WDH Stock Forecast: A Buy For The Next 6 Month

Outlook: Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 18 Jun 2023 for 6 Month
Methodology : Active Learning (ML)

Abstract

Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) prediction model is evaluated with Active Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the WDH stock is predictable in the short/long term. Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

Key Points

1. Operational Risk
2. Trust metric by Neural Network
3. Probability Distribution

WDH Target Price Prediction Modeling Methodology

We consider Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) Decision Process with Active Learning (ML) where A is the set of discrete actions of WDH 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(Statistical Hypothesis Testing)^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(Active Learning (ML)) X S(n):→ 6 Month $\begin{array}{l}\int e^x\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of WDH stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

Active Learning (ML)

Active learning (AL) is a machine learning (ML) method in which the model actively queries the user for labels on data points. This allows the model to learn more efficiently, as it is only learning about the data points that are most informative.

Statistical Hypothesis Testing

Statistical hypothesis testing is a process used to determine whether there is enough evidence to support a claim about a population based on a sample. The process involves making two hypotheses, a null hypothesis and an alternative hypothesis, and then collecting data and using statistical tests to determine which hypothesis is more likely to be true. The null hypothesis is the statement that there is no difference between the population and the sample. The alternative hypothesis is the statement that there is a difference between the population and the sample. The statistical test is used to calculate a p-value, which is the probability of obtaining the observed data or more extreme data if the null hypothesis is true. A p-value of less than 0.05 is typically considered to be statistically significant, which means that there is less than a 5% chance of obtaining the observed data or more extreme data if 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?

WDH Stock Forecast (Buy or Sell) for 6 Month

Sample Set: Neural Network
Stock/Index: WDH Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares)
Time series to forecast n: 18 Jun 2023 for 6 Month

According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

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 Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares)

1. Time value of money is the element of interest that provides consideration for only the passage of time. That is, the time value of money element does not provide consideration for other risks or costs associated with holding the financial asset. In order to assess whether the element provides consideration for only the passage of time, an entity applies judgement and considers relevant factors such as the currency in which the financial asset is denominated and the period for which the interest rate is set.
2. At the date of initial application, an entity shall determine whether the treatment in paragraph 5.7.7 would create or enlarge an accounting mismatch in profit or loss on the basis of the facts and circumstances that exist at the date of initial application. This Standard shall be applied retrospectively on the basis of that determination.
3. For the purpose of this Standard, reasonable and supportable information is that which is reasonably available at the reporting date without undue cost or effort, including information about past events, current conditions and forecasts of future economic conditions. Information that is available for financial reporting purposes is considered to be available without undue cost or effort.
4. For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.

*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

Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) is assigned short-term Ba1 & long-term Ba1 estimated rating. Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) prediction model is evaluated with Active Learning (ML) and Statistical Hypothesis Testing^1,2,3,4 and it is concluded that the WDH stock is predictable in the short/long term. According to price forecasts for 6 Month period, the dominant strategy among neural network is: Buy

WDH Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) Financial Analysis*

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

Prediction Confidence Score

Trust metric by Neural Network: 84 out of 100 with 768 signals.

References

1. Farrell MH, Liang T, Misra S. 2018. Deep neural networks for estimation and inference: application to causal effects and other semiparametric estimands. arXiv:1809.09953 [econ.EM]
2. Scholkopf B, Smola AJ. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press
3. Matzkin RL. 1994. Restrictions of economic theory in nonparametric methods. In Handbook of Econometrics, Vol. 4, ed. R Engle, D McFadden, pp. 2523–58. Amsterdam: Elsevier
4. R. Rockafellar and S. Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21–42, 2000.
5. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Boca Raton, FL: CRC Press
6. Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
7. Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.

Frequently Asked Questions

Q: What is the prediction methodology for WDH stock?
A: WDH stock prediction methodology: We evaluate the prediction models Active Learning (ML) and Statistical Hypothesis Testing
Q: Is WDH stock a buy or sell?
A: The dominant strategy among neural network is to Buy WDH Stock.
Q: Is Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) stock a good investment?
A: The consensus rating for Waterdrop Inc. American Depositary Shares (each representing the right to receive 10 Class A Ordinary Shares) is Buy and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of WDH stock?
A: The consensus rating for WDH is Buy.
Q: What is the prediction period for WDH stock?
A: The prediction period for WDH is 6 Month

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