MOLN Molecular Partners AG American Depositary Shares

Outlook: Molecular Partners AG American Depositary Shares assigned short-term B2 & long-term B1 forecasted stock rating.
Dominant Strategy : Buy
Time series to forecast n: 10 Dec 2022 for (n+3 month)
Methodology : Modular Neural Network (Market Direction Analysis)

Abstract

In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements.(Beg, M.O., Awan, M.N. and Ali, S.S., 2019. Algorithmic machine learning for prediction of stock prices. In FinTech as a Disruptive Technology for Financial Institutions (pp. 142-169). IGI Global.) We evaluate Molecular Partners AG American Depositary Shares prediction models with Modular Neural Network (Market Direction Analysis) and Ridge Regression^1,2,3,4 and conclude that the MOLN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Key Points

1. Which neural network is best for prediction?
2. What is prediction model?
3. How useful are statistical predictions?

MOLN Target Price Prediction Modeling Methodology

We consider Molecular Partners AG American Depositary Shares Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of MOLN 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= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ (n+3 month) $\sum _{i=1}^n\left(s_i\right)$

n:Time series to forecast

p:Price signals of MOLN 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?

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

Sample Set: Neural Network
Stock/Index: MOLN Molecular Partners AG American Depositary Shares
Time series to forecast n: 10 Dec 2022 for (n+3 month)

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

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%

Adjusted IFRS* Prediction Methods for Molecular Partners AG American Depositary Shares

1. Changes in market conditions that give rise to market risk include changes in a benchmark interest rate, the price of another entity's financial instrument, a commodity price, a foreign exchange rate or an index of prices or rates.
2. 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.
3. When applying the effective interest method, an entity generally amortises any fees, points paid or received, transaction costs and other premiums or discounts that are included in the calculation of the effective interest rate over the expected life of the financial instrument. However, a shorter period is used if this is the period to which the fees, points paid or received, transaction costs, premiums or discounts relate. This will be the case when the variable to which the fees, points paid or received, transaction costs, premiums or discounts relate is repriced to market rates before the expected maturity of the financial instrument. In such a case, the appropriate amortisation period is the period to the next such repricing date. For example, if a premium or discount on a floating-rate financial instrument reflects the interest that has accrued on that financial instrument since the interest was last paid, or changes in the market rates since the floating interest rate was reset to the market rates, it will be amortised to the next date when the floating interest is reset to market rates. This is because the premium or discount relates to the period to the next interest reset date because, at that date, the variable to which the premium or discount relates (ie interest rates) is reset to the market rates. If, however, the premium or discount results from a change in the credit spread over the floating rate specified in the financial instrument, or other variables that are not reset to the market rates, it is amortised over the expected life of the financial instrument.
4. In cases such as those described in the preceding paragraph, to designate, at initial recognition, the financial assets and financial liabilities not otherwise so measured as at fair value through profit or loss may eliminate or significantly reduce the measurement or recognition inconsistency and produce more relevant information. For practical purposes, the entity need not enter into all of the assets and liabilities giving rise to the measurement or recognition inconsistency at exactly the same time. A reasonable delay is permitted provided that each transaction is designated as at fair value through profit or loss at its initial recognition and, at that time, any remaining transactions are expected to occur.

*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

Molecular Partners AG American Depositary Shares assigned short-term B2 & long-term B1 forecasted stock rating. We evaluate the prediction models Modular Neural Network (Market Direction Analysis) with Ridge Regression^1,2,3,4 and conclude that the MOLN stock is predictable in the short/long term. According to price forecasts for (n+3 month) period, the dominant strategy among neural network is: Buy

Financial State Forecast for MOLN Molecular Partners AG American Depositary Shares Options & Futures

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Operational Risk	72	50
Market Risk	54	44
Technical Analysis	57	81
Fundamental Analysis	39	57
Risk Unsystematic	55	71

Prediction Confidence Score

Trust metric by Neural Network: 74 out of 100 with 638 signals.

References

1. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
2. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
3. 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.
4. 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.
5. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58:267–88
6. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97
7. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.

Frequently Asked Questions

Q: What is the prediction methodology for MOLN stock?
A: MOLN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Ridge Regression
Q: Is MOLN stock a buy or sell?
A: The dominant strategy among neural network is to Buy MOLN Stock.
Q: Is Molecular Partners AG American Depositary Shares stock a good investment?
A: The consensus rating for Molecular Partners AG American Depositary Shares is Buy and assigned short-term B2 & long-term B1 forecasted stock rating.
Q: What is the consensus rating of MOLN stock?
A: The consensus rating for MOLN is Buy.
Q: What is the prediction period for MOLN stock?
A: The prediction period for MOLN is (n+3 month)

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