Outlook: Biophytis SA American Depositary Share assigned short-term B3 & long-term Ba1 forecasted stock rating.
Dominant Strategy : Sell
Time series to forecast n: 07 Dec 2022 for (n+8 weeks)
Methodology : Active Learning (ML)

## Abstract

Prediction of the Stock Market is a challenging task in predicting the stock prices in the future. Due to the fluctuating nature of the stock, the stock market is too difficult to predict. Stock prices are constantly changing every day. Estimating of the stock market has a high demand for stock customers. Applying all extracted rules at any time is a major challenge to estimate the future stock price with high accuracy. The latest prediction techniques adopted for the stock market such as Artificial Neural Network, Neuro-Fuzzy System, Time Series Linear Models (TSLM), Recurrent Neural Network (RNN).(Güreşen, E. and Kayakutlu, G., 2008, October. Forecasting stock exchange movements using artificial neural network models and hybrid models. In International Conference on Intelligent Information Processing (pp. 129-137). Springer, Boston, MA.) We evaluate Biophytis SA American Depositary Share prediction models with Active Learning (ML) and Beta1,2,3,4 and conclude that the BPTS stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell BPTS stock.

## Key Points

1. What is Markov decision process in reinforcement learning?
2. How accurate is machine learning in stock market?
3. Reaction Function

## BPTS Target Price Prediction Modeling Methodology

We consider Biophytis SA American Depositary Share Decision Process with Active Learning (ML) where A is the set of discrete actions of BPTS 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(Beta)5,6,7= $\begin{array}{cccc}{p}_{a1}& {p}_{a2}& \dots & {p}_{1n}\\ & ⋮\\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & ⋮\\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & ⋮\\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Active Learning (ML)) X S(n):→ (n+8 weeks) $∑ i = 1 n s i$

n:Time series to forecast

p:Price signals of BPTS 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?

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

Sample Set: Neural Network
Stock/Index: BPTS Biophytis SA American Depositary Share
Time series to forecast n: 07 Dec 2022 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell BPTS 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 Biophytis SA American Depositary Share

1. For the purpose of applying the requirements in paragraphs 6.4.1(c)(i) and B6.4.4–B6.4.6, an entity shall assume that the interest rate benchmark on which the hedged cash flows and/or the hedged risk (contractually or noncontractually specified) are based, or the interest rate benchmark on which the cash flows of the hedging instrument are based, is not altered as a result of interest rate benchmark reform.
2. Hedging relationships that qualified for hedge accounting in accordance with IAS 39 that also qualify for hedge accounting in accordance with the criteria of this Standard (see paragraph 6.4.1), after taking into account any rebalancing of the hedging relationship on transition (see paragraph 7.2.25(b)), shall be regarded as continuing hedging relationships.
3. Paragraphs 6.9.7–6.9.13 provide exceptions to the requirements specified in those paragraphs only. An entity shall apply all other hedge accounting requirements in this Standard, including the qualifying criteria in paragraph 6.4.1, to hedging relationships that were directly affected by interest rate benchmark reform.
4. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.

*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

Biophytis SA American Depositary Share assigned short-term B3 & long-term Ba1 forecasted stock rating. We evaluate the prediction models Active Learning (ML) with Beta1,2,3,4 and conclude that the BPTS stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period: The dominant strategy among neural network is to Sell BPTS stock.

### Financial State Forecast for BPTS Biophytis SA American Depositary Share Options & Futures

Rating Short-Term Long-Term Senior
Outlook*B3Ba1
Operational Risk 3649
Market Risk4771
Technical Analysis6680
Fundamental Analysis6082
Risk Unsystematic3365

### Prediction Confidence Score

Trust metric by Neural Network: 75 out of 100 with 726 signals.

## References

1. J. Hu and M. P. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4:1039–1069, 2003.
2. H. Kushner and G. Yin. Stochastic approximation algorithms and applications. Springer, 1997.
3. N. B ̈auerle and A. Mundt. Dynamic mean-risk optimization in a binomial model. Mathematical Methods of Operations Research, 70(2):219–239, 2009.
4. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
5. N. B ̈auerle and J. Ott. Markov decision processes with average-value-at-risk criteria. Mathematical Methods of Operations Research, 74(3):361–379, 2011
6. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
7. Bera, A. M. L. Higgins (1997), "ARCH and bilinearity as competing models for nonlinear dependence," Journal of Business Economic Statistics, 15, 43–50.
Frequently Asked QuestionsQ: What is the prediction methodology for BPTS stock?
A: BPTS stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Beta
Q: Is BPTS stock a buy or sell?
A: The dominant strategy among neural network is to Sell BPTS Stock.
Q: Is Biophytis SA American Depositary Share stock a good investment?
A: The consensus rating for Biophytis SA American Depositary Share is Sell and assigned short-term B3 & long-term Ba1 forecasted stock rating.
Q: What is the consensus rating of BPTS stock?
A: The consensus rating for BPTS is Sell.
Q: What is the prediction period for BPTS stock?
A: The prediction period for BPTS is (n+8 weeks)