AUC Score :
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Supervised Machine Learning (ML)
Hypothesis Testing : Sign 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
CLEANAWAY WASTE MANAGEMENT LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Sign Test1,2,3,4 and it is concluded that the CWY stock is predictable in the short/long term. Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold
Key Points
- Is it better to buy and sell or hold?
- Decision Making
- Buy, Sell and Hold Signals
CWY Target Price Prediction Modeling Methodology
We consider CLEANAWAY WASTE MANAGEMENT LIMITED Decision Process with Supervised Machine Learning (ML) where A is the set of discrete actions of CWY 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= X R(Supervised Machine Learning (ML)) X S(n):→ 8 Weeks
n:Time series to forecast
p:Price signals of CWY stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Supervised Machine Learning (ML)
Supervised machine learning (ML) is a type of machine learning where a model is trained on labeled data. This means that the data has been tagged with the correct output for the input data. The model learns to predict the output for new input data based on the labeled data. Supervised ML is a powerful tool that can be used for a variety of tasks, including classification, regression, and forecasting. Classification tasks involve predicting the category of an input data, such as whether an email is spam or not. Regression tasks involve predicting a numerical value for an input data, such as the price of a house. Forecasting tasks involve predicting future values for a time series, such as the sales of a product.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?
CWY Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: CWY CLEANAWAY WASTE MANAGEMENT LIMITED
Time series to forecast: 8 Weeks
According to price forecasts, 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 Supervised Machine Learning (ML) based CWY Stock Prediction Model
- For the purposes of applying the requirements in paragraphs 5.7.7 and 5.7.8, an accounting mismatch is not caused solely by the measurement method that an entity uses to determine the effects of changes in a liability's credit risk. An accounting mismatch in profit or loss would arise only when the effects of changes in the liability's credit risk (as defined in IFRS 7) are expected to be offset by changes in the fair value of another financial instrument. A mismatch that arises solely as a result of the measurement method (ie because an entity does not isolate changes in a liability's credit risk from some other changes in its fair value) does not affect the determination required by paragraphs 5.7.7 and 5.7.8. For example, an entity may not isolate changes in a liability's credit risk from changes in liquidity risk. If the entity presents the combined effect of both factors in other comprehensive income, a mismatch may occur because changes in liquidity risk may be included in the fair value measurement of the entity's financial assets and the entire fair value change of those assets is presented in profit or loss. However, such a mismatch is caused by measurement imprecision, not the offsetting relationship described in paragraph B5.7.6 and, therefore, does not affect the determination required by paragraphs 5.7.7 and 5.7.8.
- In the reporting period that includes the date of initial application of these amendments, an entity is not required to present the quantitative information required by paragraph 28(f) of IAS 8.
- To the extent that a transfer of a financial asset does not qualify for derecognition, the transferee does not recognise the transferred asset as its asset. The transferee derecognises the cash or other consideration paid and recognises a receivable from the transferor. If the transferor has both a right and an obligation to reacquire control of the entire transferred asset for a fixed amount (such as under a repurchase agreement), the transferee may measure its receivable at amortised cost if it meets the criteria in paragraph 4.1.2.
- Rebalancing is accounted for as a continuation of the hedging relationship in accordance with paragraphs B6.5.9–B6.5.21. On rebalancing, the hedge ineffectiveness of the hedging relationship is determined and recognised immediately before adjusting the hedging relationship.
*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.
CWY CLEANAWAY WASTE MANAGEMENT LIMITED Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba3 | B1 |
Income Statement | Baa2 | Caa2 |
Balance Sheet | B1 | B2 |
Leverage Ratios | Baa2 | Caa2 |
Cash Flow | B2 | B1 |
Rates of Return and Profitability | Caa2 | 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?
Conclusions
CLEANAWAY WASTE MANAGEMENT LIMITED is assigned short-term Ba3 & long-term B1 estimated rating. CLEANAWAY WASTE MANAGEMENT LIMITED prediction model is evaluated with Supervised Machine Learning (ML) and Sign Test1,2,3,4 and it is concluded that the CWY stock is predictable in the short/long term. According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Hold
Prediction Confidence Score
References
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2016a. Double machine learning for treatment and causal parameters. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- Firth JR. 1957. A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis (Special Volume of the Philological Society), ed. JR Firth, pp. 1–32. Oxford, UK: Blackwell
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- Mnih A, Hinton GE. 2007. Three new graphical models for statistical language modelling. In International Conference on Machine Learning, pp. 641–48. La Jolla, CA: Int. Mach. Learn. Soc.
- Hartford J, Lewis G, Taddy M. 2016. Counterfactual prediction with deep instrumental variables networks. arXiv:1612.09596 [stat.AP]
- Kallus N. 2017. Balanced policy evaluation and learning. arXiv:1705.07384 [stat.ML]
- White H. 1992. Artificial Neural Networks: Approximation and Learning Theory. Oxford, UK: Blackwell
Frequently Asked Questions
Q: What is the prediction methodology for CWY stock?A: CWY stock prediction methodology: We evaluate the prediction models Supervised Machine Learning (ML) and Sign Test
Q: Is CWY stock a buy or sell?
A: The dominant strategy among neural network is to Hold CWY Stock.
Q: Is CLEANAWAY WASTE MANAGEMENT LIMITED stock a good investment?
A: The consensus rating for CLEANAWAY WASTE MANAGEMENT LIMITED is Hold and is assigned short-term Ba3 & long-term B1 estimated rating.
Q: What is the consensus rating of CWY stock?
A: The consensus rating for CWY is Hold.
Q: What is the prediction period for CWY stock?
A: The prediction period for CWY is 8 Weeks
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