Outlook: Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock is assigned short-term B1 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n: for Weeks2
Hypothesis Testing : Logistic Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.

## Summary

Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock prediction model is evaluated with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the WBS^G stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

## Key Points

1. Short/Long Term Stocks
2. What is the best way to predict stock prices?
3. What are the most successful trading algorithms?

## WBS^G Target Price Prediction Modeling Methodology

We consider Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of WBS^G 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= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Multi-Task Learning (ML)) X S(n):→ 1 Year $\stackrel{\to }{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of WBS^G stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.

### Logistic Regression

In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.

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?

## WBS^G Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: WBS^G Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock
Time series to forecast: 1 Year

According to price forecasts, the dominant strategy among neural network is: Hold

Strategic Interaction Table Legend:

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%

### Financial Data Adjustments for Multi-Task Learning (ML) based WBS^G Stock Prediction Model

1. An entity that first applies IFRS 17 as amended in June 2020 after it first applies this Standard shall apply paragraphs 7.2.39–7.2.42. The entity shall also apply the other transition requirements in this Standard necessary for applying these amendments. For that purpose, references to the date of initial application shall be read as referring to the beginning of the reporting period in which an entity first applies these amendments (date of initial application of these amendments).
2. The accounting for the time value of options in accordance with paragraph 6.5.15 applies only to the extent that the time value relates to the hedged item (aligned time value). The time value of an option relates to the hedged item if the critical terms of the option (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the option and the hedged item are not fully aligned, an entity shall determine the aligned time value, ie how much of the time value included in the premium (actual time value) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.15). An entity determines the aligned time value using the valuation of the option that would have critical terms that perfectly match the hedged item.
3. The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.
4. In accordance with paragraph 4.1.3(a), principal is the fair value of the financial asset at initial recognition. However that principal amount may change over the life of the financial asset (for example, if there are repayments of principal).

*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.

### WBS^G Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Income StatementCaa2B3
Balance SheetBaa2Ba3
Leverage RatiosBaa2Baa2
Cash FlowB2Baa2
Rates of Return and ProfitabilityB1B3

*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?

## Conclusions

Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock is assigned short-term B1 & long-term Ba3 estimated rating. Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock prediction model is evaluated with Multi-Task Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the WBS^G stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Hold

### Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 739 signals.

## References

1. Bastani H, Bayati M. 2015. Online decision-making with high-dimensional covariates. Work. Pap., Univ. Penn./ Stanford Grad. School Bus., Philadelphia/Stanford, CA
2. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
3. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
4. S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
5. S. Bhatnagar, R. Sutton, M. Ghavamzadeh, and M. Lee. Natural actor-critic algorithms. Automatica, 45(11): 2471–2482, 2009
6. Breusch, T. S. (1978), "Testing for autocorrelation in dynamic linear models," Australian Economic Papers, 17, 334–355.
7. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
Frequently Asked QuestionsQ: What is the prediction methodology for WBS^G stock?
A: WBS^G stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Logistic Regression
Q: Is WBS^G stock a buy or sell?
A: The dominant strategy among neural network is to Hold WBS^G Stock.
Q: Is Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock stock a good investment?
A: The consensus rating for Webster Financial Corporation Depositary Shares each representing a 1/40th interest in a share of 6.50% Series G non-cumulative perpetual preferred stock is Hold and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of WBS^G stock?
A: The consensus rating for WBS^G is Hold.
Q: What is the prediction period for WBS^G stock?
A: The prediction period for WBS^G is 1 Year