Modelling A.I. in Economics

YQ Stock: Is It a Bubble?

Outlook: 17 Education & Technology Group Inc. American Depositary Shares is assigned short-term Ba3 & long-term B2 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n: for Weeks2
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Factor
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

17 Education & Technology Group Inc. American Depositary Shares prediction model is evaluated with Multi-Task Learning (ML) and Factor1,2,3,4 and it is concluded that the YQ stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.5 According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: Sell

Graph 26

Key Points

  1. Multi-Task Learning (ML) for YQ stock price prediction process.
  2. Factor
  3. How do you pick a stock?
  4. What are buy sell or hold recommendations?
  5. Can neural networks predict stock market?

YQ Stock Price Forecast

We consider 17 Education & Technology Group Inc. American Depositary Shares Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of YQ 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: YQ 17 Education & Technology Group Inc. American Depositary Shares
Time series to forecast: 4 Weeks

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


F(Factor)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Multi-Task Learning (ML)) X S(n):→ 4 Weeks r s rs

n:Time series to forecast

p:Price signals of YQ stock

j:Nash equilibria (Neural Network)

k:Dominated move of YQ stock holders

a:Best response for YQ target price


Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.5 In statistics, a factor is a variable that can influence the value of another variable. Factors can be categorical or continuous. Categorical factors have a limited number of possible values, such as gender (male or female) or blood type (A, B, AB, or O). Continuous factors can have an infinite number of possible values, such as height or weight. Factors can be used to explain the variation in a dependent variable. For example, a study might find that there is a relationship between gender and height. In this case, gender would be the independent variable, height would be the dependent variable, and the factor would be gender.6,7

 

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

How do PredictiveAI algorithms actually work?

YQ 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 Multi-Task Learning (ML) based YQ Stock Prediction Model

  1. When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
  2. An entity need not undertake an exhaustive search for information but shall consider all reasonable and supportable information that is available without undue cost or effort and that is relevant to the estimate of expected credit losses, including the effect of expected prepayments. The information used shall include factors that are specific to the borrower, general economic conditions and an assessment of both the current as well as the forecast direction of conditions at the reporting date. An entity may use various sources of data, that may be both internal (entity-specific) and external. Possible data sources include internal historical credit loss experience, internal ratings, credit loss experience of other entities and external ratings, reports and statistics. Entities that have no, or insufficient, sources of entityspecific data may use peer group experience for the comparable financial instrument (or groups of financial instruments).
  3. IFRS 16, issued in January 2016, amended paragraphs 2.1, 5.5.15, B4.3.8, B5.5.34 and B5.5.46. An entity shall apply those amendments when it applies IFRS 16.
  4. There is a rebuttable presumption that unless inflation risk is contractually specified, it is not separately identifiable and reliably measurable and hence cannot be designated as a risk component of a financial instrument. However, in limited cases, it is possible to identify a risk component for inflation risk that is separately identifiable and reliably measurable because of the particular circumstances of the inflation environment and the relevant debt market

*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.

YQ 17 Education & Technology Group Inc. American Depositary Shares Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3B2
Income StatementBaa2C
Balance SheetBaa2Baa2
Leverage RatiosBaa2C
Cash FlowCaa2B2
Rates of Return and ProfitabilityCB2

*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. Barrett, C. B. (1997), "Heteroscedastic price forecasting for food security management in developing countries," Oxford Development Studies, 25, 225–236.
  2. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
  3. G. Konidaris, S. Osentoski, and P. Thomas. Value function approximation in reinforcement learning using the Fourier basis. In AAAI, 2011
  4. Chen, C. L. Liu (1993), "Joint estimation of model parameters and outlier effects in time series," Journal of the American Statistical Association, 88, 284–297.
  5. Abadie A, Imbens GW. 2011. Bias-corrected matching estimators for average treatment effects. J. Bus. Econ. Stat. 29:1–11
  6. Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
  7. R. Howard and J. Matheson. Risk sensitive Markov decision processes. Management Science, 18(7):356– 369, 1972
Frequently Asked QuestionsQ: Is YQ stock expected to rise?
A: YQ stock prediction model is evaluated with Multi-Task Learning (ML) and Factor and it is concluded that dominant strategy for YQ stock is Sell
Q: Is YQ stock a buy or sell?
A: The dominant strategy among neural network is to Sell YQ Stock.
Q: Is 17 Education & Technology Group Inc. American Depositary Shares stock a good investment?
A: The consensus rating for 17 Education & Technology Group Inc. American Depositary Shares is Sell and is assigned short-term Ba3 & long-term B2 estimated rating.
Q: What is the consensus rating of YQ stock?
A: The consensus rating for YQ is Sell.
Q: What is the forecast for YQ stock?
A: YQ target price forecast: Sell

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