GNACW Target Price Prediction

Group Nine Acquisition Corp. Warrant Research Report

Summary

Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. We evaluate Group Nine Acquisition Corp. Warrant prediction models with Active Learning (ML) and Ridge Regression1,2,3,4 and conclude that the GNACW stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy GNACW stock.

Key Points

  1. What are main components of Markov decision process?
  2. Stock Rating
  3. Game Theory

GNACW Target Price Prediction Modeling Methodology

We consider Group Nine Acquisition Corp. Warrant Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of GNACW 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(Ridge Regression)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(Active Learning (ML)) X S(n):→ (n+1 year) R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of GNACW 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?

GNACW Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: GNACW Group Nine Acquisition Corp. Warrant
Time series to forecast n: 22 Nov 2022 for (n+1 year)

According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy GNACW 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 Group Nine Acquisition Corp. Warrant

  1. A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
  2. The fact that a derivative is in or out of the money when it is designated as a hedging instrument does not in itself mean that a qualitative assessment is inappropriate. It depends on the circumstances whether hedge ineffectiveness arising from that fact could have a magnitude that a qualitative assessment would not adequately capture.
  3. A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
  4. Adjusting the hedge ratio by increasing the volume of the hedging instrument does not affect how the changes in the value of the hedged item are measured. The measurement of the changes in the fair value of the hedging instrument related to the previously designated volume also remains unaffected. However, from the date of rebalancing, the changes in the fair value of the hedging instrument also include the changes in the value of the additional volume of the hedging instrument. The changes are measured starting from, and by reference to, the date of rebalancing instead of the date on which the hedging relationship was designated. For example, if an entity originally hedged the price risk of a commodity using a derivative volume of 100 tonnes as the hedging instrument and added a volume of 10 tonnes on rebalancing, the hedging instrument after rebalancing would comprise a total derivative volume of 110 tonnes. The change in the fair value of the hedging instrument is the total change in the fair value of the derivatives that make up the total volume of 110 tonnes. These derivatives could (and probably would) have different critical terms, such as their forward rates, because they were entered into at different points in time (including the possibility of designating derivatives into hedging relationships after their initial recognition).

*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

Group Nine Acquisition Corp. Warrant assigned short-term B1 & long-term Ba3 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Ridge Regression1,2,3,4 and conclude that the GNACW stock is predictable in the short/long term. According to price forecasts for (n+1 year) period: The dominant strategy among neural network is to Buy GNACW stock.

Financial State Forecast for GNACW Group Nine Acquisition Corp. Warrant Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B1Ba3
Operational Risk 7935
Market Risk7767
Technical Analysis6569
Fundamental Analysis3088
Risk Unsystematic6254

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 785 signals.

References

  1. J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
  2. L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
  3. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
  4. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
  5. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
  6. Li L, Chen S, Kleban J, Gupta A. 2014. Counterfactual estimation and optimization of click metrics for search engines: a case study. In Proceedings of the 24th International Conference on the World Wide Web, pp. 929–34. New York: ACM
  7. A. Y. Ng, D. Harada, and S. J. Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 278–287, 1999.
Frequently Asked QuestionsQ: What is the prediction methodology for GNACW stock?
A: GNACW stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Ridge Regression
Q: Is GNACW stock a buy or sell?
A: The dominant strategy among neural network is to Buy GNACW Stock.
Q: Is Group Nine Acquisition Corp. Warrant stock a good investment?
A: The consensus rating for Group Nine Acquisition Corp. Warrant is Buy and assigned short-term B1 & long-term Ba3 forecasted stock rating.
Q: What is the consensus rating of GNACW stock?
A: The consensus rating for GNACW is Buy.
Q: What is the prediction period for GNACW stock?
A: The prediction period for GNACW is (n+1 year)

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