HFBL Stock: A Risky Bet for Investors

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

Outlook: Home Federal Bancorp Inc. of Louisiana Common Stock is assigned short-term Ba3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Inductive Learning (ML)
Hypothesis Testing : Factor
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.

Home Federal Bancorp Inc. of Louisiana Common Stock prediction model is evaluated with Inductive Learning (ML) and Factor^1,2,3,4 and it is concluded that the HFBL stock is predictable in the short/long term. Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

Key Points

How can neural networks improve predictions?
Trading Signals
Can machine learning predict?

HFBL Stock Price Forecast

We consider Home Federal Bancorp Inc. of Louisiana Common Stock Decision Process with Inductive Learning (ML) where A is the set of discrete actions of HFBL 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: HFBL Home Federal Bancorp Inc. of Louisiana Common Stock
Time series to forecast: 6 Month

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

F(Factor)^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(Inductive Learning (ML)) X S(n):→ 6 Month

\begin{array}{l} \int e^{x} rx \end{array}

n:Time series to forecast

p:Price signals of HFBL stock

j:Nash equilibria (Neural Network)

k:Dominated move of HFBL stock holders

a:Best response for HFBL target price

Inductive learning is a type of machine learning in which the model learns from a set of labeled data and makes predictions about new, unlabeled data. The model is trained on the labeled data and then used to make predictions on new data. Inductive learning is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of inductive learning algorithms, including decision trees, support vector machines, and neural networks. Each type of algorithm has its own strengths and weaknesses.⁵ In statistics, a factor is a variable that can influence the value of another variable. Factors can be categorical or continuous. Categorical factors have a limited number of possible values, such as gender (male or female) or blood type (A, B, AB, or O). Continuous factors can have an infinite number of possible values, such as height or weight. Factors can be used to explain the variation in a dependent variable. For example, a study might find that there is a relationship between gender and height. In this case, gender would be the independent variable, height would be the dependent variable, and the factor would be gender.^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?

HFBL 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 Inductive Learning (ML) based HFBL Stock Prediction Model

An entity shall apply this Standard for annual periods beginning on or after 1 January 2018. Earlier application is permitted. If an entity elects to apply this Standard early, it must disclose that fact and apply all of the requirements in this Standard at the same time (but see also paragraphs 7.1.2, 7.2.21 and 7.3.2). It shall also, at the same time, apply the amendments in Appendix C.
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.
To make that determination, an entity must assess whether it expects that the effects of changes in the liability's credit risk will be offset in profit or loss by a change in the fair value of another financial instrument measured at fair value through profit or loss. Such an expectation must be based on an economic relationship between the characteristics of the liability and the characteristics of the other financial instrument.
Interest Rate Benchmark Reform—Phase 2, which amended IFRS 9, IAS 39, IFRS 7, IFRS 4 and IFRS 16, issued in August 2020, added paragraphs 5.4.5–5.4.9, 6.8.13, Section 6.9 and paragraphs 7.2.43–7.2.46. An entity shall apply these amendments for annual periods beginning on or after 1 January 2021. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.

*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.

HFBL Home Federal Bancorp Inc. of Louisiana Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Income Statement	Ba3	Ba2
Balance Sheet	Baa2	C
Leverage Ratios	B2	Baa2
Cash Flow	Caa2	B3
Rates of Return and Profitability	Baa2	B2

*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

Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
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.
Mnih A, Teh YW. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pp. 419–26. La Jolla, CA: Int. Mach. Learn. Soc.
Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
M. L. Littman. Friend-or-foe q-learning in general-sum games. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, pages 322–328, 2001
Bessler, D. A. S. W. Fuller (1993), "Cointegration between U.S. wheat markets," Journal of Regional Science, 33, 481–501.
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 HFBL stock?
A: HFBL stock prediction methodology: We evaluate the prediction models Inductive Learning (ML) and Factor
Q: Is HFBL stock a buy or sell?
A: The dominant strategy among neural network is to Sell HFBL Stock.
Q: Is Home Federal Bancorp Inc. of Louisiana Common Stock stock a good investment?
A: The consensus rating for Home Federal Bancorp Inc. of Louisiana Common Stock is Sell and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of HFBL stock?
A: The consensus rating for HFBL is Sell.
Q: What is the prediction period for HFBL stock?
A: The prediction period for HFBL is 6 Month

What did you think about the prediction? (Insufficient-Outstanding)

Tell us how we can improve PredictiveAI