EBMT Stock: A Sinking Ship?

Outlook: Eagle Bancorp Montana Inc. Common 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
Methodology : Statistical Inference (ML)
Hypothesis Testing : Lasso 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

Eagle Bancorp Montana Inc. Common Stock prediction model is evaluated with Statistical Inference (ML) and Lasso Regression^1,2,3,4 and it is concluded that the EBMT stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.⁵ According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold

Key Points

1. Statistical Inference (ML) for EBMT stock price prediction process.
2. Lasso Regression
3. How can neural networks improve predictions?
4. How do you decide buy or sell a stock?
5. Should I buy stocks now or wait amid such uncertainty?

EBMT Stock Price Forecast

We consider Eagle Bancorp Montana Inc. Common Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of EBMT 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: EBMT Eagle Bancorp Montana Inc. Common Stock
Time series to forecast: 3 Month

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

F(Lasso Regression)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ 3 Month $\begin{array}{l}\int r^s\mathrm{rs}\end{array}$

n:Time series to forecast

p:Price signals of EBMT stock

j:Nash equilibria (Neural Network)

k:Dominated move of EBMT stock holders

a:Best response for EBMT target price

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.⁵ Lasso regression, also known as L1 regularization, 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 and to induce sparsity in the model. This is done by adding a term to the objective function that is proportional to the sum of the absolute values of the coefficients. The penalty term is called the "lasso" penalty, and it is controlled by a parameter called the "lasso constant". Lasso regression can be used to address the problem of multicollinearity in linear regression, as well as the problem of overfitting. 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. Overfitting occurs when a model is too closely fit to the training data, and as a result, it does not generalize well to new data.^6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

EBMT 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 Statistical Inference (ML) based EBMT Stock Prediction Model

1. If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).
2. Paragraph 6.3.4 permits an entity to designate as hedged items aggregated exposures that are a combination of an exposure and a derivative. When designating such a hedged item, an entity assesses whether the aggregated exposure combines an exposure with a derivative so that it creates a different aggregated exposure that is managed as one exposure for a particular risk (or risks). In that case, the entity may designate the hedged item on the basis of the aggregated exposure
3. When an entity designates a financial liability as at fair value through profit or loss, it must determine whether presenting in other comprehensive income the effects of changes in the liability's credit risk would create or enlarge an accounting mismatch in profit or loss. An accounting mismatch would be created or enlarged if presenting the effects of changes in the liability's credit risk in other comprehensive income would result in a greater mismatch in profit or loss than if those amounts were presented in profit or loss
4. 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.

*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.

EBMT Eagle Bancorp Montana Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B1	Ba3
Income Statement	Ba3	B3
Balance Sheet	Ba3	Baa2
Leverage Ratios	Baa2	B1
Cash Flow	B3	Baa2
Rates of Return and Profitability	C	C

*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

1. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
2. L. Prashanth and M. Ghavamzadeh. Actor-critic algorithms for risk-sensitive MDPs. In Proceedings of Advances in Neural Information Processing Systems 26, pages 252–260, 2013.
3. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
4. Belloni A, Chernozhukov V, Hansen C. 2014. High-dimensional methods and inference on structural and treatment effects. J. Econ. Perspect. 28:29–50
5. Cheung, Y. M.D. Chinn (1997), "Further investigation of the uncertain unit root in GNP," Journal of Business and Economic Statistics, 15, 68–73.
6. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
7. Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley

Frequently Asked Questions

Q: Is EBMT stock expected to rise?
A: EBMT stock prediction model is evaluated with Statistical Inference (ML) and Lasso Regression and it is concluded that dominant strategy for EBMT stock is Hold
Q: Is EBMT stock a buy or sell?
A: The dominant strategy among neural network is to Hold EBMT Stock.
Q: Is Eagle Bancorp Montana Inc. Common Stock stock a good investment?
A: The consensus rating for Eagle Bancorp Montana Inc. Common Stock is Hold and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of EBMT stock?
A: The consensus rating for EBMT is Hold.
Q: What is the forecast for EBMT stock?
A: EBMT target price forecast: Hold