RF Regions Financial Corporation Common Stock (Forecast)

Outlook: Regions Financial Corporation Common Stock assigned short-term Ba3 & long-term B1 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 07 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (Speculative Sentiment Analysis)

Abstract

Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. (Bi, Q., Yan, H., Chen, C. and Su, Q., 2020, August. An integrated machine learning framework for stock price prediction. In China Conference on Information Retrieval (pp. 99-110). Springer, Cham.) We evaluate Regions Financial Corporation Common Stock prediction models with Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression^1,2,3,4 and conclude that the RF stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell RF stock.

Key Points

1. Investment Risk
2. Dominated Move
3. Fundemental Analysis with Algorithmic Trading

RF Target Price Prediction Modeling Methodology

We consider Regions Financial Corporation Common Stock Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of RF 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(Multiple Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of RF 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?

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

Sample Set: Neural Network
Stock/Index: RF Regions Financial Corporation Common Stock
Time series to forecast n: 07 Dec 2022 for (n+3 month)

According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell RF stock.

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 (Yellow to Green): *Technical Analysis%

Adjusted IFRS* Prediction Methods for Regions Financial Corporation Common Stock

1. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.
2. When designating a group of items as the hedged item, or a combination of financial instruments as the hedging instrument, an entity shall prospectively cease applying paragraphs 6.8.4–6.8.6 to an individual item or financial instrument in accordance with paragraphs 6.8.9, 6.8.10, or 6.8.11, as relevant, when the uncertainty arising from interest rate benchmark reform is no longer present with respect to the hedged risk and/or the timing and the amount of the interest rate benchmark-based cash flows of that item or financial instrument.
3. For the purpose of determining whether a forecast transaction (or a component thereof) is highly probable as required by paragraph 6.3.3, an entity shall assume that the interest rate benchmark on which the hedged cash flows (contractually or non-contractually specified) are based is not altered as a result of interest rate benchmark reform.
4. 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.

*International Financial Reporting Standards (IFRS) are a set of accounting rules for the financial statements of public companies that are intended to make them consistent, transparent, and easily comparable around the world.

Conclusions

Regions Financial Corporation Common Stock assigned short-term Ba3 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) with Multiple Regression^1,2,3,4 and conclude that the RF stock is predictable in the short/long term. According to price forecasts for (n+3 month) period: The dominant strategy among neural network is to Sell RF stock.

Financial State Forecast for RF Regions Financial Corporation Common Stock Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	Ba3	B1
Operational Risk	73	30
Market Risk	85	63
Technical Analysis	61	76
Fundamental Analysis	54	50
Risk Unsystematic	46	70

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 504 signals.

References

1. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
2. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
3. Alexander, J. C. Jr. (1995), "Refining the degree of earnings surprise: A comparison of statistical and analysts' forecasts," Financial Review, 30, 469–506.
4. R. Sutton and A. Barto. Introduction to reinforcement learning. MIT Press, 1998
5. Jiang N, Li L. 2016. Doubly robust off-policy value evaluation for reinforcement learning. In Proceedings of the 33rd International Conference on Machine Learning, pp. 652–61. La Jolla, CA: Int. Mach. Learn. Soc.
6. A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
7. H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.

Frequently Asked Questions

Q: What is the prediction methodology for RF stock?
A: RF stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Multiple Regression
Q: Is RF stock a buy or sell?
A: The dominant strategy among neural network is to Sell RF Stock.
Q: Is Regions Financial Corporation Common Stock stock a good investment?
A: The consensus rating for Regions Financial Corporation Common Stock is Sell and assigned short-term Ba3 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of RF stock?
A: The consensus rating for RF is Sell.
Q: What is the prediction period for RF stock?
A: The prediction period for RF is (n+3 month)

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