AUC Score :
Short-Term Revised1 :
Dominant Strategy : SellBuy
Time series to forecast n:
Methodology : Deductive Inference (ML)
Hypothesis Testing : Wilcoxon Sign-Rank Test
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
G.U.D. HOLDINGS LIMITED prediction model is evaluated with Deductive Inference (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the GUD stock is predictable in the short/long term. Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: SellBuy
Key Points
- What is statistical models in machine learning?
- Understanding Buy, Sell, and Hold Ratings
- Can machine learning predict?
GUD Target Price Prediction Modeling Methodology
We consider G.U.D. HOLDINGS LIMITED Decision Process with Deductive Inference (ML) where A is the set of discrete actions of GUD 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(Wilcoxon Sign-Rank Test)5,6,7= X R(Deductive Inference (ML)) X S(n):→ 4 Weeks
n:Time series to forecast
p:Price signals of GUD stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Deductive Inference (ML)
Deductive inference is a type of reasoning in which a conclusion is drawn based on a set of premises that are assumed to be true. In machine learning (ML), deductive inference can be used to create models that can make predictions about new data based on a set of known rules. Deductive inference is a supervised learning algorithm, which means that it requires labeled data to train. The labeled data is used to train the model to make predictions about new data. There are many different types of deductive inference algorithms, including decision trees, rule-based systems, and expert systems. Each type of algorithm has its own strengths and weaknesses.Wilcoxon Sign-Rank Test
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.
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?
GUD Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: GUD G.U.D. HOLDINGS LIMITED
Time series to forecast: 4 Weeks
According to price forecasts, the dominant strategy among neural network is: SellBuy
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 Deductive Inference (ML) based GUD Stock Prediction Model
- In some cases, the qualitative and non-statistical quantitative information available may be sufficient to determine that a financial instrument has met the criterion for the recognition of a loss allowance at an amount equal to lifetime expected credit losses. That is, the information does not need to flow through a statistical model or credit ratings process in order to determine whether there has been a significant increase in the credit risk of the financial instrument. In other cases, an entity may need to consider other information, including information from its statistical models or credit ratings processes.
- When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.
- An entity is not required to incorporate forecasts of future conditions over the entire expected life of a financial instrument. The degree of judgement that is required to estimate expected credit losses depends on the availability of detailed information. As the forecast horizon increases, the availability of detailed information decreases and the degree of judgement required to estimate expected credit losses increases. The estimate of expected credit losses does not require a detailed estimate for periods that are far in the future—for such periods, an entity may extrapolate projections from available, detailed information.
- An entity is not required to restate prior periods to reflect the application of these amendments. The entity may restate prior periods only if it is possible to do so without the use of hindsight. If an entity restates prior periods, the restated financial statements must reflect all the requirements in this Standard for the affected financial instruments. If an entity does not restate prior periods, the entity shall recognise any difference between the previous carrying amount and the carrying amount at the beginning of the annual reporting period that includes the date of initial application of these amendments in the opening retained earnings (or other component of equity, as appropriate) of the annual reporting period that includes the date of initial application of these amendments.
*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.
GUD G.U.D. HOLDINGS LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B1 | B2 |
Income Statement | C | Caa2 |
Balance Sheet | Baa2 | B3 |
Leverage Ratios | B2 | Caa2 |
Cash Flow | B1 | B1 |
Rates of Return and Profitability | Ba2 | B2 |
*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
G.U.D. HOLDINGS LIMITED is assigned short-term B1 & long-term B2 estimated rating. G.U.D. HOLDINGS LIMITED prediction model is evaluated with Deductive Inference (ML) and Wilcoxon Sign-Rank Test1,2,3,4 and it is concluded that the GUD stock is predictable in the short/long term. According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: SellBuy
Prediction Confidence Score
References
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- L. Busoniu, R. Babuska, and B. D. Schutter. A comprehensive survey of multiagent reinforcement learning. IEEE Transactions of Systems, Man, and Cybernetics Part C: Applications and Reviews, 38(2), 2008.
- J. Harb and D. Precup. Investigating recurrence and eligibility traces in deep Q-networks. In Deep Reinforcement Learning Workshop, NIPS 2016, Barcelona, Spain, 2016.
- Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., Tempur Sealy Stock Forecast & Analysis. AC Investment Research Journal, 101(3).
- Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
- 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).
- Breiman L. 1993. Better subset selection using the non-negative garotte. Tech. Rep., Univ. Calif., Berkeley
Frequently Asked Questions
Q: What is the prediction methodology for GUD stock?A: GUD stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Wilcoxon Sign-Rank Test
Q: Is GUD stock a buy or sell?
A: The dominant strategy among neural network is to SellBuy GUD Stock.
Q: Is G.U.D. HOLDINGS LIMITED stock a good investment?
A: The consensus rating for G.U.D. HOLDINGS LIMITED is SellBuy and is assigned short-term B1 & long-term B2 estimated rating.
Q: What is the consensus rating of GUD stock?
A: The consensus rating for GUD is SellBuy.
Q: What is the prediction period for GUD stock?
A: The prediction period for GUD is 4 Weeks
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