with Statistical Hypothesis Testing1,2,3,4 and conclude that the FRC^H stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

### FRC^H FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa3Baa2
Balance SheetCBaa2
Leverage RatiosCaa2Ba3
Cash FlowB3Baa2
Rates of Return and ProfitabilityBaa2Baa2

*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 729 signals.

## References

1. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
2. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
3. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
4. Y. Le Tallec. Robust, risk-sensitive, and data-driven control of Markov decision processes. PhD thesis, Massachusetts Institute of Technology, 2007.
5. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
6. Athey S, Imbens GW. 2017a. The econometrics of randomized experiments. In Handbook of Economic Field Experiments, Vol. 1, ed. E Duflo, A Banerjee, pp. 73–140. Amsterdam: Elsevier
7. Bottou L. 2012. Stochastic gradient descent tricks. In Neural Networks: Tricks of the Trade, ed. G Montavon, G Orr, K-R Müller, pp. 421–36. Berlin: Springer
Frequently Asked QuestionsQ: What is the prediction methodology for FRC^H stock?
A: FRC^H stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Statistical Hypothesis Testing
Q: Is FRC^H stock a buy or sell?
A: The dominant strategy among neural network is to Hold FRC^H Stock.
Q: Is FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share stock a good investment?
A: The consensus rating for FIRST REPUBLIC BANK Depositary Shares each representing a 1/40th interest in a share of 5.125% Noncumulative Perpetual Series H Preferred Stock par value \$0.01 per share is Hold and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of FRC^H stock?
A: The consensus rating for FRC^H is Hold.
Q: What is the prediction period for FRC^H stock?
A: The prediction period for FRC^H is (n+3 month)