VECT Target Price Prediction

VectivBio Holding AG Ordinary Shares Research Report

Summary

Accurate prediction of stock market returns is a very challenging task due to volatile and non-linear nature of the financial stock markets. With the introduction of artificial intelligence and increased computational capabilities, programmed methods of prediction have proved to be more efficient in predicting stock prices. We evaluate VectivBio Holding AG Ordinary Shares prediction models with Modular Neural Network (News Feed Sentiment Analysis) and Factor1,2,3,4 and conclude that the VECT stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold VECT stock.

Key Points

  1. What is Markov decision process in reinforcement learning?
  2. Can machine learning predict?
  3. Operational Risk

VECT Target Price Prediction Modeling Methodology

We consider VectivBio Holding AG Ordinary Shares Stock Decision Process with Modular Neural Network (News Feed Sentiment Analysis) where A is the set of discrete actions of VECT 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(Factor)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(Modular Neural Network (News Feed Sentiment Analysis)) X S(n):→ (n+16 weeks) i = 1 n r i

n:Time series to forecast

p:Price signals of VECT stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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VECT Stock Forecast (Buy or Sell) for (n+16 weeks)

Sample Set: Neural Network
Stock/Index: VECT VectivBio Holding AG Ordinary Shares
Time series to forecast n: 23 Nov 2022 for (n+16 weeks)

According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold VECT 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 VectivBio Holding AG Ordinary Shares

  1. When an entity, consistent with its hedge documentation, frequently resets (ie discontinues and restarts) a hedging relationship because both the hedging instrument and the hedged item frequently change (ie the entity uses a dynamic process in which both the hedged items and the hedging instruments used to manage that exposure do not remain the same for long), the entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component is separately identifiable—only when it initially designates a hedged item in that hedging relationship. A hedged item that has been assessed at the time of its initial designation in the hedging relationship, whether it was at the time of the hedge inception or subsequently, is not reassessed at any subsequent redesignation in the same hedging relationship.
  2. If a financial instrument is designated in accordance with paragraph 6.7.1 as measured at fair value through profit or loss after its initial recognition, or was previously not recognised, the difference at the time of designation between the carrying amount, if any, and the fair value shall immediately be recognised in profit or loss. For financial assets measured at fair value through other comprehensive income in accordance with paragraph 4.1.2A, the cumulative gain or loss previously recognised in other comprehensive income shall immediately be reclassified from equity to profit or loss as a reclassification adjustment.
  3. The significance of a change in the credit risk since initial recognition depends on the risk of a default occurring as at initial recognition. Thus, a given change, in absolute terms, in the risk of a default occurring will be more significant for a financial instrument with a lower initial risk of a default occurring compared to a financial instrument with a higher initial risk of a default occurring.
  4. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.

*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

VectivBio Holding AG Ordinary Shares assigned short-term B3 & long-term B3 forecasted stock rating. We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) with Factor1,2,3,4 and conclude that the VECT stock is predictable in the short/long term. According to price forecasts for (n+16 weeks) period: The dominant strategy among neural network is to Hold VECT stock.

Financial State Forecast for VECT VectivBio Holding AG Ordinary Shares Stock Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3B3
Operational Risk 4032
Market Risk7037
Technical Analysis5932
Fundamental Analysis3878
Risk Unsystematic4350

Prediction Confidence Score

Trust metric by Neural Network: 92 out of 100 with 711 signals.

References

  1. 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
  2. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
  3. Mazumder R, Hastie T, Tibshirani R. 2010. Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11:2287–322
  4. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78
  5. V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 1928–1937, 2016
  6. Nie X, Wager S. 2019. Quasi-oracle estimation of heterogeneous treatment effects. arXiv:1712.04912 [stat.ML]
  7. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
Frequently Asked QuestionsQ: What is the prediction methodology for VECT stock?
A: VECT stock prediction methodology: We evaluate the prediction models Modular Neural Network (News Feed Sentiment Analysis) and Factor
Q: Is VECT stock a buy or sell?
A: The dominant strategy among neural network is to Hold VECT Stock.
Q: Is VectivBio Holding AG Ordinary Shares stock a good investment?
A: The consensus rating for VectivBio Holding AG Ordinary Shares is Hold and assigned short-term B3 & long-term B3 forecasted stock rating.
Q: What is the consensus rating of VECT stock?
A: The consensus rating for VECT is Hold.
Q: What is the prediction period for VECT stock?
A: The prediction period for VECT is (n+16 weeks)

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