MSBIP Stock: A Spark That Could Fizzle

Jorgenson, Dale W.; Weitzman, Martin L.; ZXhang, Yoshua X; Haxo, Yann M; Mat , Ying X

Outlook: Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : HoldSpeculative Trend
Time series to forecast n: for Weeks²
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Ridge 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.

Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A prediction model is evaluated with Multi-Task Learning (ML) and Ridge Regression^1,2,3,4 and it is concluded that the MSBIP 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.⁵ According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: HoldSpeculative Trend

Key Points

Trust metric by Neural Network
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Prediction Modeling

MSBIP Stock Price Forecast

We consider Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of MSBIP 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

Sample Set: Neural Network
Stock/Index: MSBIP Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A
Time series to forecast: 4 Weeks

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

F(Ridge Regression)^6,7=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Multi-Task Learning (ML)) X S(n):→ 4 Weeks

\vec{S} = (s_{1}, s_{2}, s_{3})

n:Time series to forecast

p:Price signals of MSBIP stock

j:Nash equilibria (Neural Network)

k:Dominated move of MSBIP stock holders

a:Best response for MSBIP 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.⁵ Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.^6,7

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?

MSBIP Stock Forecast (Buy or Sell) Strategic Interaction Table

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 MSBIP Stock Prediction Model

If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.
Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.
A similar example of a non-financial item is a specific type of crude oil from a particular oil field that is priced off the relevant benchmark crude oil. If an entity sells that crude oil under a contract using a contractual pricing formula that sets the price per barrel at the benchmark crude oil price minus CU10 with a floor of CU15, the entity can designate as the hedged item the entire cash flow variability under the sales contract that is attributable to the change in the benchmark crude oil price. However, the entity cannot designate a component that is equal to the full change in the benchmark crude oil price. Hence, as long as the forward price (for each delivery) does not fall below CU25, the hedged item has the same cash flow variability as a crude oil sale at the benchmark crude oil price (or with a positive spread). However, if the forward price for any delivery falls below CU25, the hedged item has a lower cash flow variability than a crude oil sale at the benchmark crude oil price (or with a positive spread).
Fluctuation around a constant hedge ratio (and hence the related hedge ineffectiveness) cannot be reduced by adjusting the hedge ratio in response to each particular outcome. Hence, in such circumstances, the change in the extent of offset is a matter of measuring and recognising hedge ineffectiveness but does not require rebalancing.

*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.

MSBIP Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Income Statement	Caa2	Baa2
Balance Sheet	Caa2	Baa2
Leverage Ratios	B2	C
Cash Flow	Caa2	C
Rates of Return and Profitability	B2	Baa2

*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?

References

H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
Ashley, R. (1988), "On the relative worth of recent macroeconomic forecasts," International Journal of Forecasting, 4, 363–376.
Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
S. Proper and K. Tumer. Modeling difference rewards for multiagent learning (extended abstract). In Proceedings of the Eleventh International Joint Conference on Autonomous Agents and Multiagent Systems, Valencia, Spain, June 2012
Efron B, Hastie T. 2016. Computer Age Statistical Inference, Vol. 5. Cambridge, UK: Cambridge Univ. Press

Frequently Asked Questions

Q: What is the prediction methodology for MSBIP stock?
A: MSBIP stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Ridge Regression
Q: Is MSBIP stock a buy or sell?
A: The dominant strategy among neural network is to HoldSpeculative Trend MSBIP Stock.
Q: Is Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A stock a good investment?
A: The consensus rating for Midland States Bancorp Inc. Depositary Shares Each Representing a 1/40th Interest in a Share of 7.750% Fixed-Rate Reset Non-Cumulative Perpetual Preferred Stock Series A is HoldSpeculative Trend and is assigned short-term B3 & long-term B1 estimated rating.
Q: What is the consensus rating of MSBIP stock?
A: The consensus rating for MSBIP is HoldSpeculative Trend.
Q: What is the prediction period for MSBIP stock?
A: The prediction period for MSBIP is 4 Weeks

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