Should You Buy SLNA Right Now?

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

Outlook: Selina Hospitality PLC Ordinary Shares is assigned short-term B2 & long-term B1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Sell
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (Social Media Sentiment Analysis)
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 Ordinary Shares prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression^1,2,3,4 and it is concluded that the SLNA stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for social media sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of social media sentiment analysis, MNNs can be used to identify the sentiment of social media posts, such as tweets, Facebook posts, and Instagram stories. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.⁵ According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Sell

Key Points

Modular Neural Network (Social Media Sentiment Analysis) for SLNA stock price prediction process.
Multiple Regression
What statistical methods are used to analyze data?
Market Outlook
Buy, Sell and Hold Signals

SLNA Stock Price Forecast

We consider Selina Hospitality PLC Ordinary Shares Decision Process with Modular Neural Network (Social Media Sentiment Analysis) where A is the set of discrete actions of SLNA 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: SLNA Selina Hospitality PLC Ordinary Shares
Time series to forecast: 8 Weeks

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

F(Multiple Regression)^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(Modular Neural Network (Social Media Sentiment Analysis)) X S(n):→ 8 Weeks

\vec{R} = (r_{1}, r_{2}, r_{3})

n:Time series to forecast

p:Price signals of SLNA stock

j:Nash equilibria (Neural Network)

k:Dominated move of SLNA stock holders

a:Best response for SLNA target price

A modular neural network (MNN) is a type of artificial neural network that can be used for social media sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of social media sentiment analysis, MNNs can be used to identify the sentiment of social media posts, such as tweets, Facebook posts, and Instagram stories. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.⁵ 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.^6,7

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SLNA 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 Modular Neural Network (Social Media Sentiment Analysis) based SLNA Stock Prediction Model

For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.
Historical information is an important anchor or base from which to measure expected credit losses. However, an entity shall adjust historical data, such as credit loss experience, on the basis of current observable data to reflect the effects of the current conditions and its forecasts of future conditions that did not affect the period on which the historical data is based, and to remove the effects of the conditions in the historical period that are not relevant to the future contractual cash flows. In some cases, the best reasonable and supportable information could be the unadjusted historical information, depending on the nature of the historical information and when it was calculated, compared to circumstances at the reporting date and the characteristics of the financial instrument being considered. Estimates of changes in expected credit losses should reflect, and be directionally consistent with, changes in related observable data from period to period
If such a mismatch would be created or enlarged, the entity is required to present all changes in fair value (including the effects of changes in the credit risk of the liability) in profit or loss. If such a mismatch would not be created or enlarged, the entity is required to present the effects of changes in the liability's credit risk in other comprehensive income.
Although the objective of an entity's business model may be to hold financial assets in order to collect contractual cash flows, the entity need not hold all of those instruments until maturity. Thus an entity's business model can be to hold financial assets to collect contractual cash flows even when sales of financial assets occur or are expected to occur in the future.

*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.

SLNA Selina Hospitality PLC Ordinary Shares Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	B1
Income Statement	B3	B1
Balance Sheet	B2	Baa2
Leverage Ratios	Baa2	C
Cash Flow	Caa2	C
Rates of Return and Profitability	Caa2	Baa2

*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

Hirano K, Porter JR. 2009. Asymptotics for statistical treatment rules. Econometrica 77:1683–701
Breusch, T. S. A. R. Pagan (1979), "A simple test for heteroskedasticity and random coefficient variation," Econometrica, 47, 1287–1294.
Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
Armstrong, J. S. M. C. Grohman (1972), "A comparative study of methods for long-range market forecasting," Management Science, 19, 211–221.
Alpaydin E. 2009. Introduction to Machine Learning. Cambridge, MA: MIT Press
Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.

Frequently Asked Questions

Q: Is SLNA stock expected to rise?
A: SLNA stock prediction model is evaluated with Modular Neural Network (Social Media Sentiment Analysis) and Multiple Regression and it is concluded that dominant strategy for SLNA stock is Sell
Q: Is SLNA stock a buy or sell?
A: The dominant strategy among neural network is to Sell SLNA Stock.
Q: Is Selina Hospitality PLC Ordinary Shares stock a good investment?
A: The consensus rating for Selina Hospitality PLC Ordinary Shares is Sell and is assigned short-term B2 & long-term B1 estimated rating.
Q: What is the consensus rating of SLNA stock?
A: The consensus rating for SLNA is Sell.
Q: What is the forecast for SLNA stock?
A: SLNA target price forecast: Sell

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