GLPI Stock: A Strong Financial Position and Bright Future

Outlook: Gaming and Leisure Properties Inc. Common Stock is assigned short-term Caa2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Modular Neural Network (Market Direction Analysis)
Hypothesis Testing : Logistic 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

Gaming and Leisure Properties Inc. Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Logistic Regression^1,2,3,4 and it is concluded that the GLPI stock is predictable in the short/long term. Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. Is Target price a good indicator?
2. What are the most successful trading algorithms?
3. Probability Distribution

GLPI Target Price Prediction Modeling Methodology

We consider Gaming and Leisure Properties Inc. Common Stock Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of GLPI 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(Logistic 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(Modular Neural Network (Market Direction Analysis)) X S(n):→ 4 Weeks $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of GLPI stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Modular Neural Network (Market Direction Analysis)

Modular neural networks (MNNs) are a type of artificial neural network that can be used for market direction analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying patterns in data or predicting future price movements. The modules are then combined to form a single neural network that can perform multiple tasks.In the context of market direction analysis, MNNs can be used to identify patterns in market data that suggest that the market is likely to move in a particular direction. This information can then be used to make predictions about future price movements.

Logistic Regression

In statistics, logistic regression is a type of regression analysis used when the dependent variable is categorical. Logistic regression is a probability model that predicts the probability of an event occurring based on a set of independent variables. In logistic regression, the dependent variable is represented as a binary variable, such as "yes" or "no," "true" or "false," or "sick" or "healthy." The independent variables can be continuous or categorical variables.

 

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?

GLPI Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: GLPI Gaming and Leisure Properties Inc. Common Stock
Time series to forecast: 4 Weeks

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

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 (Market Direction Analysis) based GLPI Stock Prediction Model

1. An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods if, and only if, it is possible without the use of hindsight and the restated financial statements reflect all the requirements in this Standard. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
2. A regular way purchase or sale gives rise to a fixed price commitment between trade date and settlement date that meets the definition of a derivative. However, because of the short duration of the commitment it is not recognised as a derivative financial instrument. Instead, this Standard provides for special accounting for such regular way contracts (see paragraphs 3.1.2 and B3.1.3–B3.1.6).
3. An equity method investment cannot be a hedged item in a fair value hedge. This is because the equity method recognises in profit or loss the investor's share of the investee's profit or loss, instead of changes in the investment's fair value. For a similar reason, an investment in a consolidated subsidiary cannot be a hedged item in a fair value hedge. This is because consolidation recognises in profit or loss the subsidiary's profit or loss, instead of changes in the investment's fair value. A hedge of a net investment in a foreign operation is different because it is a hedge of the foreign currency exposure, not a fair value hedge of the change in the value of the investment.
4. Paragraph 4.1.1(a) requires an entity to classify financial assets on the basis of the entity's business model for managing the financial assets, unless paragraph 4.1.5 applies. An entity assesses whether its financial assets meet the condition in paragraph 4.1.2(a) or the condition in paragraph 4.1.2A(a) on the basis of the business model as determined by the entity's key management personnel (as defined in IAS 24 Related Party Disclosures).

*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.

GLPI Gaming and Leisure Properties Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Caa2	Ba3
Income Statement	Caa2	C
Balance Sheet	Caa2	Ba2
Leverage Ratios	Caa2	Baa2
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	C	Caa2

*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

Gaming and Leisure Properties Inc. Common Stock is assigned short-term Caa2 & long-term Ba3 estimated rating. Gaming and Leisure Properties Inc. Common Stock prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Logistic Regression^1,2,3,4 and it is concluded that the GLPI stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 87 out of 100 with 545 signals.

References

1. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning. Berlin: Springer
2. Bamler R, Mandt S. 2017. Dynamic word embeddings via skip-gram filtering. In Proceedings of the 34th Inter- national Conference on Machine Learning, pp. 380–89. La Jolla, CA: Int. Mach. Learn. Soc.
3. Bewley, R. M. Yang (1998), "On the size and power of system tests for cointegration," Review of Economics and Statistics, 80, 675–679.
4. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014
5. Mullainathan S, Spiess J. 2017. Machine learning: an applied econometric approach. J. Econ. Perspect. 31:87–106
6. A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008
7. Harris ZS. 1954. Distributional structure. Word 10:146–62

Frequently Asked Questions

Q: What is the prediction methodology for GLPI stock?
A: GLPI stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Logistic Regression
Q: Is GLPI stock a buy or sell?
A: The dominant strategy among neural network is to Buy GLPI Stock.
Q: Is Gaming and Leisure Properties Inc. Common Stock stock a good investment?
A: The consensus rating for Gaming and Leisure Properties Inc. Common Stock is Buy and is assigned short-term Caa2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of GLPI stock?
A: The consensus rating for GLPI is Buy.
Q: What is the prediction period for GLPI stock?
A: The prediction period for GLPI is 4 Weeks

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