AUC Score :
Short-Term Revised :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Transductive Learning (ML)
Hypothesis Testing : Independent T-Test
Surveillance : Major exchange and OTC
Summary
FAR EAST GOLD LTD prediction model is evaluated with Transductive Learning (ML) and Independent T-Test1,2,3,4 and it is concluded that the FEG stock is predictable in the short/long term. Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
Key Points
- How do you decide buy or sell a stock?
- How can neural networks improve predictions?
- Can we predict stock market using machine learning?
FEG Target Price Prediction Modeling Methodology
We consider FAR EAST GOLD LTD Decision Process with Transductive Learning (ML) where A is the set of discrete actions of FEG 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(Independent T-Test)5,6,7= X R(Transductive Learning (ML)) X S(n):→ 3 Month
n:Time series to forecast
p:Price signals of FEG stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Transductive Learning (ML)
Transductive learning is a supervised machine learning (ML) method in which the model is trained on both labeled and unlabeled data. The goal of transductive learning is to predict the labels of the unlabeled data. Transductive learning is a hybrid of inductive and semi-supervised learning. Inductive learning algorithms are trained on labeled data only, while semi-supervised learning algorithms are trained on a combination of labeled and unlabeled data. Transductive learning algorithms can achieve better performance than inductive learning algorithms on tasks where there is a small amount of labeled data. This is because transductive learning algorithms can use the unlabeled data to help them learn the relationships between the features and the labels.Independent T-Test
An independent t-test is a statistical test that compares the means of two independent samples. In an independent t-test, the data points in each sample are not related to each other. The independent t-test is a parametric test, which means that it assumes that the data is normally distributed. The independent t-test is also a two-sample test, which means that it compares the means of two independent samples.
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?
FEG Stock Forecast (Buy or Sell) for 3 Month
Sample Set: Neural NetworkStock/Index: FEG FAR EAST GOLD LTD
Time series to forecast: 3 Month
According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
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 Transductive Learning (ML) based FEG Stock Prediction Model
- A similar example of a non-financial item is a specific type of crude oil from a particular oil field that is priced off the relevant benchmark crude oil. If an entity sells that crude oil under a contract using a contractual pricing formula that sets the price per barrel at the benchmark crude oil price minus CU10 with a floor of CU15, the entity can designate as the hedged item the entire cash flow variability under the sales contract that is attributable to the change in the benchmark crude oil price. However, the entity cannot designate a component that is equal to the full change in the benchmark crude oil price. Hence, as long as the forward price (for each delivery) does not fall below CU25, the hedged item has the same cash flow variability as a crude oil sale at the benchmark crude oil price (or with a positive spread). However, if the forward price for any delivery falls below CU25, the hedged item has a lower cash flow variability than a crude oil sale at the benchmark crude oil price (or with a positive spread).
- For a discontinued hedging relationship, when the interest rate benchmark on which the hedged future cash flows had been based is changed as required by interest rate benchmark reform, for the purpose of applying paragraph 6.5.12 in order to determine whether the hedged future cash flows are expected to occur, the amount accumulated in the cash flow hedge reserve for that hedging relationship shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows will be based.
- If an entity measures a hybrid contract at fair value in accordance with paragraphs 4.1.2A, 4.1.4 or 4.1.5 but the fair value of the hybrid contract had not been measured in comparative reporting periods, the fair value of the hybrid contract in the comparative reporting periods shall be the sum of the fair values of the components (ie the non-derivative host and the embedded derivative) at the end of each comparative reporting period if the entity restates prior periods (see paragraph 7.2.15).
- The assessment of whether an economic relationship exists includes an analysis of the possible behaviour of the hedging relationship during its term to ascertain whether it can be expected to meet the risk management objective. The mere existence of a statistical correlation between two variables does not, by itself, support a valid conclusion that an economic relationship exists.
*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.
FEG FAR EAST GOLD LTD Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | B2 |
Income Statement | Ba2 | C |
Balance Sheet | C | B2 |
Leverage Ratios | C | B1 |
Cash Flow | Baa2 | B3 |
Rates of Return and Profitability | Baa2 | B3 |
*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
FAR EAST GOLD LTD is assigned short-term B2 & long-term B2 estimated rating. FAR EAST GOLD LTD prediction model is evaluated with Transductive Learning (ML) and Independent T-Test1,2,3,4 and it is concluded that the FEG stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
Prediction Confidence Score
References
- ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. The Dow Jones Industrial Average (No. Stock Analysis). AC Investment Research.
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
- R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
- J. Filar, L. Kallenberg, and H. Lee. Variance-penalized Markov decision processes. Mathematics of Opera- tions Research, 14(1):147–161, 1989
- Chen X. 2007. Large sample sieve estimation of semi-nonparametric models. In Handbook of Econometrics, Vol. 6B, ed. JJ Heckman, EE Learner, pp. 5549–632. Amsterdam: Elsevier
- V. Borkar. An actor-critic algorithm for constrained Markov decision processes. Systems & Control Letters, 54(3):207–213, 2005.
Frequently Asked Questions
Q: What is the prediction methodology for FEG stock?A: FEG stock prediction methodology: We evaluate the prediction models Transductive Learning (ML) and Independent T-Test
Q: Is FEG stock a buy or sell?
A: The dominant strategy among neural network is to Hold FEG Stock.
Q: Is FAR EAST GOLD LTD stock a good investment?
A: The consensus rating for FAR EAST GOLD LTD is Hold and is assigned short-term B2 & long-term B2 estimated rating.
Q: What is the consensus rating of FEG stock?
A: The consensus rating for FEG is Hold.
Q: What is the prediction period for FEG stock?
A: The prediction period for FEG is 3 Month