Modelling A.I. in Economics

LON:FOX Stock: What stocks make fastest money?

Outlook: FOX MARBLE HOLDINGS PLC is assigned short-term B2 & long-term B3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n: for Weeks2
Methodology : Ensemble Learning (ML)
Hypothesis Testing : Lasso 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

FOX MARBLE HOLDINGS PLC prediction model is evaluated with Ensemble Learning (ML) and Lasso Regression1,2,3,4 and it is concluded that the LON:FOX 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 6 Month period, the dominant strategy among neural network is: Buy

Graph 44

Key Points

  1. Trust metric by Neural Network
  2. Prediction Modeling
  3. Market Outlook

LON:FOX Target Price Prediction Modeling Methodology

We consider FOX MARBLE HOLDINGS PLC Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of LON:FOX 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(Lasso Regression)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(Ensemble Learning (ML)) X S(n):→ 6 Month i = 1 n s i

n:Time series to forecast

p:Price signals of LON:FOX 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.

Lasso Regression

Lasso regression, also known as L1 regularization, is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates and to induce sparsity in the model. This is done by adding a term to the objective function that is proportional to the sum of the absolute values of the coefficients. The penalty term is called the "lasso" penalty, and it is controlled by a parameter called the "lasso constant". Lasso regression can be used to address the problem of multicollinearity in linear regression, as well as the problem of overfitting. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Overfitting occurs when a model is too closely fit to the training data, and as a result, it does not generalize well to new data.

 

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:FOX Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:FOX FOX MARBLE HOLDINGS PLC
Time series to forecast: 6 Month

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 Ensemble Learning (ML) based LON:FOX Stock Prediction Model

  1. IFRS 15, issued in May 2014, amended paragraphs 3.1.1, 4.2.1, 5.1.1, 5.2.1, 5.7.6, B3.2.13, B5.7.1, C5 and C42 and deleted paragraph C16 and its related heading. Paragraphs 5.1.3 and 5.7.1A, and a definition to Appendix A, were added. An entity shall apply those amendments when it applies IFRS 15.
  2. An entity shall apply Annual Improvements to IFRS Standards 2018–2020 to financial liabilities that are modified or exchanged on or after the beginning of the annual reporting period in which the entity first applies the amendment.
  3. In accordance with paragraph 4.1.3(a), principal is the fair value of the financial asset at initial recognition. However that principal amount may change over the life of the financial asset (for example, if there are repayments of principal).
  4. 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

*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:FOX FOX MARBLE HOLDINGS PLC Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2B3
Income StatementCC
Balance SheetB3B2
Leverage RatiosB2B3
Cash FlowCaa2C
Rates of Return and ProfitabilityBaa2Caa2

*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

FOX MARBLE HOLDINGS PLC is assigned short-term B2 & long-term B3 estimated rating. FOX MARBLE HOLDINGS PLC prediction model is evaluated with Ensemble Learning (ML) and Lasso Regression1,2,3,4 and it is concluded that the LON:FOX stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

Prediction Confidence Score

Trust metric by Neural Network: 93 out of 100 with 717 signals.

References

  1. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
  2. M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
  3. Chipman HA, George EI, McCulloch RE. 2010. Bart: Bayesian additive regression trees. Ann. Appl. Stat. 4:266–98
  4. Knox SW. 2018. Machine Learning: A Concise Introduction. Hoboken, NJ: Wiley
  5. D. Bertsekas. Dynamic programming and optimal control. Athena Scientific, 1995.
  6. Chernozhukov V, Newey W, Robins J. 2018c. Double/de-biased machine learning using regularized Riesz representers. arXiv:1802.08667 [stat.ML]
  7. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
Frequently Asked QuestionsQ: What is the prediction methodology for LON:FOX stock?
A: LON:FOX stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Lasso Regression
Q: Is LON:FOX stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:FOX Stock.
Q: Is FOX MARBLE HOLDINGS PLC stock a good investment?
A: The consensus rating for FOX MARBLE HOLDINGS PLC is Buy and is assigned short-term B2 & long-term B3 estimated rating.
Q: What is the consensus rating of LON:FOX stock?
A: The consensus rating for LON:FOX is Buy.
Q: What is the prediction period for LON:FOX stock?
A: The prediction period for LON:FOX is 6 Month

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