Modelling A.I. in Economics

ZIONP Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) (Forecast)

Outlook: Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 03 Jan 2023 for (n+3 month)
Methodology : Multi-Task Learning (ML)

Abstract

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief r ́esum ́e of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Net- works (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. (Ticknor, J.L., 2013. A Bayesian regularized artificial neural network for stock market forecasting. Expert systems with applications, 40(14), pp.5501-5506.) We evaluate Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) prediction models with Multi-Task Learning (ML) and Beta1,2,3,4 and conclude that the ZIONP stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

Key Points

  1. What is prediction in deep learning?
  2. What is a prediction confidence?
  3. Buy, Sell and Hold Signals

ZIONP Target Price Prediction Modeling Methodology

We consider Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of ZIONP 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(Beta)5,6,7= 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(Multi-Task Learning (ML)) X S(n):→ (n+3 month) e x rx

n:Time series to forecast

p:Price signals of ZIONP 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?

ZIONP Stock Forecast (Buy or Sell) for (n+3 month)

Sample Set: Neural Network
Stock/Index: ZIONP Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock)
Time series to forecast n: 03 Jan 2023 for (n+3 month)

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

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 Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock)

  1. If a financial asset contains a contractual term that could change the timing or amount of contractual cash flows (for example, if the asset can be prepaid before maturity or its term can be extended), the entity must determine whether the contractual cash flows that could arise over the life of the instrument due to that contractual term are solely payments of principal and interest on the principal amount outstanding. To make this determination, the entity must assess the contractual cash flows that could arise both before, and after, the change in contractual cash flows. The entity may also need to assess the nature of any contingent event (ie the trigger) that would change the timing or amount of the contractual cash flows. While the nature of the contingent event in itself is not a determinative factor in assessing whether the contractual cash flows are solely payments of principal and interest, it may be an indicator. For example, compare a financial instrument with an interest rate that is reset to a higher rate if the debtor misses a particular number of payments to a financial instrument with an interest rate that is reset to a higher rate if a specified equity index reaches a particular level. It is more likely in the former case that the contractual cash flows over the life of the instrument will be solely payments of principal and interest on the principal amount outstanding because of the relationship between missed payments and an increase in credit risk. (See also paragraph B4.1.18.)
  2. If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
  3. For loan commitments, an entity considers changes in the risk of a default occurring on the loan to which a loan commitment relates. For financial guarantee contracts, an entity considers the changes in the risk that the specified debtor will default on the contract.
  4. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.

*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

Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Multi-Task Learning (ML) with Beta1,2,3,4 and conclude that the ZIONP stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Hold

ZIONP Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementBaa2Caa2
Balance SheetB3Ba2
Leverage RatiosB2Caa2
Cash FlowCC
Rates of Return and ProfitabilityBaa2Caa2

*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: 82 out of 100 with 459 signals.

References

  1. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
  2. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
  3. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
  4. Chow, G. C. (1960), "Tests of equality between sets of coefficients in two linear regressions," Econometrica, 28, 591–605.
  5. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
  6. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
  7. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
Frequently Asked QuestionsQ: What is the prediction methodology for ZIONP stock?
A: ZIONP stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Beta
Q: Is ZIONP stock a buy or sell?
A: The dominant strategy among neural network is to Hold ZIONP Stock.
Q: Is Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) stock a good investment?
A: The consensus rating for Zions Bancorporation N.A. Depositary Shares (Each representing 1/40th Interest in a Share of Series A Floating-Rate Non-Cumulative Perpetual Preferred Stock) is Hold and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ZIONP stock?
A: The consensus rating for ZIONP is Hold.
Q: What is the prediction period for ZIONP stock?
A: The prediction period for ZIONP is (n+3 month)

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