SLNAW Stock: A Risky Investment

Outlook: Selina Hospitality PLC Warrant is assigned short-term B3 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : SellSpeculative Trend
Time series to forecast n: for Weeks²
Methodology : Ensemble Learning (ML)
Hypothesis Testing : Multiple Regression
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.

Summary

Selina Hospitality PLC Warrant prediction model is evaluated with Ensemble Learning (ML) and Multiple Regression^1,2,3,4 and it is concluded that the SLNAW stock is predictable in the short/long term. Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average. According to price forecasts for 1 Year period, the dominant strategy among neural network is: SellSpeculative Trend

Key Points

1. What is the best way to predict stock prices?
2. Can statistics predict the future?
3. Reaction Function

SLNAW Target Price Prediction Modeling Methodology

We consider Selina Hospitality PLC Warrant Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of SLNAW 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(Multiple 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(Ensemble Learning (ML)) X S(n):→ 1 Year $R=\left(\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ 0& 0& 1\end{array}\right)$

n:Time series to forecast

p:Price signals of SLNAW stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Ensemble Learning (ML)

Ensemble learning is a machine learning (ML) technique that combines multiple models to create a single model that is more accurate than any of the individual models. This is done by combining the predictions of the individual models, typically using a voting scheme or a weighted average.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.

 

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?

SLNAW Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: SLNAW Selina Hospitality PLC Warrant
Time series to forecast: 1 Year

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

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 Ensemble Learning (ML) based SLNAW Stock Prediction Model

1. When using historical credit loss experience in estimating expected credit losses, it is important that information about historical credit loss rates is applied to groups that are defined in a manner that is consistent with the groups for which the historical credit loss rates were observed. Consequently, the method used shall enable each group of financial assets to be associated with information about past credit loss experience in groups of financial assets with similar risk characteristics and with relevant observable data that reflects current conditions.
2. When defining default for the purposes of determining the risk of a default occurring, an entity shall apply a default definition that is consistent with the definition used for internal credit risk management purposes for the relevant financial instrument and consider qualitative indicators (for example, financial covenants) when appropriate. However, there is a rebuttable presumption that default does not occur later than when a financial asset is 90 days past due unless an entity has reasonable and supportable information to demonstrate that a more lagging default criterion is more appropriate. The definition of default used for these purposes shall be applied consistently to all financial instruments unless information becomes available that demonstrates that another default definition is more appropriate for a particular financial instrument.
3. The methods used to determine whether credit risk has increased significantly on a financial instrument since initial recognition should consider the characteristics of the financial instrument (or group of financial instruments) and the default patterns in the past for comparable financial instruments. Despite the requirement in paragraph 5.5.9, for financial instruments for which default patterns are not concentrated at a specific point during the expected life of the financial instrument, changes in the risk of a default occurring over the next 12 months may be a reasonable approximation of the changes in the lifetime risk of a default occurring. In such cases, an entity may use changes in the risk of a default occurring over the next 12 months to determine whether credit risk has increased significantly since initial recognition, unless circumstances indicate that a lifetime assessment is necessary
4. Accordingly the date of the modification shall be treated as the date of initial recognition of that financial asset when applying the impairment requirements to the modified financial asset. This typically means measuring the loss allowance at an amount equal to 12-month expected credit losses until the requirements for the recognition of lifetime expected credit losses in paragraph 5.5.3 are met. However, in some unusual circumstances following a modification that results in derecognition of the original financial asset, there may be evidence that the modified financial asset is credit-impaired at initial recognition, and thus, the financial asset should be recognised as an originated credit-impaired financial asset. This might occur, for example, in a situation in which there was a substantial modification of a distressed asset that resulted in the derecognition of the original financial asset. In such a case, it may be possible for the modification to result in a new financial asset which is credit-impaired at initial recognition.

*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.

SLNAW Selina Hospitality PLC Warrant Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B3	B1
Income Statement	Caa2	Baa2
Balance Sheet	C	Ba1
Leverage Ratios	Baa2	B3
Cash Flow	B3	B3
Rates of Return and Profitability	C	C

*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?

Conclusions

Selina Hospitality PLC Warrant is assigned short-term B3 & long-term B1 estimated rating. Selina Hospitality PLC Warrant prediction model is evaluated with Ensemble Learning (ML) and Multiple Regression^1,2,3,4 and it is concluded that the SLNAW stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: SellSpeculative Trend

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 491 signals.

References

1. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
2. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32
3. Athey S, Imbens G, Wager S. 2016a. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv:1604.07125 [math.ST]
4. M. Sobel. The variance of discounted Markov decision processes. Applied Probability, pages 794–802, 1982
5. Wan M, Wang D, Goldman M, Taddy M, Rao J, et al. 2017. Modeling consumer preferences and price sensitiv- ities from large-scale grocery shopping transaction logs. In Proceedings of the 26th International Conference on the World Wide Web, pp. 1103–12. New York: ACM
6. Harris ZS. 1954. Distributional structure. Word 10:146–62
7. Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell

Frequently Asked Questions

Q: What is the prediction methodology for SLNAW stock?
A: SLNAW stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Multiple Regression
Q: Is SLNAW stock a buy or sell?
A: The dominant strategy among neural network is to SellSpeculative Trend SLNAW Stock.
Q: Is Selina Hospitality PLC Warrant stock a good investment?
A: The consensus rating for Selina Hospitality PLC Warrant is SellSpeculative Trend and is assigned short-term B3 & long-term B1 estimated rating.
Q: What is the consensus rating of SLNAW stock?
A: The consensus rating for SLNAW is SellSpeculative Trend.
Q: What is the prediction period for SLNAW stock?
A: The prediction period for SLNAW is 1 Year

People also ask

⚐ What are the top stocks to invest in right now?
☵ What happens to stocks when they're delisted?