GURE Stock: The Wild Ride Continues

Outlook: Gulf Resources Inc. (NV) Common Stock is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised* :
Dominant Strategy : Speculative Trend
Time series to forecast n: for 16 Weeks
Methodology : Transfer Learning (ML)

Summary

Gulf Resources Inc. (NV) Common Stock prediction model is evaluated with Transfer Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the GURE stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend

*Revision

We revised our short-term strategy to and affirmed is assigned short-term B2 & long-term Ba3 estimated rating.

Key Points

1. What is Markov decision process in reinforcement learning?
2. What is prediction in deep learning?
3. Game Theory

GURE Target Price Prediction Modeling Methodology

We consider Gulf Resources Inc. (NV) Common Stock Decision Process with Transfer Learning (ML) where A is the set of discrete actions of GURE 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(Sign Test)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ 16 Weeks $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of GURE stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Transfer Learning (ML)

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.

Sign Test

The sign test is a non-parametric hypothesis test that is used to compare two paired samples. In a paired sample, each data point in one sample is paired with a data point in the other sample. The pairs are typically related in some way, such as before and after measurements, or measurements from the same subject under different conditions. The sign test is a non-parametric test, which means that it does not assume that the data is normally distributed. The sign test is also a dependent samples test, which means that the data points in each pair are correlated.

 

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?

GURE Stock Forecast (Buy or Sell) for 16 Weeks

Sample Set: Neural Network
Stock/Index: GURE Gulf Resources Inc. (NV) Common Stock
Time series to forecast n: 16 Weeks

According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend

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 Gulf Resources Inc. (NV) Common Stock

1. If a call option right retained by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the asset continues to be measured at its fair value. The associated liability is measured at (i) the option exercise price less the time value of the option if the option is in or at the money, or (ii) the fair value of the transferred asset less the time value of the option if the option is out of the money. The adjustment to the measurement of the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the call option right. For example, if the fair value of the underlying asset is CU80, the option exercise price is CU95 and the time value of the option is CU5, the carrying amount of the associated liability is CU75 (CU80 – CU5) and the carrying amount of the transferred asset is CU80 (ie its fair value)
2. Rebalancing refers to the adjustments made to the designated quantities of the hedged item or the hedging instrument of an already existing hedging relationship for the purpose of maintaining a hedge ratio that complies with the hedge effectiveness requirements. Changes to designated quantities of a hedged item or of a hedging instrument for a different purpose do not constitute rebalancing for the purpose of this Standard
3. When measuring the fair values of the part that continues to be recognised and the part that is derecognised for the purposes of applying paragraph 3.2.13, an entity applies the fair value measurement requirements in IFRS 13 Fair Value Measurement in addition to paragraph 3.2.14.
4. The methods used to determine whether credit risk has increased significantly on a financial instrument since initial recognition should consider the characteristics of the financial instrument (or group of financial instruments) and the default patterns in the past for comparable financial instruments. Despite the requirement in paragraph 5.5.9, for financial instruments for which default patterns are not concentrated at a specific point during the expected life of the financial instrument, changes in the risk of a default occurring over the next 12 months may be a reasonable approximation of the changes in the lifetime risk of a default occurring. In such cases, an entity may use changes in the risk of a default occurring over the next 12 months to determine whether credit risk has increased significantly since initial recognition, unless circumstances indicate that a lifetime assessment is necessary

*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

Gulf Resources Inc. (NV) Common Stock is assigned short-term B2 & long-term Ba3 estimated rating. Gulf Resources Inc. (NV) Common Stock prediction model is evaluated with Transfer Learning (ML) and Sign Test^1,2,3,4 and it is concluded that the GURE stock is predictable in the short/long term. According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Speculative Trend

GURE Gulf Resources Inc. (NV) Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Income Statement	Baa2	B1
Balance Sheet	C	C
Leverage Ratios	Caa2	Ba3
Cash Flow	C	B1
Rates of Return and Profitability	Baa2	Baa2

*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: 80 out of 100 with 554 signals.

References

1. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
2. Pennington J, Socher R, Manning CD. 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, pp. 1532–43. New York: Assoc. Comput. Linguist.
3. M. J. Hausknecht. Cooperation and Communication in Multiagent Deep Reinforcement Learning. PhD thesis, The University of Texas at Austin, 2016
4. Scott SL. 2010. A modern Bayesian look at the multi-armed bandit. Appl. Stoch. Models Bus. Ind. 26:639–58
5. J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel. Multi-agent Reinforcement Learning in Sequential Social Dilemmas. In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), Sao Paulo, Brazil, 2017
6. Miller A. 2002. Subset Selection in Regression. New York: CRC Press
7. M. J. Hausknecht and P. Stone. Deep recurrent Q-learning for partially observable MDPs. CoRR, abs/1507.06527, 2015

Frequently Asked Questions

Q: What is the prediction methodology for GURE stock?
A: GURE stock prediction methodology: We evaluate the prediction models Transfer Learning (ML) and Sign Test
Q: Is GURE stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend GURE Stock.
Q: Is Gulf Resources Inc. (NV) Common Stock stock a good investment?
A: The consensus rating for Gulf Resources Inc. (NV) Common Stock is Speculative Trend and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of GURE stock?
A: The consensus rating for GURE is Speculative Trend.
Q: What is the prediction period for GURE stock?
A: The prediction period for GURE is 16 Weeks

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