Modelling A.I. in Economics

ANALYZING LON:EJFI STOCK: A COMPREHENSIVE EVALUATION OF GROWTH POTENTIAL AND INVESTMENT OPPORTUNITIES (Forecast)

Outlook: EJF INVESTMENTS LTD is assigned short-term B1 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Supervised Machine Learning (ML)
Hypothesis Testing : Spearman Correlation
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.


EJF INVESTMENTS LTD prediction model is evaluated with Supervised Machine Learning (ML) and Spearman Correlation1,2,3,4 and it is concluded that the LON:EJFI stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Sell

Graph 40

Key Points

  1. What are the most successful trading algorithms?
  2. What is a prediction confidence?
  3. What are the most successful trading algorithms?

LON:EJFI Stock Price Forecast

We consider EJF INVESTMENTS LTD Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of LON:EJFI 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: LON:EJFI EJF INVESTMENTS LTD
Time series to forecast: 6 Month

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


F(Spearman Correlation)5,6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Supervised Machine Learning (ML)) X S(n):→ 6 Month R = r 1 r 2 r 3

n:Time series to forecast

p:Price signals of LON:EJFI stock

j:Nash equilibria (Neural Network)

k:Dominated move of LON:EJFI stock holders

a:Best response for LON:EJFI target price


Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.Spearman correlation is a nonparametric measure of the strength and direction of association between two variables. It is a rank-based correlation, which means that it does not assume that the data is normally distributed. Spearman correlation is calculated by first ranking the data for each variable, and then calculating the Pearson correlation between the ranks.

 

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:EJFI 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 Supervised Machine Learning (ML) based LON:EJFI Stock Prediction Model

  1. Changes in market conditions that give rise to market risk include changes in a benchmark interest rate, the price of another entity's financial instrument, a commodity price, a foreign exchange rate or an index of prices or rates.
  2. When identifying what risk components qualify for designation as a hedged item, an entity assesses such risk components within the context of the particular market structure to which the risk or risks relate and in which the hedging activity takes place. Such a determination requires an evaluation of the relevant facts and circumstances, which differ by risk and market.
  3. When an entity, consistent with its hedge documentation, frequently resets (ie discontinues and restarts) a hedging relationship because both the hedging instrument and the hedged item frequently change (ie the entity uses a dynamic process in which both the hedged items and the hedging instruments used to manage that exposure do not remain the same for long), the entity shall apply the requirement in paragraphs 6.3.7(a) and B6.3.8—that the risk component is separately identifiable—only when it initially designates a hedged item in that hedging relationship. A hedged item that has been assessed at the time of its initial designation in the hedging relationship, whether it was at the time of the hedge inception or subsequently, is not reassessed at any subsequent redesignation in the same hedging relationship.
  4. The accounting for the forward element of forward contracts in accordance with paragraph 6.5.16 applies only to the extent that the forward element relates to the hedged item (aligned forward element). The forward element of a forward contract relates to the hedged item if the critical terms of the forward contract (such as the nominal amount, life and underlying) are aligned with the hedged item. Hence, if the critical terms of the forward contract and the hedged item are not fully aligned, an entity shall determine the aligned forward element, ie how much of the forward element included in the forward contract (actual forward element) relates to the hedged item (and therefore should be treated in accordance with paragraph 6.5.16). An entity determines the aligned forward element using the valuation of the forward contract that would have critical terms that perfectly match the hedged item.

*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.

LON:EJFI EJF INVESTMENTS LTD Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B1B2
Income StatementBa3Baa2
Balance SheetB1C
Leverage RatiosBaa2C
Cash FlowBaa2B2
Rates of Return and ProfitabilityCBaa2

*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

  1. Sutton RS, Barto AG. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press
  2. J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37(3):332–341, 1992.
  3. D. Bertsekas and J. Tsitsiklis. Neuro-dynamic programming. Athena Scientific, 1996.
  4. Bengio Y, Ducharme R, Vincent P, Janvin C. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3:1137–55
  5. Greene WH. 2000. Econometric Analysis. Upper Saddle River, N J: Prentice Hall. 4th ed.
  6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
  7. J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson. Learning to communicate with deep multi-agent reinforcement learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 2137–2145, 2016.
Frequently Asked QuestionsQ: What is the prediction methodology for LON:EJFI stock?
A: LON:EJFI stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Spearman Correlation
Q: Is LON:EJFI stock a buy or sell?
A: The dominant strategy among neural network is to Sell LON:EJFI Stock.
Q: Is EJF INVESTMENTS LTD stock a good investment?
A: The consensus rating for EJF INVESTMENTS LTD is Sell and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of LON:EJFI stock?
A: The consensus rating for LON:EJFI is Sell.
Q: What is the prediction period for LON:EJFI stock?
A: The prediction period for LON:EJFI is 6 Month

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