Outlook: Metropolitan Bank Holding Corp. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Time series to forecast n: 03 May 2023 for (n+3 month)
Methodology : Ensemble Learning (ML)

## Abstract

Metropolitan Bank Holding Corp. Common Stock prediction model is evaluated with Ensemble Learning (ML) and Beta1,2,3,4 and it is concluded that the MCB 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

1. What is a prediction confidence?
2. Prediction Modeling
3. How do you decide buy or sell a stock?

## MCB Target Price Prediction Modeling Methodology

We consider Metropolitan Bank Holding Corp. Common Stock Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of MCB 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Ensemble 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 MCB 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?

## MCB Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: MCB Metropolitan Bank Holding Corp. Common Stock
Time series to forecast n: 03 May 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 Metropolitan Bank Holding Corp. Common Stock

1. To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.
2. In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.
3. Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
4. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.

*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

Metropolitan Bank Holding Corp. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Metropolitan Bank Holding Corp. Common Stock prediction model is evaluated with Ensemble Learning (ML) and Beta1,2,3,4 and it is concluded that the MCB 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

### MCB Metropolitan Bank Holding Corp. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa2Caa2
Balance SheetBaa2Baa2
Leverage RatiosB2Caa2
Cash FlowCBaa2
Rates of Return and ProfitabilityCBaa2

*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: 85 out of 100 with 493 signals.

## References

1. Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
2. V. Borkar. Stochastic approximation: a dynamical systems viewpoint. Cambridge University Press, 2008
3. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
4. R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
5. Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
6. Vapnik V. 2013. The Nature of Statistical Learning Theory. Berlin: Springer
7. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
Frequently Asked QuestionsQ: What is the prediction methodology for MCB stock?
A: MCB stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Beta
Q: Is MCB stock a buy or sell?
A: The dominant strategy among neural network is to Buy MCB Stock.
Q: Is Metropolitan Bank Holding Corp. Common Stock stock a good investment?
A: The consensus rating for Metropolitan Bank Holding Corp. Common Stock is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of MCB stock?
A: The consensus rating for MCB is Buy.
Q: What is the prediction period for MCB stock?
A: The prediction period for MCB is (n+3 month)