Modelling A.I. in Economics

ELD:TSX Stock: A High-Risk, High-Reward Investment

Outlook: Eldorado Gold Corporation is assigned short-term Ba3 & long-term Ba1 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised* :
Dominant Strategy : Buy
Time series to forecast n: for 3 Month
Methodology : Ensemble Learning (ML)
Hypothesis Testing : Multiple Regression
Surveillance : Major exchange and OTC

Abstract

Eldorado Gold Corporation prediction model is evaluated with Ensemble Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the ELD:TSX 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 3 Month period, the dominant strategy among neural network is: Buy


*Revision

We revised our short-term strategy to .(Based on stock rating surveillance.) We also affirmed our outlook and Eldorado Gold Corporation is assigned short-term Ba3 & long-term Ba1 estimated rating.

Graph 27

Key Points

  1. Understanding Buy, Sell, and Hold Ratings
  2. How useful are statistical predictions?
  3. Nash Equilibria

ELD:TSX Target Price Prediction Modeling Methodology

We consider Eldorado Gold Corporation Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of ELD:TSX 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(Multiple 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):→ 3 Month i = 1 n a i

n:Time series to forecast

p:Price signals of ELD:TSX 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.

Multiple Regression

Multiple regression is a statistical method that analyzes the relationship between a dependent variable and multiple independent variables. The dependent variable is the variable that is being predicted, and the independent variables are the variables that are used to predict the dependent variable. Multiple regression is a more complex statistical method than simple linear regression, which only analyzes the relationship between a dependent variable and one independent variable. Multiple regression can be used to analyze more complex relationships between variables, and it can also be used to control for confounding variables. A confounding variable is a variable that is correlated with both the dependent variable and one or more of the independent variables. Confounding variables can distort the relationship between the dependent variable and the independent variables. Multiple regression can be used to control for confounding variables by including them in the model.

 

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?

ELD:TSX Stock Forecast (Buy or Sell) for 3 Month

Sample Set: Neural Network
Stock/Index: ELD:TSX Eldorado Gold Corporation
Time series to forecast: 3 Month

According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

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 Eldorado Gold Corporation

  1. If a collar, in the form of a purchased call and written put, prevents a transferred asset from being derecognised and the entity measures the asset at fair value, it continues to measure the asset at fair value. The associated liability is measured at (i) the sum of the call exercise price and fair value of the put option less the time value of the call option, if the call option is in or at the money, or (ii) the sum of the fair value of the asset and the fair value of the put option less the time value of the call option if the call option is out of the money. The adjustment to the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the options held and written by the entity. For example, assume an entity transfers a financial asset that is measured at fair value while simultaneously purchasing a call with an exercise price of CU120 and writing a put with an exercise price of CU80. Assume also that the fair value of the asset is CU100 at the date of the transfer. The time value of the put and call are CU1 and CU5 respectively. In this case, the entity recognises an asset of CU100 (the fair value of the asset) and a liability of CU96 [(CU100 + CU1) – CU5]. This gives a net asset value of CU4, which is the fair value of the options held and written by the entity.
  2. The following are examples of when the objective of the entity's business model may be achieved by both collecting contractual cash flows and selling financial assets. This list of examples is not exhaustive. Furthermore, the examples are not intended to describe all the factors that may be relevant to the assessment of the entity's business model nor specify the relative importance of the factors.
  3. If a financial instrument that was previously recognised as a financial asset is measured at fair value through profit or loss and its fair value decreases below zero, it is a financial liability measured in accordance with paragraph 4.2.1. However, hybrid contracts with hosts that are assets within the scope of this Standard are always measured in accordance with paragraph 4.3.2.
  4. When designating risk components as hedged items, an entity considers whether the risk components are explicitly specified in a contract (contractually specified risk components) or whether they are implicit in the fair value or the cash flows of an item of which they are a part (noncontractually specified risk components). Non-contractually specified risk components can relate to items that are not a contract (for example, forecast transactions) or contracts that do not explicitly specify the component (for example, a firm commitment that includes only one single price instead of a pricing formula that references different underlyings)

*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

Eldorado Gold Corporation is assigned short-term Ba3 & long-term Ba1 estimated rating. Eldorado Gold Corporation prediction model is evaluated with Ensemble Learning (ML) and Multiple Regression1,2,3,4 and it is concluded that the ELD:TSX stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

ELD:TSX Eldorado Gold Corporation Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba1
Income StatementBaa2Caa2
Balance SheetBa3B3
Leverage RatiosBa2Baa2
Cash FlowCBaa2
Rates of Return and ProfitabilityBa1Baa2

*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

Trust metric by Neural Network: 91 out of 100 with 576 signals.

References

  1. Brailsford, T.J. R.W. Faff (1996), "An evaluation of volatility forecasting techniques," Journal of Banking Finance, 20, 419–438.
  2. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Is FFBC Stock Buy or Sell?(Stock Forecast). AC Investment Research Journal, 101(3).
  3. Mikolov T, Chen K, Corrado GS, Dean J. 2013a. Efficient estimation of word representations in vector space. arXiv:1301.3781 [cs.CL]
  4. Breiman L. 2001b. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16:199–231
  5. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Ma- chine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994, pages 157–163, 1994
  6. D. Bertsekas. Min common/max crossing duality: A geometric view of conjugacy in convex optimization. Lab. for Information and Decision Systems, MIT, Tech. Rep. Report LIDS-P-2796, 2009
  7. Bell RM, Koren Y. 2007. Lessons from the Netflix prize challenge. ACM SIGKDD Explor. Newsl. 9:75–79
Frequently Asked QuestionsQ: What is the prediction methodology for ELD:TSX stock?
A: ELD:TSX stock prediction methodology: We evaluate the prediction models Ensemble Learning (ML) and Multiple Regression
Q: Is ELD:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Buy ELD:TSX Stock.
Q: Is Eldorado Gold Corporation stock a good investment?
A: The consensus rating for Eldorado Gold Corporation is Buy and is assigned short-term Ba3 & long-term Ba1 estimated rating.
Q: What is the consensus rating of ELD:TSX stock?
A: The consensus rating for ELD:TSX is Buy.
Q: What is the prediction period for ELD:TSX stock?
A: The prediction period for ELD:TSX is 3 Month

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