HAYW Stock: A High-Growth Company with a Bright Future

Outlook: Hayward Holdings Inc. Common Stock is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Ensemble Learning (ML)
Hypothesis Testing : Wilcoxon Rank-Sum 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

Hayward Holdings Inc. Common Stock prediction model is evaluated with Ensemble Learning (ML) and Wilcoxon Rank-Sum Test^1,2,3,4 and it is concluded that the HAYW 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.⁵ According to price forecasts for 3 Month period, the dominant strategy among neural network is: Buy

Key Points

1. Ensemble Learning (ML) for HAYW stock price prediction process.
2. Wilcoxon Rank-Sum Test
3. Fundemental Analysis with Algorithmic Trading
4. Buy, Sell and Hold Signals
5. Fundemental Analysis with Algorithmic Trading

HAYW Stock Price Forecast

We consider Hayward Holdings Inc. Common Stock Decision Process with Ensemble Learning (ML) where A is the set of discrete actions of HAYW 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

Sample Set: Neural Network
Stock/Index: HAYW Hayward Holdings Inc. Common Stock
Time series to forecast: 3 Month

According to price forecasts, the dominant strategy among neural network is: Buy

F(Wilcoxon Rank-Sum Test)^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(Ensemble Learning (ML)) X S(n):→ 3 Month $\overrightarrow{S}=\left(s_1,s_2,s_3\right)$

n:Time series to forecast

p:Price signals of HAYW stock

j:Nash equilibria (Neural Network)

k:Dominated move of HAYW stock holders

a:Best response for HAYW target price

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.⁵ 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.^6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do Predictive A.I. algorithms actually work?

HAYW Stock Forecast (Buy or Sell) Strategic Interaction Table

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 Ensemble Learning (ML) based HAYW Stock Prediction Model

1. When a group of items that constitute a net position is designated as a hedged item, an entity shall designate the overall group of items that includes the items that can make up the net position. An entity is not permitted to designate a non-specific abstract amount of a net position. For example, an entity has a group of firm sale commitments in nine months' time for FC100 and a group of firm purchase commitments in 18 months' time for FC120. The entity cannot designate an abstract amount of a net position up to FC20. Instead, it must designate a gross amount of purchases and a gross amount of sales that together give rise to the hedged net position. An entity shall designate gross positions that give rise to the net position so that the entity is able to comply with the requirements for the accounting for qualifying hedging relationships.
2. The risk of a default occurring on financial instruments that have comparable credit risk is higher the longer the expected life of the instrument; for example, the risk of a default occurring on an AAA-rated bond with an expected life of 10 years is higher than that on an AAA-rated bond with an expected life of five years.
3. An entity's risk management is the main source of information to perform the assessment of whether a hedging relationship meets the hedge effectiveness requirements. This means that the management information (or analysis) used for decision-making purposes can be used as a basis for assessing whether a hedging relationship meets the hedge effectiveness requirements.
4. An entity has not retained control of a transferred asset if the transferee has the practical ability to sell the transferred asset. An entity has retained control of a transferred asset if the transferee does not have the practical ability to sell the transferred asset. A transferee has the practical ability to sell the transferred asset if it is traded in an active market because the transferee could repurchase the transferred asset in the market if it needs to return the asset to the entity. For example, a transferee may have the practical ability to sell a transferred asset if the transferred asset is subject to an option that allows the entity to repurchase it, but the transferee can readily obtain the transferred asset in the market if the option is exercised. A transferee does not have the practical ability to sell the transferred asset if the entity retains such an option and the transferee cannot readily obtain the transferred asset in the market if the entity exercises its option

*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.

HAYW Hayward Holdings Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Income Statement	C	Ba3
Balance Sheet	Ba1	Baa2
Leverage Ratios	B3	B1
Cash Flow	Caa2	Ba1
Rates of Return and Profitability	B2	C

*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?

References

1. Chamberlain G. 2000. Econometrics and decision theory. J. Econom. 95:255–83
2. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60
3. Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
4. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
5. Arjovsky M, Bottou L. 2017. Towards principled methods for training generative adversarial networks. arXiv:1701.04862 [stat.ML]
6. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
7. Vilnis L, McCallum A. 2015. Word representations via Gaussian embedding. arXiv:1412.6623 [cs.CL]

Frequently Asked Questions

Q: Is HAYW stock expected to rise?
A: HAYW stock prediction model is evaluated with Ensemble Learning (ML) and Wilcoxon Rank-Sum Test and it is concluded that dominant strategy for HAYW stock is Buy
Q: Is HAYW stock a buy or sell?
A: The dominant strategy among neural network is to Buy HAYW Stock.
Q: Is Hayward Holdings Inc. Common Stock stock a good investment?
A: The consensus rating for Hayward Holdings Inc. Common Stock is Buy and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of HAYW stock?
A: The consensus rating for HAYW is Buy.
Q: What is the forecast for HAYW stock?
A: HAYW target price forecast: Buy