Modelling A.I. in Economics

LON:COPL Stock: Soars on Positive Economic Data (Forecast)

Outlook: CANADIAN OVERSEAS PETROLEUM LIMITED is assigned short-term B2 & 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 : Reinforcement 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.


CANADIAN OVERSEAS PETROLEUM LIMITED prediction model is evaluated with Reinforcement Machine Learning (ML) and Spearman Correlation1,2,3,4 and it is concluded that the LON:COPL stock is predictable in the short/long term. Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Graph 6

Key Points

  1. Market Signals
  2. Is it better to buy and sell or hold?
  3. Is now good time to invest?

LON:COPL Target Price Prediction Modeling Methodology

We consider CANADIAN OVERSEAS PETROLEUM LIMITED Decision Process with Reinforcement Machine Learning (ML) where A is the set of discrete actions of LON:COPL 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(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(Reinforcement Machine Learning (ML)) X S(n):→ 16 Weeks e x rx

n:Time series to forecast

p:Price signals of LON:COPL stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Reinforcement Machine Learning (ML)

Reinforcement machine learning (RL) is a type of machine learning where an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and is able to learn from its mistakes. RL is a powerful tool that can be used for a variety of tasks, including game playing, robotics, and finance.

Spearman Correlation

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:COPL Stock Forecast (Buy or Sell)

Sample Set: Neural Network
Stock/Index: LON:COPL CANADIAN OVERSEAS PETROLEUM LIMITED
Time series to forecast: 16 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 Reinforcement Machine Learning (ML) based LON:COPL Stock Prediction Model

  1. When designating a risk component as a hedged item, the hedge accounting requirements apply to that risk component in the same way as they apply to other hedged items that are not risk components. For example, the qualifying criteria apply, including that the hedging relationship must meet the hedge effectiveness requirements, and any hedge ineffectiveness must be measured and recognised.
  2. 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.
  3. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
  4. For hedges other than hedges of foreign currency risk, when an entity designates a non-derivative financial asset or a non-derivative financial liability measured at fair value through profit or loss as a hedging instrument, it may only designate the non-derivative financial instrument in its entirety or a proportion of it.

*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:COPL CANADIAN OVERSEAS PETROLEUM LIMITED Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*B2Ba3
Income StatementCaa2Baa2
Balance SheetBaa2B1
Leverage RatiosBa2Caa2
Cash FlowB2B1
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. Hoerl AE, Kennard RW. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67
  2. Efron B, Hastie T, Johnstone I, Tibshirani R. 2004. Least angle regression. Ann. Stat. 32:407–99
  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. Clements, M. P. D. F. Hendry (1995), "Forecasting in cointegrated systems," Journal of Applied Econometrics, 10, 127–146.
  5. K. Boda and J. Filar. Time consistent dynamic risk measures. Mathematical Methods of Operations Research, 63(1):169–186, 2006
  6. Bessler, D. A. T. Covey (1991), "Cointegration: Some results on U.S. cattle prices," Journal of Futures Markets, 11, 461–474.
  7. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
Frequently Asked QuestionsQ: What is the prediction methodology for LON:COPL stock?
A: LON:COPL stock prediction methodology: We evaluate the prediction models Reinforcement Machine Learning (ML) and Spearman Correlation
Q: Is LON:COPL stock a buy or sell?
A: The dominant strategy among neural network is to Buy LON:COPL Stock.
Q: Is CANADIAN OVERSEAS PETROLEUM LIMITED stock a good investment?
A: The consensus rating for CANADIAN OVERSEAS PETROLEUM LIMITED is Buy and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of LON:COPL stock?
A: The consensus rating for LON:COPL is Buy.
Q: What is the prediction period for LON:COPL stock?
A: The prediction period for LON:COPL is 16 Weeks

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