Dominant Strategy : Sell
Time series to forecast n: 23 Jun 2023 for 16 Weeks
Methodology : Modular Neural Network (Market Direction Analysis)
Summary
MERCHANTS TRUST PLC prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Logistic Regression1,2,3,4 and it is concluded that the LON:72HN 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 16 Weeks period, the dominant strategy among neural network is: Sell
Key Points
- What statistical methods are used to analyze data?
- What is neural prediction?
- What are main components of Markov decision process?
LON:72HN Target Price Prediction Modeling Methodology
We consider MERCHANTS TRUST PLC Decision Process with Modular Neural Network (Market Direction Analysis) where A is the set of discrete actions of LON:72HN 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= X R(Modular Neural Network (Market Direction Analysis)) X S(n):→ 16 Weeks
n:Time series to forecast
p:Price signals of LON:72HN 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?
LON:72HN Stock Forecast (Buy or Sell) for 16 Weeks
Sample Set: Neural NetworkStock/Index: LON:72HN MERCHANTS TRUST PLC
Time series to forecast n: 23 Jun 2023 for 16 Weeks
According to price forecasts for 16 Weeks 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 MERCHANTS TRUST PLC
- In some circumstances an entity does not have reasonable and supportable information that is available without undue cost or effort to measure lifetime expected credit losses on an individual instrument basis. In that case, lifetime expected credit losses shall be recognised on a collective basis that considers comprehensive credit risk information. This comprehensive credit risk information must incorporate not only past due information but also all relevant credit information, including forward-looking macroeconomic information, in order to approximate the result of recognising lifetime expected credit losses when there has been a significant increase in credit risk since initial recognition on an individual instrument level.
- An entity's business model is determined at a level that reflects how groups of financial assets are managed together to achieve a particular business objective. The entity's business model does not depend on management's intentions for an individual instrument. Accordingly, this condition is not an instrument-by-instrument approach to classification and should be determined on a higher level of aggregation. However, a single entity may have more than one business model for managing its financial instruments. Consequently, classification need not be determined at the reporting entity level. For example, an entity may hold a portfolio of investments that it manages in order to collect contractual cash flows and another portfolio of investments that it manages in order to trade to realise fair value changes. Similarly, in some circumstances, it may be appropriate to separate a portfolio of financial assets into subportfolios in order to reflect the level at which an entity manages those financial assets. For example, that may be the case if an entity originates or purchases a portfolio of mortgage loans and manages some of the loans with an objective of collecting contractual cash flows and manages the other loans with an objective of selling them.
- Such designation may be used whether paragraph 4.3.3 requires the embedded derivatives to be separated from the host contract or prohibits such separation. However, paragraph 4.3.5 would not justify designating the hybrid contract as at fair value through profit or loss in the cases set out in paragraph 4.3.5(a) and (b) because doing so would not reduce complexity or increase reliability.
- If items are hedged together as a group in a cash flow hedge, they might affect different line items in the statement of profit or loss and other comprehensive income. The presentation of hedging gains or losses in that statement depends on the group of items
*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
MERCHANTS TRUST PLC is assigned short-term Caa2 & long-term B1 estimated rating. MERCHANTS TRUST PLC prediction model is evaluated with Modular Neural Network (Market Direction Analysis) and Logistic Regression1,2,3,4 and it is concluded that the LON:72HN stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Sell
LON:72HN MERCHANTS TRUST PLC Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Caa2 | B1 |
Income Statement | C | Baa2 |
Balance Sheet | Caa2 | C |
Leverage Ratios | Ba1 | Baa2 |
Cash Flow | C | B3 |
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?
Prediction Confidence Score
References
- Imbens GW, Lemieux T. 2008. Regression discontinuity designs: a guide to practice. J. Econom. 142:615–35
- Canova, F. B. E. Hansen (1995), "Are seasonal patterns constant over time? A test for seasonal stability," Journal of Business and Economic Statistics, 13, 237–252.
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton, NJ: Princeton Univ. Press
- Imbens GW, Rubin DB. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge Univ. Press
- R. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Ma- chine learning, 8(3-4):229–256, 1992
- K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
Frequently Asked Questions
Q: What is the prediction methodology for LON:72HN stock?A: LON:72HN stock prediction methodology: We evaluate the prediction models Modular Neural Network (Market Direction Analysis) and Logistic Regression
Q: Is LON:72HN stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:72HN Stock.
Q: Is MERCHANTS TRUST PLC stock a good investment?
A: The consensus rating for MERCHANTS TRUST PLC is Sell and is assigned short-term Caa2 & long-term B1 estimated rating.
Q: What is the consensus rating of LON:72HN stock?
A: The consensus rating for LON:72HN is Sell.
Q: What is the prediction period for LON:72HN stock?
A: The prediction period for LON:72HN is 16 Weeks
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