Outlook: Sterling Bancorp Inc. Common Stock assigned short-term B2 & long-term Ba2 forecasted stock rating.
Dominant Strategy : Hold
Time series to forecast n: 10 Dec 2022 for (n+8 weeks)
Methodology : Supervised Machine Learning (ML)

## Abstract

Stock market forecasting is considered to be a challenging topic among time series forecasting. This study proposes a novel two-stage ensemble machine learning model named SVR-ENANFIS for stock price prediction by combining features of support vector regression (SVR) and ensemble adaptive neuro fuzzy inference system (ENANFIS).(Rather, A.M., Agarwal, A. and Sastry, V.N., 2015. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), pp.3234-3241.) We evaluate Sterling Bancorp Inc. Common Stock prediction models with Supervised Machine Learning (ML) and Polynomial Regression1,2,3,4 and conclude that the SBT 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

1. Trust metric by Neural Network
2. Reaction Function
3. Buy, Sell and Hold Signals

## SBT Target Price Prediction Modeling Methodology

We consider Sterling Bancorp Inc. Common Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of SBT 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(Polynomial 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(Supervised Machine Learning (ML)) X S(n):→ (n+8 weeks) $\begin{array}{l}\int {r}^{s}\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of SBT 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?

## SBT Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: SBT Sterling Bancorp Inc. Common Stock
Time series to forecast n: 10 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 Sterling Bancorp Inc. Common Stock

1. However, the fact that a financial asset is non-recourse does not in itself necessarily preclude the financial asset from meeting the condition in paragraphs 4.1.2(b) and 4.1.2A(b). In such situations, the creditor is required to assess ('look through to') the particular underlying assets or cash flows to determine whether the contractual cash flows of the financial asset being classified are payments of principal and interest on the principal amount outstanding. If the terms of the financial asset give rise to any other cash flows or limit the cash flows in a manner inconsistent with payments representing principal and interest, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b). Whether the underlying assets are financial assets or non-financial assets does not in itself affect this assessment.
2. The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.
3. An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.
4. 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).

*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

Sterling Bancorp Inc. Common Stock assigned short-term B2 & long-term Ba2 forecasted stock rating. We evaluate the prediction models Supervised Machine Learning (ML) with Polynomial Regression1,2,3,4 and conclude that the SBT 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 SBT Sterling Bancorp Inc. Common Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B2Ba2
Operational Risk 4983
Market Risk3043
Technical Analysis8669
Fundamental Analysis7565
Risk Unsystematic3979

### Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 681 signals.

## References

1. White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
2. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
3. J. Filar, D. Krass, and K. Ross. Percentile performance criteria for limiting average Markov decision pro- cesses. IEEE Transaction of Automatic Control, 40(1):2–10, 1995.
4. M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
5. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
6. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., What are buy sell or hold recommendations?(AIRC Stock Forecast). AC Investment Research Journal, 101(3).
7. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
Frequently Asked QuestionsQ: What is the prediction methodology for SBT stock?
A: SBT stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Polynomial Regression
Q: Is SBT stock a buy or sell?
A: The dominant strategy among neural network is to Hold SBT Stock.
Q: Is Sterling Bancorp Inc. Common Stock stock a good investment?
A: The consensus rating for Sterling Bancorp Inc. Common Stock is Hold and assigned short-term B2 & long-term Ba2 forecasted stock rating.
Q: What is the consensus rating of SBT stock?
A: The consensus rating for SBT is Hold.
Q: What is the prediction period for SBT stock?
A: The prediction period for SBT is (n+8 weeks)