Dominant Strategy : Buy
Time series to forecast n: 28 May 2023 for (n+16 weeks)
Methodology : Modular Neural Network (Market Volatility Analysis)
Abstract
Amalgamated Financial Corp. Common Stock (DE) prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the AMAL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: BuyKey Points
- Decision Making
- Can statistics predict the future?
- Can neural networks predict stock market?
AMAL Target Price Prediction Modeling Methodology
We consider Amalgamated Financial Corp. Common Stock (DE) Decision Process with Modular Neural Network (Market Volatility Analysis) where A is the set of discrete actions of AMAL 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(Polynomial Regression)5,6,7= X R(Modular Neural Network (Market Volatility Analysis)) X S(n):→ (n+16 weeks)
n:Time series to forecast
p:Price signals of AMAL 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?
AMAL Stock Forecast (Buy or Sell) for (n+16 weeks)
Sample Set: Neural NetworkStock/Index: AMAL Amalgamated Financial Corp. Common Stock (DE)
Time series to forecast n: 28 May 2023 for (n+16 weeks)
According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy
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 Amalgamated Financial Corp. Common Stock (DE)
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
- When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
- For the purpose of applying paragraph 6.5.11, at the point when an entity amends the description of a hedged item as required in paragraph 6.9.1(b), the amount accumulated in the cash flow hedge reserve shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows are determined.
- The credit risk on a financial instrument is considered low for the purposes of paragraph 5.5.10, if the financial instrument has a low risk of default, the borrower has a strong capacity to meet its contractual cash flow obligations in the near term and adverse changes in economic and business conditions in the longer term may, but will not necessarily, reduce the ability of the borrower to fulfil its contractual cash flow obligations. Financial instruments are not considered to have low credit risk when they are regarded as having a low risk of loss simply because of the value of collateral and the financial instrument without that collateral would not be considered low credit risk. Financial instruments are also not considered to have low credit risk simply because they have a lower risk of default than the entity's other financial instruments or relative to the credit risk of the jurisdiction within which an entity operates.
*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
Amalgamated Financial Corp. Common Stock (DE) is assigned short-term Ba1 & long-term Ba1 estimated rating. Amalgamated Financial Corp. Common Stock (DE) prediction model is evaluated with Modular Neural Network (Market Volatility Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the AMAL stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period, the dominant strategy among neural network is: Buy
AMAL Amalgamated Financial Corp. Common Stock (DE) Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | Ba1 |
Income Statement | Ba1 | B2 |
Balance Sheet | Caa2 | B1 |
Leverage Ratios | B3 | Caa2 |
Cash Flow | Baa2 | Baa2 |
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?
Prediction Confidence Score

References
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- Dudik M, Erhan D, Langford J, Li L. 2014. Doubly robust policy evaluation and optimization. Stat. Sci. 29:485–511
- G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
- 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.
- Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
Frequently Asked Questions
Q: What is the prediction methodology for AMAL stock?A: AMAL stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Volatility Analysis) and Polynomial Regression
Q: Is AMAL stock a buy or sell?
A: The dominant strategy among neural network is to Buy AMAL Stock.
Q: Is Amalgamated Financial Corp. Common Stock (DE) stock a good investment?
A: The consensus rating for Amalgamated Financial Corp. Common Stock (DE) is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of AMAL stock?
A: The consensus rating for AMAL is Buy.
Q: What is the prediction period for AMAL stock?
A: The prediction period for AMAL is (n+16 weeks)
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