**Outlook:**Clene Inc. Common Stock assigned short-term Ba3 & long-term B2 forecasted stock rating.

**Dominant Strategy :**Sell

**Time series to forecast n: 08 Dec 2022**for (n+6 month)

**Methodology :**Modular Neural Network (Financial Sentiment Analysis)

## Abstract

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making.(Jordan, M.I. and Mitchell, T.M., 2015. Machine learning: Trends, perspectives, and prospects. Science, 349(6245), pp.255-260.)** We evaluate Clene Inc. Common Stock prediction models with Modular Neural Network (Financial Sentiment Analysis) and Factor ^{1,2,3,4} and conclude that the CLNN stock is predictable in the short/long term. **

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

## Key Points

- Stock Rating
- Operational Risk
- Is it better to buy and sell or hold?

## CLNN Target Price Prediction Modeling Methodology

We consider Clene Inc. Common Stock Decision Process with Modular Neural Network (Financial Sentiment Analysis) where A is the set of discrete actions of CLNN 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}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (Financial Sentiment Analysis)) X S(n):→ (n+6 month) $\overrightarrow{S}=\left({s}_{1},{s}_{2},{s}_{3}\right)$

n:Time series to forecast

p:Price signals of CLNN 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?

## CLNN Stock Forecast (Buy or Sell) for (n+6 month)

**Sample Set:**Neural Network

**Stock/Index:**CLNN Clene Inc. Common Stock

**Time series to forecast n: 08 Dec 2022**for (n+6 month)

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

**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 Clene Inc. Common Stock

- The underlying pool must contain one or more instruments that have contractual cash flows that are solely payments of principal and interest on the principal amount outstanding
- If, at the date of initial application, determining whether there has been a significant increase in credit risk since initial recognition would require undue cost or effort, an entity shall recognise a loss allowance at an amount equal to lifetime expected credit losses at each reporting date until that financial instrument is derecognised (unless that financial instrument is low credit risk at a reporting date, in which case paragraph 7.2.19(a) applies).
- An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
- An embedded prepayment option in an interest-only or principal-only strip is closely related to the host contract provided the host contract (i) initially resulted from separating the right to receive contractual cash flows of a financial instrument that, in and of itself, did not contain an embedded derivative, and (ii) does not contain any terms not present in the original host debt contract.

*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

Clene Inc. Common Stock assigned short-term Ba3 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) with Factor ^{1,2,3,4} and conclude that the CLNN stock is predictable in the short/long term.**

**According to price forecasts for (n+6 month) period, the dominant strategy among neural network is: Sell**

### Financial State Forecast for CLNN Clene Inc. Common Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | Ba3 | B2 |

Operational Risk | 69 | 41 |

Market Risk | 45 | 46 |

Technical Analysis | 49 | 44 |

Fundamental Analysis | 89 | 68 |

Risk Unsystematic | 66 | 62 |

### Prediction Confidence Score

## References

- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
- M. Benaim, J. Hofbauer, and S. Sorin. Stochastic approximations and differential inclusions, Part II: Appli- cations. Mathematics of Operations Research, 31(4):673–695, 2006
- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
- Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
- Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
- A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
- J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017

## Frequently Asked Questions

Q: What is the prediction methodology for CLNN stock?A: CLNN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Financial Sentiment Analysis) and Factor

Q: Is CLNN stock a buy or sell?

A: The dominant strategy among neural network is to Sell CLNN Stock.

Q: Is Clene Inc. Common Stock stock a good investment?

A: The consensus rating for Clene Inc. Common Stock is Sell and assigned short-term Ba3 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of CLNN stock?

A: The consensus rating for CLNN is Sell.

Q: What is the prediction period for CLNN stock?

A: The prediction period for CLNN is (n+6 month)

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