FORG ForgeRock Inc. Class A Common Stock

Outlook: ForgeRock Inc. Class A Common Stock assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 30 Dec 2022 for (n+8 weeks)
Methodology : Modular Neural Network (Social Media Sentiment Analysis)

Abstract

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted.(Mehtab, S. and Sen, J., 2020. A time series analysis-based stock price prediction using machine learning and deep learning models. arXiv preprint arXiv:2004.11697.) We evaluate ForgeRock Inc. Class A Common Stock prediction models with Modular Neural Network (Social Media Sentiment Analysis) and Independent T-Test^1,2,3,4 and conclude that the FORG stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. How do you know when a stock will go up or down?
2. Why do we need predictive models?
3. Nash Equilibria

FORG Target Price Prediction Modeling Methodology

We consider ForgeRock Inc. Class A Common Stock Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of FORG 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(Independent T-Test)^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 (Social Media Sentiment Analysis)) X S(n):→ (n+8 weeks) $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of FORG 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?

FORG Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: FORG ForgeRock Inc. Class A Common Stock
Time series to forecast n: 30 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) 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 ForgeRock Inc. Class A Common Stock

1. Interest Rate Benchmark Reform, which amended IFRS 9, IAS 39 and IFRS 7, issued in September 2019, added Section 6.8 and amended paragraph 7.2.26. An entity shall apply these amendments for annual periods beginning on or after 1 January 2020. Earlier application is permitted. If an entity applies these amendments for an earlier period, it shall disclose that fact.
2. A single hedging instrument may be designated as a hedging instrument of more than one type of risk, provided that there is a specific designation of the hedging instrument and of the different risk positions as hedged items. Those hedged items can be in different hedging relationships.
3. An entity shall apply Prepayment Features with Negative Compensation (Amendments to IFRS 9) retrospectively in accordance with IAS 8, except as specified in paragraphs 7.2.30–7.2.34
4. If the underlyings are not the same but are economically related, there can be situations in which the values of the hedging instrument and the hedged item move in the same direction, for example, because the price differential between the two related underlyings changes while the underlyings themselves do not move significantly. That is still consistent with an economic relationship between the hedging instrument and the hedged item if the values of the hedging instrument and the hedged item are still expected to typically move in the opposite direction when the underlyings move.

*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

ForgeRock Inc. Class A Common Stock assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) with Independent T-Test^1,2,3,4 and conclude that the FORG stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

FORG ForgeRock Inc. Class A Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Caa2	C
Balance Sheet	C	B3
Leverage Ratios	Baa2	Caa2
Cash Flow	Caa2	Ba3
Rates of Return and Profitability	Baa2	B1

*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: 77 out of 100 with 614 signals.

References

1. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
2. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
3. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
4. Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
5. P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
6. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
7. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the Fifteenth National Conference on Artificial Intelligence and Tenth Innovative Applications of Artificial Intelligence Conference, AAAI 98, IAAI 98, July 26-30, 1998, Madison, Wisconsin, USA., pages 746–752, 1998.

Frequently Asked Questions

Q: What is the prediction methodology for FORG stock?
A: FORG stock prediction methodology: We evaluate the prediction models Modular Neural Network (Social Media Sentiment Analysis) and Independent T-Test
Q: Is FORG stock a buy or sell?
A: The dominant strategy among neural network is to Hold FORG Stock.
Q: Is ForgeRock Inc. Class A Common Stock stock a good investment?
A: The consensus rating for ForgeRock Inc. Class A Common Stock is Hold and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of FORG stock?
A: The consensus rating for FORG is Hold.
Q: What is the prediction period for FORG stock?
A: The prediction period for FORG is (n+8 weeks)

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