Modelling A.I. in Economics

Should You Buy HBANL Right Now? (Forecast)

Outlook: Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J 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 : Buy
Time series to forecast n: for Weeks2
Methodology : Transductive Learning (ML)
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

Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J Non-Cumulative Perpetual Preferred Stock prediction model is evaluated with Transductive Learning (ML) and Logistic Regression1,2,3,4 and it is concluded that the HBANL stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.5 According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

Graph 25

Key Points

  1. Transductive Learning (ML) for HBANL stock price prediction process.
  2. Logistic Regression
  3. Can neural networks predict stock market?
  4. What are the most successful trading algorithms?
  5. Stock Rating

HBANL Stock Price Forecast

We consider Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J Non-Cumulative Perpetual Preferred Stock Decision Process with Transductive Learning (ML) where A is the set of discrete actions of HBANL 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: HBANL Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J Non-Cumulative Perpetual Preferred Stock
Time series to forecast: 3 Month

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


F(Logistic Regression)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Transductive Learning (ML)) X S(n):→ 3 Month i = 1 n a i

n:Time series to forecast

p:Price signals of HBANL stock

j:Nash equilibria (Neural Network)

k:Dominated move of HBANL stock holders

a:Best response for HBANL target price


Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.5 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.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?

HBANL 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 Transductive Learning (ML) based HBANL Stock Prediction Model

  1. When assessing a modified time value of money element, an entity must consider factors that could affect future contractual cash flows. For example, if an entity is assessing a bond with a five-year term and the variable interest rate is reset every six months to a five-year rate, the entity cannot conclude that the contractual cash flows are solely payments of principal and interest on the principal amount outstanding simply because the interest rate curve at the time of the assessment is such that the difference between a five-year interest rate and a six-month interest rate is not significant. Instead, the entity must also consider whether the relationship between the five-year interest rate and the six-month interest rate could change over the life of the instrument such that the contractual (undiscounted) cash flows over the life of the instrument could be significantly different from the (undiscounted) benchmark cash flows. However, an entity must consider only reasonably possible scenarios instead of every possible scenario. If an entity concludes that the contractual (undiscounted) cash flows could be significantly different from the (undiscounted) benchmark cash flows, the financial asset does not meet the condition in paragraphs 4.1.2(b) and 4.1.2A(b) and therefore cannot be measured at amortised cost or fair value through other comprehensive income.
  2. At the date of initial application, an entity shall use reasonable and supportable information that is available without undue cost or effort to determine the credit risk at the date that a financial instrument was initially recognised (or for loan commitments and financial guarantee contracts at the date that the entity became a party to the irrevocable commitment in accordance with paragraph 5.5.6) and compare that to the credit risk at the date of initial application of this Standard.
  3. If an entity previously accounted at cost (in accordance with IAS 39), for an investment in an equity instrument that does not have a quoted price in an active market for an identical instrument (ie a Level 1 input) (or for a derivative asset that is linked to and must be settled by delivery of such an equity instrument) it shall measure that instrument at fair value at the date of initial application. Any difference between the previous carrying amount and the fair value shall be recognised in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.
  4. Rebalancing does not apply if the risk management objective for a hedging relationship has changed. Instead, hedge accounting for that hedging relationship shall be discontinued (despite that an entity might designate a new hedging relationship that involves the hedging instrument or hedged item of the previous hedging relationship as described in paragraph B6.5.28).

*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.

HBANL Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J Non-Cumulative Perpetual Preferred Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Income StatementCBaa2
Balance SheetBa3Caa2
Leverage RatiosBa3Baa2
Cash FlowB1B2
Rates of Return and ProfitabilityBa1Caa2

*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. Abadir, K. M., K. Hadri E. Tzavalis (1999), "The influence of VAR dimensions on estimator biases," Econometrica, 67, 163–181.
  2. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Can Neural Networks Predict Stock Market?. AC Investment Research Journal, 220(44).
  3. L. Panait and S. Luke. Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3):387–434, 2005.
  4. Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
  5. E. Altman, K. Avrachenkov, and R. N ́u ̃nez-Queija. Perturbation analysis for denumerable Markov chains with application to queueing models. Advances in Applied Probability, pages 839–853, 2004
  6. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
  7. Meinshausen N. 2007. Relaxed lasso. Comput. Stat. Data Anal. 52:374–93
Frequently Asked QuestionsQ: Is HBANL stock expected to rise?
A: HBANL stock prediction model is evaluated with Transductive Learning (ML) and Logistic Regression and it is concluded that dominant strategy for HBANL stock is Buy
Q: Is HBANL stock a buy or sell?
A: The dominant strategy among neural network is to Buy HBANL Stock.
Q: Is Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J Non-Cumulative Perpetual Preferred Stock stock a good investment?
A: The consensus rating for Huntington Bancshares Incorporated Depositary Shares Each Representing a 1/40th Interest in a Share of 6.875% Series J Non-Cumulative Perpetual Preferred Stock is Buy and is assigned short-term B1 & long-term Ba3 estimated rating.
Q: What is the consensus rating of HBANL stock?
A: The consensus rating for HBANL is Buy.
Q: What is the forecast for HBANL stock?
A: HBANL target price forecast: Buy

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