Outlook: Zynerba Pharmaceuticals Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 27 Feb 2023 for (n+1 year)
Methodology : Supervised Machine Learning (ML)

Abstract

Zynerba Pharmaceuticals Inc. Common Stock prediction model is evaluated with Supervised Machine Learning (ML) and Linear Regression1,2,3,4 and it is concluded that the ZYNE stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

Key Points

1. What statistical methods are used to analyze data?
2. What is a prediction confidence?
3. Decision Making

ZYNE Target Price Prediction Modeling Methodology

We consider Zynerba Pharmaceuticals Inc. Common Stock Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of ZYNE 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(Linear Regression)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(Supervised Machine Learning (ML)) X S(n):→ (n+1 year) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of ZYNE 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?

ZYNE Stock Forecast (Buy or Sell) for (n+1 year)

Sample Set: Neural Network
Stock/Index: ZYNE Zynerba Pharmaceuticals Inc. Common Stock
Time series to forecast n: 27 Feb 2023 for (n+1 year)

According to price forecasts for (n+1 year) 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 (Grey to Black): *Technical Analysis%

IFRS Reconciliation Adjustments for Zynerba Pharmaceuticals Inc. Common Stock

1. Unless paragraph 6.8.8 applies, for a hedge of a non-contractually specified benchmark component of interest rate risk, an entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component shall be separately identifiable—only at the inception of the hedging relationship.
2. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
3. For example, when the critical terms (such as the nominal amount, maturity and underlying) of the hedging instrument and the hedged item match or are closely aligned, it might be possible for an entity to conclude on the basis of a qualitative assessment of those critical terms that the hedging instrument and the hedged item have values that will generally move in the opposite direction because of the same risk and hence that an economic relationship exists between the hedged item and the hedging instrument (see paragraphs B6.4.4–B6.4.6).
4. Conversely, if changes in the extent of offset indicate that the fluctuation is around a hedge ratio that is different from the hedge ratio that is currently used for that hedging relationship, or that there is a trend leading away from that hedge ratio, hedge ineffectiveness can be reduced by adjusting the hedge ratio, whereas retaining the hedge ratio would increasingly produce hedge ineffectiveness. Hence, in such circumstances, an entity must evaluate whether the hedging relationship reflects an imbalance between the weightings of the hedged item and the hedging instrument that would create hedge ineffectiveness (irrespective of whether recognised or not) that could result in an accounting outcome that would be inconsistent with the purpose of hedge accounting. If the hedge ratio is adjusted, it also affects the measurement and recognition of hedge ineffectiveness because, on rebalancing, the hedge ineffectiveness of the hedging relationship must be determined and recognised immediately before adjusting the hedging relationship in accordance with paragraph B6.5.8.

*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.

Conclusions

Zynerba Pharmaceuticals Inc. Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Zynerba Pharmaceuticals Inc. Common Stock prediction model is evaluated with Supervised Machine Learning (ML) and Linear Regression1,2,3,4 and it is concluded that the ZYNE stock is predictable in the short/long term. According to price forecasts for (n+1 year) period, the dominant strategy among neural network is: Sell

ZYNE Zynerba Pharmaceuticals Inc. Common Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba1Ba1
Income StatementCaa2B1
Balance SheetB3Caa2
Leverage RatiosCaa2C
Cash FlowCaa2B1
Rates of Return and ProfitabilityBaa2B2

*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?

Prediction Confidence Score

Trust metric by Neural Network: 89 out of 100 with 754 signals.

References

1. Hartigan JA, Wong MA. 1979. Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C 28:100–8
2. G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
3. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
4. V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
5. Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
6. Clements, M. P. D. F. Hendry (1996), "Intercept corrections and structural change," Journal of Applied Econometrics, 11, 475–494.
7. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
Frequently Asked QuestionsQ: What is the prediction methodology for ZYNE stock?
A: ZYNE stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Linear Regression
Q: Is ZYNE stock a buy or sell?
A: The dominant strategy among neural network is to Sell ZYNE Stock.
Q: Is Zynerba Pharmaceuticals Inc. Common Stock stock a good investment?
A: The consensus rating for Zynerba Pharmaceuticals Inc. Common Stock is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ZYNE stock?
A: The consensus rating for ZYNE is Sell.
Q: What is the prediction period for ZYNE stock?
A: The prediction period for ZYNE is (n+1 year)