Outlook: Hecla Mining Company Preferred Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 28 Jan 2023 for (n+3 month)
Methodology : Modular Neural Network (News Feed Sentiment Analysis)

## Abstract

Hecla Mining Company Preferred Stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the HL^B stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

## Key Points

1. What is statistical models in machine learning?
2. Nash Equilibria
3. Game Theory

## HL^B Target Price Prediction Modeling Methodology

We consider Hecla Mining Company Preferred Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of HL^B 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(Statistical Hypothesis Testing)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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+3 month) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of HL^B 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?

## HL^B Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: HL^B Hecla Mining Company Preferred Stock
Time series to forecast n: 28 Jan 2023 for (n+3 month)

According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

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%

## IFRS Reconciliation Adjustments for Hecla Mining Company Preferred Stock

1. Expected credit losses reflect an entity's own expectations of credit losses. However, when considering all reasonable and supportable information that is available without undue cost or effort in estimating expected credit losses, an entity should also consider observable market information about the credit risk of the particular financial instrument or similar financial instruments.
2. An entity must look through until it can identify the underlying pool of instruments that are creating (instead of passing through) the cash flows. This is the underlying pool of financial instruments.
3. Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period
4. The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.

*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.

## Conclusions

Hecla Mining Company Preferred Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Hecla Mining Company Preferred Stock prediction model is evaluated with Modular Neural Network (News Feed Sentiment Analysis) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the HL^B stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Sell

### HL^B Hecla Mining Company Preferred Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBa2B2
Balance SheetBaa2Baa2
Leverage RatiosB1Caa2
Cash FlowCaa2Baa2
Rates of Return and ProfitabilityBaa2B1

*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?

### Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 836 signals.

## References

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2. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
3. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
4. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
5. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
6. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
7. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
Frequently Asked QuestionsQ: What is the prediction methodology for HL^B stock?
A: HL^B stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Statistical Hypothesis Testing
Q: Is HL^B stock a buy or sell?
A: The dominant strategy among neural network is to Sell HL^B Stock.
Q: Is Hecla Mining Company Preferred Stock stock a good investment?
A: The consensus rating for Hecla Mining Company Preferred Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of HL^B stock?
A: The consensus rating for HL^B is Sell.
Q: What is the prediction period for HL^B stock?
A: The prediction period for HL^B is (n+3 month)