OPTN Stock: A Spark That Could Fizzle

Jorgenson, Dale W.; Weitzman, Martin L.; ZXhang, Yoshua X; Haxo, Yann M; Mat , Ying X

Outlook: OptiNose Inc. Common Stock is assigned short-term B3 & long-term B3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Hold
Time series to forecast n: for Weeks²
Methodology : Active Learning (ML)
Hypothesis Testing : Wilcoxon Rank-Sum Test
Surveillance : Major exchange and OTC

¹The accuracy of the model is being monitored on a regular basis.(15-minute period)

²Time series is updated based on short-term trends.

OptiNose Inc. Common Stock prediction model is evaluated with Active Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the OPTN stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.⁵ According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold

Key Points

Game Theory
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OPTN Stock Price Forecast

We consider OptiNose Inc. Common Stock Decision Process with Active Learning (ML) where A is the set of discrete actions of OPTN 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

Sample Set: Neural Network
Stock/Index: OPTN OptiNose Inc. Common Stock
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: Hold

F(Wilcoxon Rank-Sum Test)^6,7=

\begin{matrix} p_{a 1} & p_{a 2} & \dots & p_{1 n} \\ ⋮ \\ p_{j 1} & p_{j 2} & \dots & p_{j n} \\ ⋮ \\ p_{k 1} & p_{k 2} & \dots & p_{k n} \\ ⋮ \\ p_{n 1} & p_{n 2} & \dots & p_{n n} \end{matrix}

X R(Active Learning (ML)) X S(n):→ 8 Weeks

\begin{array}{l} \int e^{x} rx \end{array}

n:Time series to forecast

p:Price signals of OPTN stock

j:Nash equilibria (Neural Network)

k:Dominated move of OPTN stock holders

a:Best response for OPTN target price

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.⁵ The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.^6,7

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?

OPTN Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

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%

Financial Data Adjustments for Active Learning (ML) based OPTN Stock Prediction Model

For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
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.
The expected credit losses on a loan commitment shall be discounted using the effective interest rate, or an approximation thereof, that will be applied when recognising the financial asset resulting from the loan commitment. This is because for the purpose of applying the impairment requirements, a financial asset that is recognised following a draw down on a loan commitment shall be treated as a continuation of that commitment instead of as a new financial instrument. The expected credit losses on the financial asset shall therefore be measured considering the initial credit risk of the loan commitment from the date that the entity became a party to the irrevocable commitment.
When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

OPTN OptiNose Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B3
Income Statement	Caa2	Ba3
Balance Sheet	Baa2	Caa2
Leverage Ratios	Ba3	C
Cash Flow	C	Caa2
Rates of Return and Profitability	Caa2	B3

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

References

Allen, P. G. (1994), "Economic forecasting in agriculture," International Journal of Forecasting, 10, 81–135.
Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
Bickel P, Klaassen C, Ritov Y, Wellner J. 1998. Efficient and Adaptive Estimation for Semiparametric Models. Berlin: Springer
Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
P. Marbach. Simulated-Based Methods for Markov Decision Processes. PhD thesis, Massachusetts Institute of Technology, 1998
Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60

Frequently Asked Questions

Q: What is the prediction methodology for OPTN stock?
A: OPTN stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Wilcoxon Rank-Sum Test
Q: Is OPTN stock a buy or sell?
A: The dominant strategy among neural network is to Hold OPTN Stock.
Q: Is OptiNose Inc. Common Stock stock a good investment?
A: The consensus rating for OptiNose Inc. Common Stock is Hold and is assigned short-term B3 & long-term B3 estimated rating.
Q: What is the consensus rating of OPTN stock?
A: The consensus rating for OPTN is Hold.
Q: What is the prediction period for OPTN stock?
A: The prediction period for OPTN is 8 Weeks

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